Think you know how long you'll wait? Research says think again.
New research from the University of Florida Warrington College of Business suggests that consumers routinely misjudge how long everyday services will take, not because they are bad at estimating averages but because they underestimate the variability around those averages. The finding, which the researchers frame as a behavioral bias, applies to settings ranging from coffee shops and bank branches to customer service call centres where people wait for a response. According to the study, customers tend to anchor on a typical or best, case experience and treat it as the norm, leaving them psychologically unprepared when a particular visit runs far longer than expected. The work adds to a growing body of behavioural science literature examining how people form expectations about time and how those expectations shape satisfaction.
The question of how consumers perceive waiting has long interested marketers, psychologists and operations researchers, because the experience of waiting often matters more to satisfaction than the actual number of minutes clocked. Previous work in this area has established that unoccupied time feels longer than occupied time, that uncertain waits feel longer than known, finite waits, and that unexplained delays are more frustrating than those with a clear reason attached. The new University of Florida research extends this line of inquiry by focusing specifically on how well people understand the spread of possible service times, rather than just their central estimate. In doing so, it highlights a distinction that most consumers rarely make consciously: knowing that a service usually takes about ten minutes is not the same as understanding how often it takes thirty.
The core insight is that individuals tend to focus on a single plausible duration, often drawn from their most recent or most memorable experience, and then build their expectations around it. Because of this, they systematically underweight the probability of unusually long waits, even in services where such delays are common and well documented. This matters for businesses because customer frustration is often driven less by the average wait than by the gap between what a customer anticipated and what actually occurred. A person who expects ten minutes and waits twenty, five may feel more aggrieved than someone who expects thirty minutes and waits the same amount of time. The research therefore points to expectation, setting, not just speed, as a lever for improving customer experience.
The implications for service businesses are considerable, particularly for industries where demand fluctuates sharply or where backlogs build unpredictably. Companies that communicate only a single estimated wait time may inadvertently be setting customers up for disappointment, whereas those that offer ranges or explain why delays happen may soften the blow when things go wrong. Consumer advocates have long argued that transparent communication about delays is fairer than vague reassurance, and this research lends empirical weight to that position. For customers themselves, the practical takeaway is a nudge toward building in a buffer and recognising that a fast past experience is not a guarantee. The findings also carry relevance for public services, healthcare queues and transport, where unpredictability is endemic.
This study arrives amid a broader reckoning with waiting in modern life, as automation, self, service kiosks and digital queues reshape how people experience delays. Businesses increasingly compete on convenience, and tools that provide live updates, position, in, queue notifications and dynamic scheduling have proliferated. Yet even the most sophisticated queue, management technology cannot eliminate variability, and in some cases it may create new expectations that are hard to meet. The research fits into a wider conversation about customer patience in an era of instant delivery and on, demand apps, where tolerance for delays appears to be shrinking even as the complexity of service systems grows. Understanding how people mentally model variability, rather than merely averages, could therefore inform everything from app design to staffing decisions.
There is a historical parallel in how industries have learned to manage expectations about uncertainty. Airlines, for instance, eventually moved toward giving passengers more realistic information about delays and connections after years of complaints about vague announcements, and hospitals have experimented with expected wait, time displays in emergency departments. Utilities and telecom providers have similarly faced pressure to be candid about repair timelines rather than promising quick fixes. Each of these shifts reflects a hard, won lesson: that managing perception is often as important as managing the underlying operation. The new research suggests that many service sectors may still be underestimating how poorly their customers grasp the true range of possible waiting times.
Looking ahead, the findings are likely to feed into further academic work testing whether interventions such as providing wait, time ranges, explaining variability or offering proactive updates can measurably improve customer satisfaction. Researchers may also examine whether the bias varies across cultures, age groups and types of service, and whether repeated exposure to unpredictable waits eventually corrects it. For businesses, the practical question is whether to invest in managing expectations alongside managing queues, since the two are not the same thing. For consumers, the lesson is simpler and slightly humbling: the next time you assume a quick stop at the counter or a brief hold on the phone, the odds of a longer wait may be higher than you think.
