The source of truth: How the origin of advice dictates who we trust — and why it matters

Predicting whether a new stock will rise, a stroller will hold up, or a job offer is the right fit can be daunting, leading people to seek advice to avoid possible missteps.

New research in the journal Organizational Behavior and Human Decision Processes, co-authored by assistant professor Katie S. Mehr at the Alberta School of Business and Matt Meister of the University of San Francisco, offers a powerful framework, arguing that people do not just blindly trust experts. Instead, the study reveals that our reliance on advice depends in part on the way advice is generated, and how this interacts with the expertise of the advisor.

For instance, advice might be generated based on someone’s direct experience: someone who went to a restaurant might tell a friend about their experience and recommend they go to the restaurant. Or, advice might be generated based on someone’s synthesis of data and information: a financial analyst might evaluate a stock’s past performance to determine whether she recommends a client buy it or not. Mehr’s work suggests that these different ways of generating advice matter, and shape whether we rely on advice from experts or novices.

"This work is important because it shows that the exact same advice can be made more or less persuasive, depending on the fit between how the advice was generated and the advisor’s expertise," says Mehr.

The value of knowing the process

The central finding of the study is that reliance on advice is highly context-dependent. The researchers focused on two broad categories of advice-givers: "novices," who have relatively limited domain experience, and "experts," who possess deeper domain experience.

The proposed framework reveals distinct pathways for how advice is received based on how it was formed:

• Direct lived experience: When advice is generated from a direct, personal encounter, people are more likely to follow the advice of novices over experts. For example, if someone says, "Based on my experience working at that company, you should take the job," this advice is more persuasive when shared by a novice (vs. expert). People believe novices share similar goals and experience the domain in a fundamentally similar way, so their advice is more persuasive here.
  
  
• Data synthesis and extrapolation: However, when that exact same advice is framed as being from data synthesis—such as, "Based on the data I reviewed about the company, you should take the job"—people overwhelmingly prefer to follow the advice of experts. Experts are perceived to have a unique, objective ability to synthesize complex information.
  
  
• The middle ground: The researchers also found that advice formed from a combination of experience and data synthesis, such as observing an experience rather than living it directly, falls somewhere in the middle. Because advice from observed or similar experiences is still advice based on experience, but notably does still require some synthesis of complex information, the clear preference for either an expert or a novice becomes less pronounced.
  
  By understanding these pathways, advisors, organizations, and advice or ratings platforms can better present their advice. For instance, experts could highlight the large amount of data they synthesized to arrive at their advice, while novices could describe the direct experience they had that coloured their opinions.
  

  "With so much advice available online, it can be useful to provide advice-givers strategies to make their advice stand out,” says Mehr. "Fitting the way advice was generated with one’s expertise is a strategy advice-givers can use.”
  
  — Katie Mehr

The power of similarity

While the benefits of expert data synthesis are clear, the researchers noted a critical nuance regarding novices: trust hinges on perceived experience similarity. In one of the studies, participants were asked to consider booking an airline ticket in economy class.

The study found that people relied heavily on the novice's advice only when the novice's experience matched their own anticipated experience—for example, if the advice-giver also was flying economy. When the novice flew business class, people perceived them as less similar, significantly altering how persuasive the advice was. This finding highlights the key reason people follow novices: because they usually expect to have similar experiences to a fellow novice. But when people know the novice did not have a similar experience, they rely less on the novice, even when advice is formed from the novice’s direct experience.

A shift in how we share information

The research provides a compelling blueprint for how platforms and organizations can structure reviews, testimonials, and expert analysis.

The findings suggest that companies shouldn't just elevate expert voices; they need to strategically match advisor expertise to their description of how that advice was formed. An expert who wants to be persuasive should explicitly highlight the extensive data they reviewed, while companies looking to showcase what a product actually feels like should elevate the voices of novices who have used the product themselves.

Key takeaways

• The "fit" dictates persuasion: The persuasiveness of advice doesn't just rely on who is giving it, but on the alignment between the advisor’s level of expertise and the specific way they generated their recommendation.
  
  
• Lived experience favours the novice: When advice is based on a direct, personal encounter (such as working at a company or trying out a product), decision-makers are more likely to follow the advice of a novice over an expert.
  
  
• Data synthesis demands an expert: When the exact same advice is framed as the result of analyzing data, reviewing information, or forecasting, people overwhelmingly prefer to follow the guidance of an expert.
  
  
• Relatability is crucial: For a novice's advice to be effective, their lived experience should seem to be similar to the anticipated experience of the person asking for advice.

Read the full article in Organizational Behavior and Human Decision Processes at DOI:10.1016/j.obhdp.2026.104476.

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