Technology Myths and Urban Legends

In user research, we often hear people describe how they think technology works. Sometimes, those theories are accurate, but most often, they are not. Through the many user interviews, field studies, and user testing sessions that we’ve conducted for our Life Online study, we’ve heard many technology-related myths. We also observed how these myths impact user behavior and spread from user to user, effectively becoming urban legends.

Myths Are Formed to Avoid the Perceived Risks Associated with Technology

A technology myth is an (often inaccurate) user-generated theory about how a system functions based on personal perceptions or second-hand experiences rather than a factual understanding of the system’s functionality.

It’s human nature to generate explanations for phenomena around us. We form theories about how the world works and why things happen. An explanation, whether true or not, makes us feel in control.

Instances of this tendency abound in every culture’s mythology. For example, oceanic storms and earthquakes were destructive and unpredictable to ancient Greek sailors. But if they had an explanation (Poseidon’s anger), then they felt they could exert control over the outcome (through prayers and sacrifices).

Technology myths seem to arise in the same way. Users don’t have a clear understanding of how a system works, so they generate possible explanations that seem logical to them based on existing knowledge and experience. 

Unfounded explanations about technology have been created since the early days of personal internet use. In 2000, Andy Cockburn and Steve Jones found that only 1 out of 11participants had a correct understanding of how an internet browser’s Back button works. The remaining ten participants, who misunderstood the functionality of the Back button, experienced navigation issues as they browsed the web.

There are a variety of conditions that can lead to myths about technology. Two common situations that fuel such myths include:

• Uncertainty about how technology functions
• Distrust of digital products or the company behind them

In these situations, any additional concerns users might have, such as concerns about privacy, security, or saving money, often fuel speculation about what happens when interacting with digital technology.

Uncertainty About How Technology Functions

A lack of understanding of how technology works allows users to create unfounded explanations.

For example, one woman in our lab-based usability study in Kansas City was very concerned about information security.   

Whenever she submitted contact information through an online form, she clicked the Back button and manually removed her data from the form fields — even though that content had already been submitted.

“It makes me feel like I’m deleting it. It’s probably still on there [the computer] or whatever, but it makes me feel like it’s secure,” she said.

Because this participant was security conscious and because she didn’t understand how the information submitted in web forms is instantly sent to the website’s server, she adopted the myth that she could protect her information this way.

Distrust of Digital Products and the Company Behind Them

Some technology myths can be at least partially attributed to a sense of distrust toward digital products. Many users have a general fear that designers and companies seek to manipulate them and are on high alert for any evidence of such manipulation.

During our usability testing in China, we heard two different theories about how pricing worked on Taobao, the hugely popular shopping platform.

One participant had heard from a friend that, for the same product, Taobao charged iPhone users more than it charged Android users. He assumed that, since iPhones were more expensive than Android devices, Taobao took advantage of these “richer” customers. Consequently, he refused to shop on any ecommerce sites on his iPhone. (We informally tested his theory and did not find any difference in Taobao prices between iPhone and Android.)

A different participant told us that products are cheaper on Taobao’s mobile channels than on its desktop website. (She proved that her theory was actually true by searching for the same products on both desktop and mobile and finding lower prices on mobile.)

During our usability test in Raleigh, North Carolina, one participant was planning an upcoming vacation to Arizona. While researching flights, she told the facilitator that airline websites track how many times a user performs the same search (destination and dates), and, for repeat searches, the airlines raise prices for that user because “they know you’re interested.”

Clearly, the participant was suspicious that companies were tracking her behavior and profiting from it. Therefore, she said she researched flights on one device but purchased on another to prevent the website from recognizing her. She also shopped only on aggregator sites like Kayak or Orbitz and didn’t buy flights from airlines directly.

This is one instance where a technology myth has become so widely believed it could be described as an urban legend. In fact, this theory is so popular that Time magazine covered it, and Consumer Reports investigated it. Airlines deny the practice, but the results of the investigation seem inconclusive. One author of this article is a sworn believer in this theory and says she’s seen it happen in her own experience. The other author of this article is pretty sure that airlines do not increase prices based on user interest. Even UX consultants aren’t exempt from these technology myths!

New Technologies (Like AI) Are Often the Subject of Tech Myths

Not surprisingly, the emergence of new technologies often incites tech myths.

Myths about the internet were rampant at its outset. When cell phones were becoming mainstream, there was a common misconception that they could cause cancer in humans. While it is true that cell phones emit a type of radiation similar to that produced by microwave ovens, the level of radiation is not enough to be considered harmful to human health. An article on Cancer.gov indicates that, based on numerous studies on the topic, there is no conclusive evidence linking cell phone use to an increased risk of cancer.

Artificial intelligence (AI) is one such emerging technology that has triggered myths based on a lack of understanding of how it works.

Google has published a white paper addressing 6 common myths about AI. One of the myths discussed is that all AI systems are “black boxes,” far less explainable than nonAI techniques. The paper explores this myth, stating, “As with human-based processes or traditional software, some AI systems are quite simple and easy to explain, while others are far more complex.”

We conducted a study of people interacting with AI systems such as Facebook, Instagram, and Netflix that used machine-learning algorithms for recommendations and personalization. We found that a lack of clarity in the type of information these algorithms used as inputs made it difficult for users to create a mental model of how the algorithms determined what content would be displayed in their newsfeeds.

They assumed that the posts in their newsfeed that they engaged with (through the Like button and its relatives) are taken into account by the algorithm. Overall, there was little understanding as to what influenced recommended content. This lack of understanding led participants to theorize as to the possible inputs. Some of these theories were farfetched and reflected a lack of transparency in the algorithm. For example, one user noted:

“This is interesting and creepy — yesterday I was talking about craving pho, which I normally don’t eat, and now I see this [ad for a] pho burrito; I wonder if they just record your conversations.”

The lack of transparency in the input made users suspicious. They assumed that almost every one of their actions (whether online or in the real world) was taken into account by the algorithm, and they ended up believing that the systems were more “creepy” and intrusive than they were in reality.

The explainability of these technologies is a challenge that tech developers and designers must contend with in order to build understanding with its user base. Otherwise, myths will arise, which will often impact user behavior.

How Technology Myths Impact User Behavior

Users’ beliefs (accurate or inaccurate) about a system form their mental model: their unique, internal understanding of how that system works. That mental model influences the actions the user will take in the system.

When the user’s mental model is substantially different from the reality of the system, UX problems arise. Thus, these technology myths and urban legends, while interesting, can be harmful.

Consider how the study participants in the examples above adjusted their behavior based on their unfounded beliefs.

• Removing the text entered in every form submitted online is a waste of time and a reason to avoid filling out forms on the web.
• Avoiding one channel for fear of increased prices means more planning and less convenience.
• Switching devices for the same flight search means extra work entering the same information twice.

In all these situations, the end result is a high interaction cost: people need to plan and exert effort to overcome the (real or imaginary) hurdles these myths raise.

Types of Technology Myths

Most technology myths fall into one of three categories.

Type of Myth	Description	Example
System-specific	A myth developed specifically about your product or a feature in your UI	Taobao increases price for iPhone users.
Industry-specific	A myth developed about your industry or larger product type	Airlines increase prices if they notice you search repeatedly for a flight.
Universal	A myth that applies across products and industries.	Form fields should be cleared to protect privacy. Artificial-intelligence outputs are inexplicable.

Understanding what types of myths exist can prepare you for uncovering these beliefs during user research.

Watch for Technology Myths During Qualitative Exploratory Research

Technology myths can be uncovered through qualitative research. Qualitative research aims to gain a deeper understanding of real-world problems through observation and open-ended feedback.

During Usability Testing

Behavioral research methods such as moderated usability testing are often used to evaluate specific systems. When conducting system-specific behavioral research, watch for system-specific technology myths. And if your system utilizes emerging technologies such as AI, you should also be on the lookout for universal myths.

During qualitative usability testing, users should think aloud as they complete tasks with the system. Listen to what they share about their beliefs and understanding of the system’s functionality. After test activities, you may need to follow up with probing questions related to what they shared while completing the tasks to get a deeper understanding of what they believe and why.

Pay special attention to new, novel, and complex designs or interactions, as users are usually less certain about how these elements may work.

If you’ve discovered a myth held by users in your research, investigate whether the belief is unique to the individual or if it is a widespread urban legend. Look for other clues as well: Does the feature generate a lot of questions for your support team or on social media? If so, it’s a good indication that your users are confused about how it works and will come up with their own explanations.

During Attitudinal Studies and Context Studies

Attitudinal studies such as user interviews and focus groups, as well as context methods like contextual inquiry, diary studies, and field studies give you the opportunity to uncover insights about people’s worldviews, including their attitudes, beliefs, and norms. They are also often focused on broad subject matters (e.g., how people approach the task of buying a home as opposed to how they use a specific interface). As a result, they are great for learning about industry-specific and universal technology myths.

How to Dispel Technology Myths

If you find a technology myth in your research, consider what it says about your product. If the myth makes users less efficient or unlikely to use your system to their full advantage, change how you communicate that feature to users and show explanation messages to clarify how your system works.

In many cases, it’s better to hide the complexity and show only what users need to know to avoid confusion. However, the existence of technology myths or urban legends can be a clue that more explicit communication with your users is needed.

Explainability in relation to AI-based systems and features is an area where more focus and innovation are necessary to produce new methods and tools to give users insight into why an AI system behaves the way it does.

Being aware of people’s misunderstandings about technology and clarifying them through communication is the best approach to counteract any technology myth.

References

University of Canterbury. Which Way Now? Analysing and Easing Inadequacies in WWW Navigation. Retrieved from https://www.csse.canterbury.ac.nz/andrew.cockburn/papers/webIJHCS96.pdf.

Google. Exploring 6 AI myths. Retrieved from https://ai.google/static/documents/exploring-6-myths.pdf.