Enterprise decisions should be entirely business-rational. However, even high-performing organizations can make decisions that are influenced by hidden biases. In the critical context of lone worker safety solutions, this can result in increased business risk.
The two main biases that negatively impact lone worker safety solution decision-making are “social proof” and the “halo effect.” Let’s examine what these biases look like in practice.
Social Proof: References, User Counts, and Surveys
Lone worker safety is a complex topic and few managers have deep expertise. This means choosing a solution is a decision made with significant uncertainty.
Search “lone worker safety solution” on Google, and you’ll see:
- Logos and client references that every provider highlights
- Impressively large number of users of a given lone worker safety solution (per the provider)
- Workplace safety surveys that show lone worker protection ranks among EHS managers’ top priorities
This is the impact of “social proof.” We tend to assume that if enterprise peers have already made this decision, it must be the right one because:
- This solution must be good, because it has so many prestigious users.
- We need to find a lone worker solution quickly, because every other company ranks this topic among their top three priorities.
The assumptions of social proof drive business decision-making, instead of actual proof.
The Conformity Experiment (Asch, 1951)
This classic study invited students to take part in a vision test. In each group, there was only one genuine participant: unknown to them, everyone else was an actor planted by the research team.
Participants were asked to choose which of three lines matched the length of a reference line. The answer was obvious, however the actors deliberately gave a wrong answer on seven out of the trials.
75% of participants conformed to the majority’s incorrect answer at least once, even though the error was obvious. This study demonstrated a natural tendency to conform to majority opinion.
Here is Nash’s study.
Social Proof Steers Enterprises Away From the Best Solution
Here’s how decision-making is diverted away from rationality:
- Organizations with 50,000+ employees are shown, however only a tiny fraction of that number are actually equipped with that lone worker solution.
- A simple calculation—each organization multiplied by a few dozen users—shows that the actual user count is far short of the advertised count.
- Organizations ask the wrong question: “What are your top priorities?” instead of “What can we actually implement and complete in the next 12 months?”
Relying on social proof distracts enterprises from the criteria that truly make a difference when a lone worker is in distress. Decision-making gets steered toward solutions that aren’t necessarily the best options in the lone worker safety market.
Halo Effect: The Lone Worker Smartwatch and Artificial Intelligence
Imagine an enterprise is presented with two lone worker safety solutions:
- A connected smartwatch for lone worker safety, and;
- A lone worker safety solution powered by artificial intelligence.
They might immediately think:
- Smartwatch = Apple Watch = Apple = one of the most innovative companies in the world
- Artificial intelligence = ChatGPT = the technology redefining the future
This positive perception of a single standout feature ends up shaping how every other feature is evaluated: “This lone worker safety solution must be perfect for our teams!” Unconsciously, the known positive qualities stand in for the solution as a whole, with no real evidence that this is the case.
This is known as the halo effect, or the “contamination effect.” The beautiful halo of what little is known overwhelms the ability to effectively evaluate what is not known.
The Halo Experiment (Nisbett & Wilson, 1977)
118 students at the University of Michigan were asked to evaluate an instructor with a strong foreign accent. For the first group, the instructor behaved in a manner that was warm and friendly; for the second, he was cold and distant.
On three objective questions (physical appearance, manners, accent) the second group rated the instructor significantly more negatively than the first. 80% of students in the second group described his accent as “irritating”, versus 50% in the first group.
This study demonstrated that a single impression can disproportionately influence judgment across all other qualities.
Here is the study by Nisbett and Wilson.
The Rational Brain Becomes Deceived
Once again, enterprise decision-making takes a turn away from business-rationality:
- When the concept of a lone worker smartwatch is rationally analyzed, it’s clearly better suited to elderly care (personal emergency response systems). Experience from processing more than 25,000 alerts highlights just how poorly it fits lone worker safety needs.
- “Artificial intelligence” begs a simple question: “What is the concrete benefit of this technology for a lone worker alert?” AI, by its very nature, relies on massive amounts of data. It’s hard to talk seriously about AI when it’s bolted onto second-generation dedicated hardware (2008). This is a textbook case of “security blanket syndrome” and the question has never been answered to anyone’s satisfaction.
Conclusion: How to Avoid These Biases
The first step is to recognize that hidden biases can affect decision-making for lone worker safety. Knowing really is half the battle!
The second step—and the most important one—is defining clear decision criteria. These criteria should encompass the entire lone worker safety solution: technology, emergency response, oversight, and adoption. They can be further refined as different solutions are evaluated and compared.
The final step puts “Why?” at the center of every conversation when evaluating providers:
- According to the provider’s website, they equip the competition: “Why did they choose your solution? Do they offer benefits specific to your industry?”
- They have many users: “Why do enterprises keep using your solution? What’s your process for driving real-world adoption?”
- AI is built into their solution: “Why? What value does AI actually provide?”
These three steps can help support a decision-making process for lone worker safety solutions that is business-rational. Use them to choose a solution that’s genuinely effective, simple, and aligned to your business needs.
Gilles Rooss, Founder