Hubbry Logo
User errorUser errorMain
Open search
User error
Community hub
User error
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
User error
User error
User error
View on Wikipedia
from Wikipedia

A user error is an error made by the human user of a complex system, usually a computer system, in interacting with it. Although the term is sometimes used by human–computer interaction practitioners, the more formal term human error is used in the context of human reliability.

Related terms such as PEBKAC ("problem exists between keyboard and chair"), PEBMAC ("problem exists between monitor and chair"), identity error or ID-10T/1D-10T error ("idiot error"), PICNIC ("problem in chair, not in computer"), IBM error ("idiot behind machine error"), skill issue ("lack of skill"), and other similar phrases are also used as slang in technical circles with derogatory meaning.[1] This usage implies a lack of computer savviness, asserting that problems arising when using a device are the fault of the user. Critics of the term argue that many problems are caused instead by poor product designs that fail to anticipate the capabilities and needs of the user.

The term can also be used for non-computer-related mistakes.

Causes

[edit]

Joel Spolsky points out that users usually do not pay full attention to the computer system while using it. He suggests compensating for this when building usable systems, thus allowing a higher percentage of users to complete tasks without errors:

For example, suppose the goal of your program is to allow people to convert digital camera photos into a web photo album. If you sit down a group of average users with your program and ask them all to complete this task, then the more usable your program is, the higher the percentage of users that will be able to successfully create a web photo album. To be scientific about it, imagine 100 real world users. They are not necessarily familiar with computers. They have many diverse talents, but some of them distinctly do not have talents in the computer area. Some of them are being distracted while they try to use your program. The phone is ringing. The baby is crying. And the cat keeps jumping on the desk and batting around the mouse.

Now, even without going through with this experiment, I can state with some confidence that some of the users will simply fail to complete the task, or will take an extraordinary amount of time doing it.[2]

Experts in interaction design such as Alan Cooper[3] believe this concept puts blame in the wrong place, the user, instead of blaming the error-inducing design and its failure to take into account human limitations. Bruce "Tog" Tognazzini describes an anecdote of Dilbert creator Scott Adams losing a significant amount of work of comment moderation at his blog due to a poorly constructed application that conveyed a wrong mental model, even though the user took explicit care to preserve the data.[4]

Jef Raskin advocated designing devices in ways that prevent erroneous actions.[5]

Don Norman suggests changing the common technical attitude towards user error:

Don't think of the user as making errors; think of the actions as approximations of what is desired.[6]

Acronyms and other names

[edit]

Terms like PEBMAC/PEBCAK or an ID10T error are often used by tech support operators and computer experts to describe a user error as a problem that is attributed to the user's ignorance instead of a software or hardware malfunction. These phrases are used as a humorous[7] way to describe user errors. A highly popularized example of this is a user mistaking their CD-ROM tray for a cup holder, or a user looking for the "any key". However, any variety of stupidity or ignorance-induced problems can be described as user errors.

PEBKAC/PEBCAK/PICNIC

[edit]

Phrases used by the tech savvy to mean that a problem is caused entirely by the fault of the user include PEBKAC[8] (an acronym for "problem exists between keyboard and chair"), PEBCAK[9] (an alternative, but similar, acronym for "problem exists between chair and keyboard"), POBCAK (a US government/military acronym for "problem occurs between chair and keyboard"), PICNIC[10] ("problem in chair not in computer") and EBKAC ("Error between keyboard and chair"). Another variant is PEBUAK (Problem Exists Between User and Keyboard).

In 2006, Intel began running a number of PEBCAK web-based advertisements[11] to promote its vPro platform.

If the same sentiment wants to be conveyed without using an acronym, the phrase "chair to keyboard interface error" is often used.

ID-10-T error

[edit]

ID-Ten-T error[12] (also seen as ID10T and ID107) is a masked jab at the user: when ID-Ten-T is spelled out it becomes ID10T ("IDIOT"). It is also known as a "Ten-T error" or "ID:10T error". The User Friendly comic strip presented this usage in a cartoon on 11 February 1999.[13]

In United States Navy and Army slang, the term has a similar meaning, though it is pronounced differently:

  • The Navy pronounces ID10T as "eye dee ten tango".[14]
  • The Army instead uses the word 1D10T which it pronounces as "one delta ten tango".

In other languages

[edit]

In Danish, it is called a Fejl 40, or 'error 40', indicating that the error was 40 centimetres (16 in) from the device. Swedish has a similar expression, Felkod 60, referring to the error being 60 centimeters away from the device.

In Swedish, the phrase skit bakom spakarna ('shit behind the levers') or skit bakom styret ('shit behind the steering wheel') or the abbreviation SBS-problem is used. A variant used in the ICT domain is skit bakom tangenterna/tangentbordet ('shit behind the keys/keyboard') abbreviated SBT.

In French, it is described as an ICC problem (interface chaise–clavier), a problem with the keyboard–chair interface, very similarly to the PEBKAC.

In Québec, it is called a Cas-18, indicating that the error was 18 inches (46 cm) from the device. Better known as 'code-18'.

In Brazilian Portuguese, it is often called a BIOS problem (bicho ignorante operando o sistema),[15] translated as 'ignorant animal operating the system', or a USB error (usuário super burro), translated as 'super dumb user'.

In Spanish, some call it 'Error 200' (error doscientos), because it rhymes with the explanation. When asked for the full explanation, it's often offered as "sí, error 200, entre la mesa y el asiento" ('yeah, error 200, between the desk and the seat'). Other multiples of 100 also work because of the same rhyme. Also called Error de capa 8 (8th layer error) referring to the OSI Protocol layers[16] when the user is the one who caused the error, for example El servidor no es accesible por un error de capa 8 (Server is not accessible due to an 8th layer error) when users can not access a server because they typed in the wrong password.

In German, it is called a DAU (dümmster anzunehmender User), literally translated as 'dumbest assumed user', referring to the common engineering acronym GAU (größter anzunehmender Unfall), for a maximum credible accident, or worst-case scenario.

In Bulgarian, it is called a "Problem with behind-keyboard device" (Проблем със задклавиатурното устройство).

In subcultures

[edit]

The computing jargon refers to "wetware bugs" as the user is considered part of the system, in a hardware/software/wetware layering.

The automotive repair persons' version is referring to the cause of a problem as a "faulty steering actuator", "broken linkage between the seat and the steering wheel", "loose nut between the steering wheel and the seat," or more simply, "loose nut behind the wheel." Similarly, typewriter repair people used to refer to "a loose nut behind the keyboard" or a "defective keyboard controller."

The broadcast engineering or amateur radio version is referred to as a "short between the headphones". Another term used in public safety two-way radio (i.e. police, fire, ambulance, etc.) is a "defective PTT button actuator".

Another similar term used in the United States military is "operator headspace and timing issue" or "OHT," borrowing terminology related to the operation of the M2 Browning machine gun.[17]

"(It's a) carbon based error", indicates a user problem (humans are a carbon-based life-form), as opposed to a silicon-based one.[18]

Some support technicians refer to it as "biological interface error".

The networking administrators' version is referring to the cause of a problem as a "layer 8 issue", referring to the "user" or "political" layer on top of the seven-layer OSI model of computer networking.

In video game culture, user error is sometimes referred to as a "skill issue", often as a retort to the player complaining about the game's perceived unfairness.[19]

See also

[edit]
Look up PEBCAK in Wiktionary, the free dictionary.

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

User error

View on Grokipedia
from Grokipedia
User error refers to mistakes made by individuals interacting with computer systems, software, or other technological interfaces, resulting in unintended outcomes or failure to achieve desired goals. These errors often stem from slips in execution, such as pressing the wrong key due to , or mistakes in , where the user's of the system is inaccurate, leading to flawed intentions. In human-computer interaction (HCI), user errors are distinguished from system faults and are frequently attributed to inadequate interface design rather than inherent user incompetence. The concept of user error has been central to HCI since the field's emergence in the 1980s, emphasizing that human operators represent a of failures in complex systems. Common examples include misconfiguring settings in applications, entering incorrect data in forms, or overlooking security protocols, which can lead to productivity losses, , or vulnerabilities in . Lapses, another category of error involving memory or attention failures, further highlight how cognitive limitations interact with technological demands. To mitigate user errors, designers employ principles like providing clear feedback, using consistent conventions, and conducting to align interfaces with users' expectations and behaviors. This user-centered approach shifts focus from blaming individuals to improving system reliability, recognizing that most errors are predictable and preventable through better engineering. In practice, analyzing user errors offers valuable insights for refining products, as seen in fields from to cybersecurity.

Definition and Overview

Definition

User error refers to an error in the operation or use of a , device, or software that is attributable to the actions or decisions of the user, rather than to inherent defects in the hardware, software, or of the technology itself. This concept is prevalent in fields such as , , and human-machine interaction, where it describes deviations from expected behavior stemming directly from user input or choices. Key characteristics of user error include both intentional and unintentional actions by the user that result in unintended or undesired outcomes, such as incorrect , misinterpretation of instructions, or improper sequencing of operations. These errors highlight the role of human agency in system interactions, often occurring in complex environments where users must navigate interfaces or procedures without full prior familiarity. Unlike systemic issues, user errors are transient and context-specific, tied to individual rather than reproducible flaws in the . User error is distinctly contrasted with hardware failures, which involve physical defects or malfunctions in system components, and software bugs, which are programming errors embedded in the code that cause consistent deviations from intended functionality. This emphasis on user agency differentiates it from technology-inherent problems, focusing instead on the human element in error causation. Early documented uses of the concept appear in , often under terms like "operator error," as seen in technical reports on operations, where "operator-error rerun" described job resubmissions due to user mistakes in or . Further references in late studies quantified operator errors as contributing to 50-70% of failures in electronic systems, underscoring their prevalence in early mainframe environments. Informally, user errors have inspired humorous acronyms like PEBKAC (Problem Exists Between Keyboard and ) among IT professionals.

Historical Development

The concept of user error emerged in the mid-20th century alongside the rise of mainframe , where human operators were often held responsible for system failures in punch-card-based . During the 1950s, technologies like the and relied heavily on punched cards for input. This era marked the initial recognition of user error as a distinct category in , rooted in the limitations of early human-machine interfaces that demanded precise manual intervention without intuitive feedback mechanisms. The 1970s brought a pivotal shift through advancements in human-computer interaction (HCI), exemplified by PARC's development of the computer in 1973, which introduced graphical user interfaces (GUIs) and the to make systems more accessible and less prone to operator mistakes. These innovations stemmed from studies emphasizing , moving beyond blame toward designing interfaces that aligned with human information processing capabilities, as influenced by early models from psychologists like Broadbent (1958). By the 1980s, the popularization of personal computing further highlighted user error in everyday contexts, with IT support communities adopting slang terms like "PEBKAC" (problem exists between keyboard and chair) to describe perceived user-induced issues, reflecting a growing but still user-centric view in technical discourse. The field's roots in and human factors engineering, formalized post-World War II, provided a critical lens, with seminal works like Fitts and Jones (1947) analyzing design-induced errors in complex systems such as aircraft cockpits, principles later applied to . A landmark critique came in 1988 with Donald A. Norman's , which argued that apparent user errors often result from poor design lacking affordances and feedback, famously stating, "The fault... lies not in ourselves, but in [the] product design that ignores the needs of users." In the post-2000 era, the evolution toward mobile and AI-driven interfaces has significantly reduced attributions of user error by incorporating predictive, adaptive designs that anticipate and mitigate slip-ups, such as autocorrect in touchscreens and voice assistants that parse inputs. Despite these advances, user error remains a persistent concept in , as multimodal AI interfaces continue to reveal gaps between expectations and behaviors, though with far less frequency than in earlier decades.

Causes

Technical Factors

Technical factors contributing to user error primarily stem from deficiencies in system design and implementation that hinder effective human-technology interaction. Poor (UI) layout, such as cluttered or non-intuitive arrangements, can lead to misinputs by overwhelming users or obscuring key actions. Ambiguous icons or symbols further exacerbate this by failing to convey intended functions clearly, prompting incorrect selections. Additionally, inadequate feedback mechanisms—such as delayed or absent confirmations of user actions—leave individuals uncertain about whether inputs were registered, increasing the likelihood of repeated or erroneous attempts. A review of studies found that poor user interfaces and fragmented displays were associated with errors in 76% of cases, highlighting the pervasive role of design flaws in error induction. Hardware limitations also play a significant role in precipitating user errors through ergonomic mismatches and compatibility issues. Small keyboards on mobile devices, for instance, restrict finger placement and increase typing inaccuracies due to limited key size and spacing, with studies showing higher error rates on touchscreen keyboards under 4 cm in width compared to larger physical ones. Incompatible peripherals, such as mismatched input devices or adapters, can cause unintended activations or failures in recognition, leading to accidental actions like erroneous data entry. Ergonomic problems, including awkwardly positioned or non-adjustable hardware, contribute to physical strain that indirectly amplifies input errors over prolonged use. Environmental influences within workspaces compound these technical vulnerabilities by altering interaction reliability. Distractions in shared or open-plan environments, such as ambient from colleagues, interrupt task focus and double error rates even in brief 3-second interruptions. Low-visibility conditions, like screen glare from overhead lighting or poor ambient illumination, reduce UI readability and prompt misreads or overlooked elements, thereby elevating operational mistakes. The National Institute of Standards and Technology (NIST) emphasizes that such environmental-technical interactions often underlie critical use s in software interfaces, particularly where visibility and distraction gaps impair safe operation.

Human Factors

Human factors contributing to user error arise from the interplay of cognitive processes, behavioral patterns, and physiological conditions, as studied in and . These elements explain why individuals deviate from intended actions during system interactions, often independently of external design flaws. Research in human-computer interaction (HCI) highlights how internal user states can amplify the likelihood of mistakes, emphasizing the need to understand human limitations to contextualize error occurrence. Cognitive biases significantly influence user behavior, leading to systematic deviations in judgment and . For instance, prompts users to selectively attend to information aligning with their preconceptions. Similarly, induces lapses in attention and reduced , impairing sustained focus and increasing the propensity for attentional errors during prolonged interactions. These biases and states distort information processing, resulting in unintended actions that persist even in familiar environments. Skill and experience gaps further exacerbate user errors, particularly among novices who lack the contextual to interpret system commands accurately. Without adequate familiarity, beginners often misapply instructions, leading to operational failures that stem from incomplete mental models of the interface. This gap highlights the role of prior exposure in building effective interaction strategies, where inexperience creates barriers to intuitive use. Physiological factors, such as age-related declines, also play a critical role in error proneness by affecting sensory and motor capabilities. Declines in and contrast sensitivity can hinder precise input, while reduced dexterity impairs fine , both contributing to inaccuracies in target selection and manipulation. These changes underscore how biological aging alters interaction reliability, particularly in tasks demanding high precision. Theoretical frameworks from provide quantitative insights into these human factors. Fitts' Law, a foundational model, posits that the time required for aimed movements is a function of target distance and size, where larger distances or smaller targets prolong execution and elevate error probability in interface operations. This law illustrates how human motor limitations interact with design constraints to predict error rates, informing the analysis of physiological and skill-related influences on performance.

Types and Examples

Input and Operation Errors

Input and operation errors occur when users directly interact with devices or software, leading to unintended actions due to imprecise physical or cognitive inputs during routine tasks. These errors are prevalent in everyday , where motor skills and attention intersect with digital interfaces, often resulting in minor disruptions that accumulate over time. For instance, typing and mistakes, such as typos or incorrect keystrokes, arise from the inherent limitations of manual input, with average error rates in manual data entry hovering around 1% across various contexts. A common manifestation of these input errors is the "fat-finger" phenomenon on touchscreens, where users inadvertently tap adjacent keys or buttons due to finger size relative to small interface elements, frequently leading to issues like entering incorrect or selecting wrong options. This type of error is exacerbated in mobile environments, where screen constraints amplify the of mis-touches, contributing to frequent password reset requests that account for 20-50% of all IT tickets. Such incidents highlight how physical interaction flaws can cascade into operational hurdles, often requiring user intervention or support to resolve. Navigation errors represent another key category, involving accidental selections of incorrect , menu items, or icons within applications, which can derail workflows or trigger unwanted processes. These mishaps stem from cluttered interfaces or hasty interactions, diverting users from intended paths and sometimes necessitating or recovery steps. In real-world scenarios, unintentional in file explorers exemplifies this, with 56% of workers admitting to accidentally deleting cloud-based at some point, underscoring the prevalence of such operational slips in tasks. Similarly, misdialing in VoIP systems—often due to erroneous number entry or interface misnavigation—can lead to failed communications, illustrating how input errors extend beyond to broader interaction dynamics. Overall, these errors, while typically recoverable, emphasize the need for intuitive designs to mitigate their frequency in user-system engagements.

Configuration and Setup Errors

Configuration and setup errors occur when users incorrectly configure systems, software, or devices during initial installation or maintenance, leading to operational failures. These errors often arise from overlooking compatibility requirements, such as mismatched software versions or unaddressed dependencies, which can cause immediate crashes or long-term instability. For instance, in distributed systems like Apache Hadoop, upgrades fail when new versions introduce incompatible data formats, such as required fields in serialization protocols that old nodes cannot parse, resulting in crashes during rolling upgrades. Similarly, multiple versions of dynamically linked libraries (DLLs) in Windows environments contribute to application crashes by passing invalid arguments or conflicting with peripherals, with ntdll.dll alone implicated in 86 crashes across analyzed applications. Parameter misconfigurations represent a significant subset of setup errors, particularly in networking and contexts, where incorrect settings disrupt connectivity or expose vulnerabilities. In networks, conflicts frequently stem from DHCP server misconfigurations, such as overlapping scopes or rogue servers assigning duplicate addresses, which disable affected interfaces and halt communication between devices. For protocols, common issues include default credentials and permissive service permissions in systems like Certificate Services (ADCS), where web enrollment is left enabled, allowing attackers to issue fraudulent certificates and compromise networks. Weak (MFA) setups, such as retaining static password hashes on smart cards, further enable pass-the-hash attacks without requiring credential changes. Device setup issues often involve faulty pairings or incompatible installations that prevent proper integration. In (IoT) ecosystems, (BLE) pairing failures commonly result from outdated or platform-specific differences, causing unstable connections or complete inability to pair devices like sensors with gateways. Driver installations exacerbate this, as incompatible versions—particularly for peripherals like graphics cards or printers—trigger errors during operating system upgrades, such as Windows 11's Memory Integrity feature failing due to unsigned or outdated drivers flagged in . Historical and modern case studies illustrate the scale of configuration errors. The Y2K bug exemplified date format setup flaws in legacy systems, where two-digit year representations (e.g., "00" interpreted as 1900) risked miscalculations in financial and operational software, prompting global remediation efforts estimated at over $50 billion to expand to four-digit formats. In contemporary cloud environments, misconfigurations like exposed Amazon S3 buckets have led to data breaches; for example, a healthcare provider's bucket leaked over 60,000 patient records due to absent password protections, underscoring persistent risks from inadequate access controls. Similarly, Toyota's 2023 breach exposed data on 2.15 million users for a decade because of unchecked cloud settings lacking proper identity and access management (IAM) policies.

Terminology and Acronyms

English-Language Acronyms

In support, professionals often employ humorous acronyms to euphemistically describe instances of user error, where the issue stems from the user's actions rather than technical faults. One prominent example is PEBKAC, standing for "Problem Exists Between Keyboard And ," which originated in tech support environments as a lighthearted way to attribute problems to operator mistakes. A close variant, PEBCAK ("Problem Exists Between And Keyboard"), emerged similarly in the same era, emphasizing the physical distance between the user and the device as the metaphorical source of the error. These terms extend to related acronyms like ("Problem In Not In Computer"), a variant that reinforces the idea of the user as the root cause without directly assigning blame. Another widely recognized term is the ID-10-T error (often written as ID10T), a phonetic play on "" pronounced as "eye-dee-ten-tee," used from the onward in and IT contexts to mask references to user-induced mistakes. In settings, it appears as ID10T in the Navy (pronounced "eye dee ten ") or 1D10T in the ("one delta ten "), serving as coded language during to maintain . This allows support staff to document or discuss errors discreetly, avoiding overt criticism of the individual involved. These acronyms function as internal within helpdesks and technical teams, enabling communication about user errors without escalating tensions or violating protocols. They appear in examples from early online tech forums, including posts where support anecdotes highlighted operator oversights in setups. Over time, such terms have spread culturally through professional literature, notably popularized in Thomas A. Limoncelli, Christina J. Hogan, and Strata R. Chalup's The Practice of System and Network Administration (2001), which documents sysadmin practices and informal lingo to foster better .

Variations in Other Languages and Cultures

In non-English speaking countries, user error terminology often adapts English IT slang while incorporating local linguistic nuances. In German, the term DAU, standing for "dümmster anzunehmender User" (dumbest assumed user), is commonly used in technical contexts to refer to errors stemming from the least competent user imaginable, paralleling assumptions in engineering about worst-case scenarios. Similarly, in French IT environments, ICC denotes "Interface Chaise-Clavier" (chair-keyboard interface), a euphemistic way to attribute issues to the operator without direct confrontation. Subcultural adaptations extend these concepts within global communities. In gaming circles, "noob " describes mistakes by inexperienced players, derived from "noob" as a for novices, emphasizing skill gaps rather than malice. Among open-source developers, ("Read The Fine Manual") signals user negligence in overlooking documentation, a term that underscores expectations of in collaborative coding environments. Since the , globalization through memes and online forums has disseminated these terms across borders, blending English origins with local flavors and accelerating their adoption in multicultural tech spaces.

Impacts

Effects on Individuals

User errors in and digital interactions frequently trigger immediate emotional responses such as and , with indicating that end-users experience frustrating interactions for 30.5% to 45.9% of their total computer usage time. These incidents often arise from unexpected system behaviors or task interruptions, leading to feelings of helplessness or self-directed , as documented in workplace studies where 71.1% of frustration events were rated as highly intense on a 1-9 scale in early ; more recent UX studies suggest frustration affects around 25% of interactions. In severe cases, repeated errors contribute to , particularly in social or settings, and can erode an individual's in their technical abilities, fostering a broader sense of inadequacy. On a practical level, user errors like accidental deletions or incorrect inputs result in , compelling individuals to invest significant time in recovery processes that may not fully restore lost files or . Such mishaps waste a substantial portion of active computer time, with common examples including hours spent application crashes or misplaced features. Financial repercussions include costs for professional services, which typically range from $500 to $2,000 for logical errors on personal hard drives as of 2024, or expenses for device repairs following operational mistakes, such as hardware mishandling. Over time, persistent user errors exacerbate emotional strain, potentially leading to "computer anxiety" or , where individuals develop avoidance behaviors toward technology to evade further distress. This is particularly evident in long-term patterns, such as reduced engagement with digital tools due to accumulated negative experiences, resulting in over-reliance on external support from family or professionals. Demographics play a key role, with higher incidences among elderly users and those with low ; for instance, as of 2023, 41% of adults aged 50 and older report feeling overwhelmed by the pace of technology updates, contributing to elevated stress levels from error-related challenges. Studies highlight that these groups experience amplified emotional and practical burdens, widening digital literacy gaps and perpetuating cycles of disengagement.

Effects on Organizations and Systems

User errors, particularly misconfigurations during routine , frequently result in operational disruptions such as server outages and across organizations. For instance, in October 2021, a configuration change to a backbone router by a employee inadvertently severed the company's internal communication tools, leading to a six-hour global outage that affected billions of users and halted internal operations. Similarly, human errors like accidental deletions or improper configuration updates have been identified as a leading cause of major software outages, with IT technicians sometimes deleting critical databases or applying faulty changes that cascade into widespread service failures. These disruptions impose substantial financial costs on , including elevated helpdesk expenses and lost . Forrester Research estimates the average cost of a single password reset—a common user error—at $70 per incident, though recent estimates suggest $100 or more accounting for . which can accumulate significantly in large enterprises handling thousands of such requests annually. Additionally, tech disruptions stemming from user-induced issues contribute to nearly $4 million in annual lost per organization, as employees face frequent interruptions equivalent to 3.6 tech issues and 2.7 updates per month. errors contribute significantly to global business losses, with cybersecurity incidents alone projected to cost $10.5 trillion annually by 2025. User errors heighten security risks by enabling breaches, especially through phishing interactions that compromise organizational networks. Human error, including interactions with phishing, contributes to a significant portion of data breaches, with social engineering involved in about 22% according to the 2024 Verizon DBIR. In the 2020s, such incidents have fueled ransomware outbreaks; for example, the 2020 Magellan Health ransomware attack exposed over 365,000 patient records after employees likely interacted with phishing payloads, resulting in operational shutdowns and regulatory scrutiny. Another case involved the 2023 MGM Resorts breach, initiated by a social engineering call to the service desk mimicking a user error scenario, which led to widespread system disruptions and an estimated $100 million in losses. Beyond immediate incidents, persistent user errors impose systemic strain on IT resources in large enterprises, amplifying challenges. Frequent support requests for error resolution overload helpdesks, diverting personnel from strategic tasks and contributing to bottlenecks in . In distributed environments, this increased load from misconfigurations and operational mistakes can exacerbate issues, as IT teams struggle to maintain amid rising ticket volumes that grow faster than organizational expansion. For example, data from NetDiligence shows staff mistakes averaging around $75,000 per incident in recovery costs for small and medium businesses, a burden that scales disproportionately in enterprises due to complex systems; more recent estimates are higher. In , user errors in AI tools, such as incorrect prompts, have led to increased productivity losses in enterprises.

Prevention Strategies

User Training and Education

User training and education play a crucial role in mitigating user errors by equipping individuals with the necessary skills and awareness to interact effectively with systems. Common methods include workshops, which provide hands-on guidance for tasks like software navigation, tutorials that offer step-by-step instructions to prevent input mistakes, and simulations that allow practice in safe environments to simulate real-world operations without consequences. These approaches target human factors such as and familiarity, fostering better decision-making during interactions. Awareness programs further support error prevention through targeted campaigns that highlight error-prone situations, such as overlooking confirmation prompts or misconfiguring settings, often integrated into corporate modules to instill best practices from the outset. For instance, sessions emphasize recognizing common pitfalls in system use, promoting a culture of vigilance and proactive error checking. These programs are particularly effective when combined with interactive elements like quizzes or to reinforce learning. Studies in human-computer interaction demonstrate the effectiveness of such training, with error management training (EMT)—which encourages learners to make and learn from errors—showing a positive mean effect on performance (d = 0.44 overall), and larger effects on post-training transfer tasks (d = 0.56) and distinct tasks (d = 0.80), indicating substantial reductions in error rates when applying skills to novel scenarios. These gains are attributed to enhanced metacognitive strategies and control during error encounters. Tailored training approaches customize content for specific user groups to maximize relevance and . For older adults, programs often use simplified tutorials with larger fonts, slower pacing, and verbal guidance to address challenges like reduced or slower processing speeds, leading to improved task completion rates and fewer navigation errors. shows that such customized interventions can increase and reduce self-reported errors in adoption among seniors. These methods ensure that training aligns with diverse cognitive and physical needs, promoting long-term error avoidance.

System Design and Usability Improvements

UI/UX enhancements focus on creating intuitive interfaces that anticipate and mitigate user mistakes through features like confirmation dialogs and auto-corrections. Confirmation dialogs, for example, prompt users to verify potentially destructive actions, such as file deletions, thereby preventing unintended errors before they occur. Intuitive designs reduce by employing natural mappings and visible affordances, making system behaviors predictable and aligning with user expectations to minimize slips. These enhancements draw from established principles, such as Ben Shneiderman's golden rules, which advocate for error prevention by constraining invalid inputs—such as limiting numeric fields to digits only—and providing targeted recovery guidance if issues arise. Error-proofing techniques integrate s directly into software to block or detect errors at their source, inspired by methodologies adapted for digital environments. Validation checks, for instance, automatically verify input formats—like email addresses—before processing, halting erroneous submissions and promoting without user intervention. functions serve as a key fail-safe, enabling users to reverse actions easily, which encourages experimentation and limits the consequences of inadvertent choices, such as accidental edits in document editors. These techniques shift the burden from users to the system, ensuring errors are either impossible or immediately reversible. Adherence to international standards and guidelines further standardizes these improvements for broad applicability. The ISO 9241-110 standard outlines seven dialogue principles for human-system interaction, including error tolerance—which designs systems to recover from mistakes with minimal disruption—and , allowing users to initiate and manage actions safely to avoid unintended outcomes. Similarly, Jakob Nielsen's ten usability heuristics emphasize error prevention as a core tenet, recommending the elimination of high-risk conditions through defaults, constraints, and feedback to avert both slips and more deliberate mistakes. Compliance with these frameworks, derived from empirical studies, ensures interfaces are ergonomic and resilient to common human limitations. Recent innovations in AI-driven predictive interfaces represent advanced system-level interventions to curb user errors proactively. Adaptive keyboards with word , for example, suggest completions based on , reducing uncorrected typing errors by about 25% in on-screen keyboard use among blind users by facilitating quicker and more accurate selections. These AI enhancements, powered by , extend to auto-correction in mobile apps, where predictive algorithms analyze patterns to preempt misinputs, achieving keystroke reductions of up to 73% in free-text entry scenarios and thereby lowering overall rates. Such technologies exemplify how can personalize interfaces, adapting in real-time to user for sustained minimization.

References

  1. https://www.interaction-design.org/literature/topics/human-error
  2. https://www.nngroup.com/articles/user-mistakes/
  3. https://users.ece.cmu.edu/~koopman/des_s99/human/
  4. https://www.sciencedirect.com/science/article/abs/pii/S000368700600041X
  5. https://pages.ucsd.edu/~dnorman/DNMss/errordesign.html
  6. https://www.computerhope.com/jargon/u/usererro.htm
  7. https://www.uxpin.com/studio/blog/user-error/
  8. https://www.inclusionhub.com/articles/designing-for-user-error
  9. http://roc.cs.berkeley.edu/papers/hpts01-draft.pdf
  10. https://www.browserstack.com/guide/difference-between-bugs-and-errors
  11. https://ntrs.nasa.gov/api/citations/19720007517/downloads/19720007517.pdf
  12. https://www.computerhope.com/jargon/p/pebkac.htm
  13. https://getbackstory.ai/Story/Details/4214
  14. https://www.ibm.com/history/punched-card
  15. https://www.tandfonline.com/doi/full/10.1080/00140139.2021.1953615
  16. https://computerhistory.org/blog/from-alto-to-ai/
  17. https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/human-computer-interaction-brief-intro
  18. https://www.cio.com/article/274775/it-organization-luser-pebkac-and-other-ways-it-insults-users.html
  19. https://quality-one.com/human-factors-engineering/
  20. https://www.amazon.com/Design-Everyday-Things-Revised-Expanded/dp/0465050654
  21. https://arxiv.org/html/2411.10234v1
  22. https://www.researchgate.net/publication/385899390_Generative_AI_in_Multimodal_User_Interfaces_Trends_Challenges_and_Cross-Platform_Adaptability
  23. https://www.emergobyul.com/news/common-user-interface-design-flaws-can-induce-use-errors
  24. https://www.researchgate.net/publication/8202797_Poor_Interface_Design_and_Lack_of_Usability_Testing_Facilitate_Medical_Error
  25. https://pmc.ncbi.nlm.nih.gov/articles/PMC7651955/
  26. https://www.sciencedirect.com/science/article/abs/pii/S1071581917300654
  27. https://uxpajournal.org/user-performance-and-satisfaction-of-tablet-physical-keyboards/
  28. https://pmc.ncbi.nlm.nih.gov/articles/PMC10692799/
  29. https://gato-docs.its.txst.edu/jcr:539b2954-119b-41cc-bb48-c36dda90a4ed/ergo_alt_kybds.pdf
  30. https://www.cbsnews.com/news/study-3-second-distractions-double-workplace-errors/
  31. https://pmc.ncbi.nlm.nih.gov/articles/PMC9180354/
  32. https://www.nist.gov/document/nistir-7804pdf
  33. https://dl.acm.org/doi/10.1145/3706598.3713450
  34. https://pmc.ncbi.nlm.nih.gov/articles/PMC10790890/
  35. https://www.researchgate.net/publication/271728002_More_Information_May_Reduce_Errors_for_Novice_Users
  36. https://mhealth.jmir.org/2023/1/e43186
  37. https://www.docuclipper.com/blog/data-entry-statistics/
  38. https://www.avatier.com/blog/passwords-case-passwordless/
  39. https://venturebeat.com/ai/report-56-of-workers-admit-theyve-accidentally-deleted-cloud-data
  40. https://www.cs.purdue.edu/homes/yonglezh/pub/upgrade-sosp21.pdf
  41. https://www2.eecs.berkeley.edu/Pubs/TechRpts/2005/CSD-05-1393.pdf
  42. https://support.microsoft.com/en-us/topic/fix-duplicate-ip-address-conflicts-on-a-dhcp-network-d68499da-69a3-da3b-4630-d17e502adf50
  43. https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-278a
  44. https://media.defense.gov/2023/Oct/05/2003314578/-1/-1/0/JOINT_CSA_TOP_TEN_MISCONFIGURATIONS_TLP-CLEAR.PDF
  45. https://www.sidekickinteractive.com/uncategorized/how-to-prevent-ble-pairing-failures-and-what-to-do-when-it-happens/
  46. https://learn.microsoft.com/en-us/answers/questions/4115808/windows-11-how-can-i-identify-incompatible-drivers
  47. https://www.asisonline.org/security-news/blog/2025/today-in-security-history-the-y2k-bug/
  48. https://www.hhs.gov/sites/default/files/threats-in-healthcare-cloud-computing.pdf
  49. https://cloudsecurityalliance.org/blog/2025/07/21/reflecting-on-the-2023-toyota-data-breach
  50. https://www.pcmag.com/encyclopedia/term/id10t
  51. https://www.webopedia.com/definitions/id10t-error/
  52. https://www.lenovo.com/us/en/glossary/id10t/
  53. https://www.theregister.com/2017/12/22/on_call/
  54. https://www.computer-dictionary-online.org/definitions-d/dau
  55. https://www.lalanguefrancaise.com/dictionnaire/definition/interface-chaise-clavier
  56. https://www.own3d.tv/en/blog/dictionary/noob/
  57. https://www.kitware.com/welcome-and-guide-first-time-contributors-with-a-github-action/
  58. https://pmc.ncbi.nlm.nih.gov/articles/PMC6223105/
  59. http://www.cs.umd.edu/~ben/papers/Lazar2003User.pdf
  60. https://www.nngroup.com/articles/frustrated-users/
  61. https://academic.oup.com/iwc/article/18/2/187/742018
  62. https://corporatefinanceinstitute.com/resources/data-science/data-loss/
  63. https://www.handyrecovery.com/how-much-does-data-recovery-cost/
  64. https://www.forbes.com/sites/technology/article/data-recovery-costs-2024/
  65. https://pmc.ncbi.nlm.nih.gov/articles/PMC6770433/
  66. https://www.nature.com/articles/s41599-024-03457-9
  67. https://www.aarp.org/research/topics/technology/info-2023/2023-technology-trends-older-adults.html
  68. https://pmc.ncbi.nlm.nih.gov/articles/PMC10150999/
  69. https://www.pingdom.com/outages/internet-outages-the-12-most-impactful/
  70. https://www.dynatrace.com/news/blog/six-causes-of-major-software-outages-and-how-to-avoid-them/
  71. https://www.keepersecurity.com/resources/cost-of-a-helpdesk-password-reset/
  72. https://www.prnewswire.com/news-releases/tech-disruptions-cost-companies-millions-of-dollars-in-lost-productivity-annually-according-to-research-from-ivanti-302550936.html
  73. https://cybersecurityventures.com/cybercrime-damage-costs-10-5-trillion-by-2025/
  74. https://www.verizon.com/business/resources/reports/dbir/
  75. https://www.stealthlabs.com/blog/the-25-biggest-data-breaches-and-attacks-of-2020/
  76. https://specopssoft.com/blog/mgm-resorts-service-desk-hack/
  77. https://netdiligence.com/wp-content/uploads/2019/07/NetDiligence-2019-Cyber-Claims-Study.pdf
  78. https://www.gartner.com/en/articles/ai-user-errors
  79. https://doi.org/10.1037/0021-9010.93.1.59
  80. https://pubmed.ncbi.nlm.nih.gov/18211135/
  81. https://doi.org/10.1155/2015/849308
  82. https://www.apa.org/monitor/2021/07/tech-older-adults
  83. https://www.nngroup.com/articles/ten-usability-heuristics/
  84. https://statmodeling.stat.columbia.edu/2024/01/06/ben-shneidermans-golden-rules-of-interface-design/
  85. https://asq.org/quality-resources/mistake-proofing
  86. https://www.alekvs.com/what-is-iso-9241-a-complete-guide-to-hci-and-usability-standards/
  87. https://pmc.ncbi.nlm.nih.gov/articles/PMC11388727/
  88. https://pmc.ncbi.nlm.nih.gov/articles/PMC6395458/
Add your contribution
Related Hubs
User Avatar
No comments yet.