Human Error and Commercial 

Aviation Accidents: A 

Comprehensive, Fine-Grained 

Analysis Using HFACS

Scott Shappell

Clemson University 

Clemson, SC 29634

Cristy Detwiler

Kali Holcomb

Carla Hackworth

Federal Aviation Administration

Civil Aerospace Medical Institute

Oklahoma City, OK 73125

Albert Boquet

Embry-Riddle Aeronautical University

Daytona Beach, FL 32114

Douglas Wiegmann

Mayo Clinic

Rochester, MN 55905

July 2006

Final Report

DOT/FAA/AM-06/18

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine technical reports are 

available in full-text from the Civil Aerospace Medical Institute’s publications 

Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-06/18 

4. Title and Subtitle 5. Report Date 

Human Error and Commercial Aviation Accidents: A Comprehensive, July 2006 

Fine-Grained Analysis Using HFACS 

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

Shappell SA,1 Detwiler CA,2 Holcomb KA,2 Hackworth CA,2 Boquet AJ,3

Wiegmann DA4

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 1

Clemson University, Clemson, South Carolina 29634 2

FAA Civil Aerospace Medical Institute, Oklahoma City, OK 73125

3

Embry-Riddle Aeronautical University, Daytona Beach, FL 32114 

4

Mayo Clinic; Rochester, MN 55905

11. Contract or Grant No. 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S. W. 

Washington, D.C. 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved subtask AM-B-05-HRR-521. 

16. Abstract 

The Human Factors Analysis and Classification System (HFACS) is a theoretically based tool for investigating 

and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS 

can be reliably used to identify general trends in the human factors associated with military and general aviation 

accidents. The aim of this study was to extend previous examinations of aviation accidents to include specific 

aircrew, environmental, supervisory, and organizational factors associated with 14 CFR Part 121 (Air Carrier) 

and 14 CFR Part 135 (Commuter) accidents using HFACS. The majority of causal factors were attributed to 

the aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. 

Comparisons were made between HFACS categories and traditional situational variables such as weather, 

lighting, and geographic region. Recommendations were made based on the HFACS findings presented. 

17. Key Words 18. Distribution Statement 

Human Error, HFACS, Aviation Accidents, Commercial 

Aviation

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, 

VA 22060; and the National Technical Information 

Service, Springfield, VA 22161

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 22 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

1

Human Error and Commercial Aviation Accidents: 

A Comprehensive, Fine-Grained Analysis Using HFACS

“Flying is not inherently dangerous, but to an even greater extent than the sea, it is terribly unforgiving ....” 

—Captain A. G. Lumplugh, British Aviation Insurance Group

INTRODUCTION

Since Silas Christofferson first carried passengers on his 

hydroplane between San Francisco and Oakland harbors 

in 1913, engineers and psychologists have endeavored to 

improve the safety of passenger and cargo flight. What 

began as an industry fraught with adversity and at times 

tragedy has emerged as arguably one of the safest modes 

of transportation today.

Indeed, no one can question the tremendous strides 

that have been made since those first passenger flights 

nearly a century ago. However, while commercial1 aviation accident rates have reached unprecedented levels of 

safety, little, if any, improvement has been realized over 

the last decade for either the air carrier or commuter/air 

taxi industry (Figure 1). Indeed, some have even suggested that the current accident rate is as good as it gets 

– or is it?

The challenge for the Federal Aviation Administration (FAA) and other civil aviation safety organizations 

is to improve an already very safe industry. The question 

is where to start when most of the “low hanging fruit” 

(e.g., improved powerplant and airframe technology, 

advanced avionics, and the introduction of automation) 

have been “picked.”

Although percentages vary, most would agree that 

somewhere between 60-80% of aviation accidents are 

due, at least in part, to human error (Shappell & Wiegmann, 1996). That being said, it may be surprising that 

with few exceptions (e.g., Billings & Reynard, 1984; 

Gaur, 2005; Li, Baker, Grabowski, & Rebok, 2001; 

Shappell & Wiegmann, 2003a, 2003b; Wiegmann & 

Shappell, 2003) most studies to date have focused on 

situational factors or pilot demographics, rather than 

the underlying human error causes of accidents. While 

no one disagrees that factors like weather, lighting (i.e., 

day versus night), and terrain contribute to accidents, 

pilots have little, if any, control over them. Likewise, 

little can be done to affect one’s gender, age, occupation, 

1 The FAA distinguishes between two types of commercial operations: 

those occurring under 14 CFR Part 121 – Air Carrier Operations 

and those occurring under CFR Part 135 - Commuter/air taxi 

operations.

or even flight experience, as flight hours alone are not 

the sole determinant of a safe pilot. 

Judging from current accident rates, situational and 

pilot demographic data alone have provided little in the 

way of preventing accidents, apart from identifying target 

populations for the dissemination of safety information. 

This is not to say that these variables are unimportant, 

nor would anyone argue that they do not influence 

aviation safety. However, given the multi-factorial nature 

of accidents (Baker, 1995), it may make more sense to 

examine these variables within the context of what we 

know about human error and accident causation. Perhaps 

then we might be able to affect human error and reduce 

aviation accidents beyond current levels. 

The problem is that, unlike situational and demographic variables that are tangible and well-defined (e.g., 

instrument meteorological conditions and visual meteorological conditions), human error is much more complex, 

making it difficult to apply any sort of taxonomy that is 

both easily understood and universally accepted. However, 

that may have changed with the development of the Human Factors Analysis and Classification System (HFACS) 

in the mid-1990s. In fact, since the U.S. Navy/Marine 

Corps fielded the original version in 1997, HFACS has 

been used to reliably investigate and classify human error 

in a variety of high-risk settings including civilian aviation (Gaur, 2005; Shappell & Wiegmann, 2003a, 2003b, 

2004; Wiegmann & Shappell, 2001a, 2003).

Figure 1. Air carrier and commuter/air taxi accident 

rates since 1985 (Source: NTSB).

0

0 . 5

1

1 . 5

2

2 . 5

3

3 . 5

4

4 . 5

8 5 8 6 8 7 8 8 8 9 9 0 9 1 9 2 9 3 9 4 9 5 9 6 9 7 9 8 9 9 ' 0 0 ' 0 1 ' 0 2 ' 0 3 ' 0 4

Accidents per 100,000 Flight Hours

Year

Figure 1. 135 & 121 Accident Rate by year

Commuter

Air Carrier
2

HFACS

It is generally accepted that (Wiegmann & Shappell, 

2001a, 2003) aviation accidents are typically the result of 

a chain of events that often culminate with the unsafe acts 

of operators (aircrew). The aviation industry is not alone 

in this belief, as the safety community has embraced a 

sequential theory of accident investigation since Heinrich 

first published his axioms of industrial safety in 1931 

(Heinrich, Peterson, & Roos, 1931). However, it was 

not until Reason published his “Swiss cheese” model of 

human error in 1990 that the aviation community truly 

began to examine human error in a systematic manner.

Drawing upon Reason’s (1990) concept of latent and 

active failures, HFACS describes human error at each of 

four levels: 1) the unsafe acts of operators (e.g., aircrew, 

maintainers, air traffic controllers), 2) preconditions for 

unsafe acts, 3) unsafe supervision (i.e., middle-management), and 4) organizational influences (Figure 2).

 A 

brief description of each causal category is provided to 

familiarize the reader.

Unsafe Acts of Operators

The unsafe acts of operators (aircrew) can be loosely 

classified into one of two categories: errors and violations (Reason, 1990). While both are common within 

2 A complete description of all 19 HFACS causal categories is available 

elsewhere (see Wiegmann & Shappell, 2003).

most settings, they differ markedly when the rules and 

regulations of an organization are considered. That is, 

while errors represent authorized behavior that fails to 

meet the desired outcome, violations refer to the willful 

disregard of the rules and regulations. It is within these 

two overarching categories that HFACS describes three 

types of errors (decision, skill-based, and perceptual) and 

two types of violations (routine and exceptional).

Errors

Decision errors. One of the more common error forms, 

decision errors, represents conscious, goal-intended 

behavior that proceeds as designed, yet the plan proves 

inadequate or inappropriate for the situation. Often 

referred to as honest mistakes, these errors typically manifest as poorly executed procedures, improper choices, or 

simply the misinterpretation and/or misuse of relevant 

information.

Skill-based errors. In contrast to decision errors, the 

second error form, skill-based errors, occurs with little 

or no conscious thought. Indeed, just as decision errors 

can be thought of as “thinking” errors, skill-based errors 

can be thought of as “doing” errors. For instance, little 

thought goes into turning one’s steering wheel or shifting gears in an automobile. Likewise, basic flight skills 

such as stick and rudder movements and visual scanning 

refer more to how one does something rather than where 

one is going or why. The difficulty with these highly 

Errors

UNSAFE

ACTS

Perceptual

Errors

Skill-Based

Errors

Decision

Errors Routine Exceptional

Violations

Inadequate

Supervision

Planned

Inappropriate

Operations

Failed to

Correct

Problem

Supervisory

Violations

UNSAFE

SUPERVISION

Resource

Management

Organizational

Climate

Operational

Process

ORGANIZATIONAL

INFLUENCES

PRECONDITIONS

FOR

UNSAFE ACTS

Condition of 

Operators

Physical/

Mental

Limitations

Adverse 

Mental 

States

Technological 

Environment

Physical 

Environment

Personal

Readiness

Crew Resource 

Management

Personnel 

Factors

Adverse 

Physiological 

States

Environmental 

Factors

Figure 2. The HFACS framework.
3

practiced and seemingly automatic behaviors is that they 

are particularly susceptible to attention and/or memory 

failures. As a result, skill-based errors frequently appear 

- the breakdown in visual scan patterns, inadvertent activation/deactivation of switches, forgotten intentions, and 

omitted items in checklists. Even the manner (or skill) 

with which one flies an aircraft (aggressive, tentative, or 

controlled) can affect safety.

Perceptual errors. While decision and skill-based errors have dominated most accident databases and have, 

therefore, been included in most error frameworks, the 

third and final error form, perceptual errors, has received 

comparatively less attention. No less important, these 

“perceiving” errors arise when sensory input is degraded, 

or “unusual” as is often the case when flying at night, in 

the weather, or in other visually impoverished environments. Faced with acting on imperfect or incomplete 

information, aircrew run the risk of misjudging distances, 

altitude, and decent rates, as well as responding incorrectly 

to a variety of visual/vestibular illusions.

Violations

Routine violations. Although there are many ways to 

distinguish between types of violations, two distinct forms 

have been identified based on their etiology. The first, 

routine violations tend to be habitual by nature and are 

often enabled by a system of supervision and management 

that tolerates such departures from the rules (Reason, 

1990). Often referred to as “bending the rules,” the classic example is that of the individual who drives his/her 

automobile consistently 5-10 mph faster than allowed 

by law. While clearly against the law, the behavior is, in 

effect, sanctioned by local authorities (police) who often 

will not enforce the law until speeds in excess of 10 mph 

over the posted limit are observed.

Exceptional violations. These types of violations, on the 

other hand, are isolated departures from authority, neither 

typical of the individual nor condoned by management. 

For example, while authorities might condone driving 65 

in a 55 mph zone, driving 105 mph in a 55 mph zone 

would almost certainly result in a speeding ticket. It is 

important to note that, while most exceptional violations 

are appalling, they are not considered “exceptional” because of their extreme nature. Rather, they are regarded 

as exceptional because they are neither typical of the 

individual nor condoned by authority.

Preconditions for Unsafe Acts

Simply focusing on unsafe acts, however, is like 

focusing on a patient’s symptoms without understanding the underlying disease state that caused it. As such, 

investigators must dig deeper into the preconditions for 

unsafe acts. Within HFACS, three major subdivisions 

are described: 1) condition of the operator, 2) personnel 

factors, and 3) environmental factors.

Condition of the Operator

Adverse mental states. Being prepared mentally is critical 

in nearly every endeavor; perhaps it is even more so in 

aviation. With this in mind, the first of three categories, 

adverse mental states, was created to account for those 

mental conditions that adversely affect performance and 

contribute to unsafe acts. Principal among these are the 

loss of situational awareness, mental fatigue, circadian 

dysrhythmia, and pernicious attitudes such as overconfidence, complacency, and misplaced motivation.

Adverse physiological states. Equally important, however, 

are those adverse physiological states that preclude the 

safe conduct of flight. Particularly important to aviation 

are conditions such as spatial disorientation, visual illusions, hypoxia, illness, intoxication, and a whole host of 

pharmacological and medical abnormalities known to 

affect performance. It is important to understand that 

conditions like spatial disorientation are physiological 

states that cannot be turned on or off – they just exist. 

As a result, these adverse physiological states often lead 

to the commission of unsafe acts like perceptual errors. 

For instance, it is not uncommon in aviation for a pilot 

to become spatially disoriented (adverse physiological 

state) and subsequently misjudge the aircraft’s pitch or 

attitude (perceptual error), resulting in a loss of control 

and/or collision with the terrain.

Physical and/or mental limitations. The third and final 

category of substandard conditions, physical/mental limitations, includes those instances when necessary sensory 

information is either unavailable, or if available, individuals simply do not have the aptitude, skill, or time to safely 

deal with it. In aviation, the former often includes not 

seeing other aircraft or obstacles due to the size and/or 

contrast of the object in the visual field. Likewise, there 

are instances when an individual simply may not possess 

the necessary aptitude, physical ability, or proficiency 

to operate safely. After all, just as not everyone can play 

linebacker for their favorite professional football team 

or be a concert pianist, not everyone has the aptitude or 

physical attributes necessary to fly aircraft.

Personnel Factors 

Often times, things that we do to ourselves will lead to 

undesirable conditions and unsafe acts as described above. 

Referred to as personnel factors, these preconditions have 

been divided into two general categories: crew resource 

management and personal readiness.

Crew resource management (CRM). It is not hard to 

imagine that when all members of the crew are not acting in a coordinated manner, confusion (adverse mental 
4

state) and poor decisions in the cockpit can ensue. Crew 

resource mismanagement, as it is referred to here, includes 

the failures of both inter- and intra-cockpit communication, as well as communication with Air Traffic Control 

(ATC )and other ground personnel. This category also 

includes those instances when crewmembers do not work 

together as a team, or when individuals directly responsible 

for the conduct of operations fail to coordinate activities 

before, during, and after a flight.

Personal readiness. Individuals must, by necessity, ensure 

that they are adequately prepared for flight. Consequently, 

the category of personal readiness was created to account 

for those instances when rules such as disregarding crew 

rest requirements, violating alcohol restrictions, or selfmedicating, are not adhered to. However, even behaviors 

that do not necessarily violate existing rules or regulations (e.g., running ten miles before piloting an aircraft 

or not observing good dietary practices) may reduce the 

operating capabilities of the individual and are, therefore, 

captured here as well.

Environmental Factors

Although not human per se, environmental factors 

can also contribute to the substandard conditions of 

operators and hence to unsafe acts. Very broadly, these 

environmental factors can be captured within two general 

categories: the physical environment and the technological environment.

Physical environment. The impact that the physical 

environment can have on aircrew has long been known 

and much has been documented in the literature on 

this topic (e.g., Nicogossian, Huntoon, & Pool, 1994; 

Reinhart, 1996). The term physical environment refers 

to both the operational environment (e.g., weather, 

altitude, terrain), as well as the ambient environment, 

such as heat, vibration, lighting, and toxins in the cockpit. For example, flying into adverse weather reduces 

visual cues, which can lead to spatial disorientation and 

perceptual errors. Other aspects of the physical environment such as heat can cause dehydration, which reduces 

a pilot’s alertness level, producing a subsequent slowing 

of decision-making processes or even the inability to 

control the aircraft. Likewise, a loss of pressurization at 

high altitudes or maneuvering at high altitudes without 

supplemental oxygen in unpressurized aircraft can result 

in hypoxia, which leads to delirium, confusion, and a 

host of unsafe acts.

Technological environment. Pilots that often find themselves in a technological environment that can also have 

a tremendous impact on their performance. Within the 

context of HFACS, the term technological environment 

encompasses a variety of issues, including the design of 

equipment and controls, display/interface characteristics, 

checklist design, and automation, to name a few. Indeed, 

one of the classic design problems first discovered in 

aviation was the similarity between the controls used 

to raise and lower the flaps and those used to raise and 

lower the landing gear. Such similarities often caused 

confusion among pilots, resulting in the frequent raising 

of the landing gear while still on the ground. Likewise, 

automation designed to improve human performance 

can have unforeseen consequences. For example, highly 

reliable automation has been shown to induce adverse 

mental states such as overconfidence and complacency, 

resulting in pilots following the instructions of the automation even when “common sense” suggests otherwise. 

In contrast, unreliable automation can often result in a 

lack of confidence and disuse of automation even though 

aided performance is safer than unaided performance 

(Wickens & Hollands, 2000).

Unsafe Supervision

Clearly, aircrews are responsible for their actions and, 

as such, must be held accountable. However, in some instances, they are the unwitting inheritors of latent failures 

attributable to those who supervise them. To account for 

these latent failures, the overarching category of unsafe 

supervision was created within which four categories 

(inadequate supervision, planned inappropriate operations, failed to correct known problems, and supervisory 

violations) are included.

Inadequate supervision. This category refers to failures 

within the supervisory chain of command as a direct result 

of some supervisory action or inaction. At a minimum, 

supervisors must provide the opportunity for individuals to succeed. It is expected, therefore, that individuals 

will receive adequate training, professional guidance, 

oversight, and operational leadership, and that all will 

be managed appropriately. When this is not the case, 

aircrew can become isolated, thereby increasing the risks 

associated with day-to-day operations.

Planned inappropriate operations. The risk associated 

with supervisory failures come in many forms. Occasionally, for example, the operational tempo and/or schedule 

are planned such that individuals are put at unacceptable 

risk and, ultimately, performance is adversely affected. As 

such, the category of planned inappropriate operations 

was created to account for all aspects of improper or 

inappropriate crew scheduling and operational planning, 

which may focus on such issues as crew pairing, crew rest, 

and managing the risk associated with specific flights.

Failed to correct known problems. The remaining two 

categories of unsafe supervision, the failure to correct 

known problems and supervisory violations, are similar, 

yet considered separately within HFACS. The failure to 

correct known problems refers to those instances when 
5

deficiencies among individuals, equipment, training, or 

other related safety areas are “known” to the supervisor, 

yet are allowed to continue uncorrected. For example, the 

failure to consistently correct or discipline inappropriate 

behavior certainly fosters an unsafe atmosphere but is not 

considered a violation if no specific rules or regulations 

were broken.

Supervisory violations. This category is reserved for those 

instances when supervisors willfully disregard existing 

rules and regulations. For instance, permitting aircrew 

to operate an aircraft without current qualifications or 

license is a flagrant violation that may set the stage for 

the tragic sequence of events that may follow.

Organizational Influences

Where decisions and practices by front-line supervisors 

and middle-management can adversely impact aircrew 

performance, fallible decisions of upper-level management may directly affect supervisors and the personnel 

they manage. Unfortunately, these organizational influences often go unnoticed or unreported by even the 

best-intentioned accident investigators. The HFACS 

framework describes three latent organizational failures: 

1) resource management, 2) organizational climate, and 

3) operational processes. 

Resource management. This category refers to the 

management, allocation, and maintenance of organizational resources, including human resource management 

(selection, training, staffing), monetary safety budgets, 

and equipment design (ergonomic specifications). In 

general, corporate decisions about how such resources 

should be managed center around two distinct objectives 

– the goal of safety and the goal of on-time, cost-effective 

operations. In times of prosperity, both objectives can 

be easily balanced and satisfied. However, there may 

also be times of fiscal austerity that demand some give 

and take between the two. Unfortunately, history tells 

us that safety is often the loser in such battles, as safety 

and training are often the first to be cut in organizations 

experiencing financial difficulties.

Organizational climate. The concept of an organization’s culture has been described in many ways; however, 

here it refers to a broad class of organizational variables 

that influence worker performance. One telltale sign 

of an organization’s climate is its structure, as reflected 

in the chain-of-command, delegation of authority and 

responsibility, communication channels, and formal 

accountability for actions. Just like in the cockpit, 

communication and coordination are vital within an 

organization. However, an organization’s policies and 

culture are also good indicators of its climate. Consequently, when policies are ill-defined, adversarial, or 

conflicting, or when they are supplanted by unofficial 

rules and values, confusion abounds, and safety suffers 

within an organization.

Operational process. Finally, operational process refers 

to formal processes (operational tempo, time pressures, 

production quotas, incentive systems, schedules, etc.), 

procedures (performance standards, objectives, documentation, instructions about procedures, etc.), and oversight 

within the organization (organizational self-study, risk 

management, and the establishment and use of safety 

programs). Poor upper-level management and decisions 

concerning each of these organizational factors can also 

have a negative, albeit indirect, effect on operator performance and system safety. 

PURPOSE

The goal of the present study was twofold: 1) to extend our previous HFACS analyses beyond military and 

general aviation (GA) to include a comprehensive analysis 

of commercial aviation; and 2) to combine the power 

of a theoretically derived human error framework (i.e., 

HFACS) with traditional situational and demographic 

data from the accident records. In accomplishing both 

objectives, the present study will begin to quantify the 

role human error plays in the genesis of commercial 

aviation accidents. 

METHOD

Data

Commercial aviation accident data (i.e., 14 CFR Part 

121 – air carrier; 14 CFR Part 135 – commuter) from 

calendar years 1990-2002 were obtained from databases 

maintained by the National Transportation Safety Board 

(NTSB) and the FAA’s National Aviation Safety Data 

Analysis Center (NASDAC). The NTSB reports two levels 

of investigation: factual and final. The factual investigation 

is a preliminary report that only includes basic descriptive information associated with the accident (location, 

time-of-day, weather conditions, etc.) but lists no causal 

factors. The final report contains all the information in 

the factual report as well as the causal factors associated 

with the accident. Consequently, only final reports were 

included in this study.

Also eliminated from consideration were accidents 

that were classified as having “undetermined causes” 

and those that were attributed to sabotage, suicide, or 

criminal activity (e.g., stolen aircraft). The data were 

culled further to include only those accidents that involved aircrew or supervisory error. Of the remaining 

1,020 accidents, 181 involved air carrier aircraft and 

839 involved commuter aircraft.
6

A summary of the remaining air carrier and commuter 

accidents involving aircrew or supervisory error is presented in Table 1. Sixty-eight percent of all commercial 

aviation accidents included in this study involved some 

form of aircrew or supervisory error.

Causal Factor Analysis Using HFACS

Six pilots were recruited from the Oklahoma City 

area as subject-matter experts (SMEs). All were certified 

flight instructors with a minimum of 1,000 flight hours 

at the time they were recruited. 

Each pilot was provided roughly 16 hours of instruction on the HFACS framework, which included didactic 

lecture and practice (with feedback) using the HFACS 

framework with NTSB/NASDAC accident reports. After 

training, the pilot-raters were randomly assigned accidents so at least two separate pilot-raters independently 

analyzed each accident.

Using narrative and tabular data obtained from both 

the NTSB and the FAA NASDAC, the pilot-raters were 

instructed to classify each aircrew or supervisory causal 

factor identified by the NTSB using the HFACS framework. Note that only those causal and contributory factors identified by the NTSB were classified. That is, the 

pilot-raters were instructed not to introduce additional 

casual factors that were not identified by the original 

investigation. 

After the pilot-raters made their initial classifications 

of the NTSB causal factors (i.e., skill-based error, decision-error, etc.), the two independent ratings were compared. Where disagreements existed, the corresponding 

pilot-raters were instructed to reconcile their differences, 

and the consensus classification was included in the 

database for further analysis. Overall, pilot-raters agreed 

on the classification of causal factors within the HFACS 

framework more than 85% of the time, an excellent 

level of agreement considering that this was essentially a 

classification/decision-making task.

Human Factors Quality Assurance

The data used in this study were drawn from NTSB/

NASDAC investigation reports that are often highly 

technical in nature, requiring a fundamental understanding of specific terms, flight conditions, and the 

overall domain of aviation to be effectively classified 

and coded. As aviation SMEs, the pilot-raters were able 

to clearly understand all aspects of the accident report. 

Consequently, they were considered the appropriate 

personnel for conducting the overall HFACS analysis of 

the commercial accident reports.

Pilots, however, are not SMEs in the domain of psychology or human factors and may not fully understand the 

theoretical underpinnings associated with the various error 

types within the HFACS framework. As a result, pilots 

might classify human error data somewhat differently 

than SMEs in human factors. On the other hand, pilots 

in this study were trained on HFACS, which provided 

some level of expertise when assessing human error. In 

fact, an earlier study addressed this issue by comparing 

the coded database of a commercial pilot rater to that of a 

psychologist and found the data to be reliable (Wiegmann 

& Shappell, 2001a).

Nonetheless, to ensure that the pilot raters grasped 

the psychological aspects underlying human error and 

HFACS, four additional SMEs (all co-authors of this 

manuscript) with expertise in human factors/aviation 

psychology examined each HFACS classification that the 

pilot SMEs had assigned to a given human cause factor. 

Table 1. Frequency of Accidents Associated with an Aircrew or Supervisory 

Human Error. 

Aircrew/Supervisory Error Only 

Year Air Carrier Commuter Combined 

Total

Accidents Percentage 

1990 9 81 90 134 67% 

1991 10 71 81 121 67% 

1992 9 67 76 103 74% 

1993 14 67 81 99 82% 

1994 11 74 85 113 75% 

1995 13 59 72 105 69% 

1996 14 71 85 123 69% 

1997 22 68 90 130 69% 

1998 14 62 76 121 63% 

1999 15 62 77 120 64% 

2000 20 62 82 135 61% 

2001 18 52 70 120 58% 

2002 12 43 55 92 60% 

Total 181 839 1020 1516 68% 

Average 13.92 64.54 78.46 116.6 

Note: Percentages represent the percent of commercial (both air carrier and commuter) aviation 

accidents associated with aircrew/supervisory error. For example, 90 of 134 commercial aviation 

accidents (67%) were associated with aircrew and/or supervisory error. 
7

To aid in the process, descriptive statistics were used to 

identify outliers in the data, after which the corresponding 

NTSB/NASDAC report was obtained. The reports were 

then independently reviewed by a minimum of two human factors SMEs for agreement with the previous codes. 

After the human factors SMEs came to a consensus, the 

codes were either changed in the database or left as the 

pilot SMEs originally coded them. In the end, less than 

5% of all causal factors were modified during the human 

factors quality assurance process.

RESULTS

Overall

A summary of the HFACS analyses of commercial 

aviation accidents can be found in Table 2. What is apparent from the data is that the majority of human causal 

factors identified in the database involved aircrew and their 

environment (i.e., unsafe acts of operators and preconditions 

for unsafe acts) rather than supervisory or organizational 

factors. Nevertheless, when organizational influences 

were observed, they typically involved operational processes 

such as inadequate or non-existent procedures, directives, 

standards, and/or requirements, or in the case of commuter operations, inadequate surveillance of operations. 

Unsafe supervision, on the other hand, typically involved 

inadequate supervision, in general, or the failure to provide 

adequate training. 

As anticipated, a large number of environmental conditions were identified within the commercial aviation 

database, particularly those associated with aspects of the 

physical environment like weather and lighting. However, 

they were not uniformly distributed across air carrier 

and commuter operations, as considerably more issues 

associated with the physical environment were observed 

during commuter (63%) than air carrier operations 

(37%). In contrast, the accident record revealed surprisingly few problems associated with the technological 

environment.

Preconditions associated with aircrew were also frequently observed within the accident record. For instance, 

crew resource management failures were identified in nearly 

one out of every five air carrier accidents examined. Even 

more interesting, the nature of the CRM failure differed 

Table 2. Frequency and percentage of accidents associated with 

each HFACS causal category by type of operation.

 HFACS Category Air Carrier Commuter Total

Organizational Influences N (%) N (%) N (%) 

Resource Management 4 (2.2) 0 (0.0) 4 (0.4) 

Organizational Climate 0 (0.0) 4 (0.5) 4 (0.4) 

Operational Process 21 (11.6) 29 (3.5) 50 (4.9) 

Unsafe Supervision 

Inadequate Supervision 15 (8.3) 21 (2.5) 36 (3.5) 

Planned Inappropriate Operations 3 (1.7) 5 (0.6) 8 (0.8) 

Failed to Correct Known Problems 0 (0.0) 0 (0.0) 0 (0.0) 

Supervisory Violations 0 (0.0) 2 (0.2) 2 (0.2) 

Preconditions of Unsafe Acts 

Environmental Conditions 

Technological Environment 11 (6.1) 4 (0.5) 15 (1.5) 

Physical Environment 67 (37.0) 525 (62.6) 592 (58.0) 

Conditions of the Operator 

Adverse Mental States 6 (3.3) 60 (7.2) 66 (6.5) 

Adverse Physiological States 6 (3.3) 18 (2.1) 24 (2.4) 

Physical/Mental Limitations 6 (3.3) 39 (4.6) 45 (4.4) 

Personnel Factors 

Crew Resource Management 34 (18.8) 75 (8.9) 109 (10.7) 

Personal Readiness 0 (0.0) 3 (0.4) 3 (0.3) 

Unsafe Acts of the Operator 

Skill-based Errors 77 (42.5) 499 (59.5) 576 (56.5) 

Decision Errors 71 (39.2) 303 (36.1) 374 (36.7) 

Perceptual Errors 10 (5.5) 56 (6.7) 66 (6.5) 

Violations 31 (17.1) 205 (24.4) 236 (23.1) 

Note: Numbers in the table are frequencies and percentages (parentheses) of accidents that 

involved at least one instance of an HFACS category. For example 77 of the 181 air 

carrier accidents (77/181 or 42.5%) were associated with at least one skill-based error. 

Because accidents are generally associated with more than one causal factor, the 

percentages in the table do not add up to 100%. 
8

between the two commercial operations. That is, while 

over 60% of the CRM failures associated with air carrier 

accidents involved “inflight” CRM failures (inflight crew 

coordination, communication, monitoring of activities, 

etc.), over 80% of the CRM failures observed during 

commuter operations involved “preflight” activities (such 

as planning and briefing).

Although arguably not as common, the condition of 

the operator was cited as a causal factor in several of the 

accidents examined. For instance, adverse mental states

(e.g., diverted attention, pressure, etc.) were identified 

in just over 7% of the commuter accidents, followed 

by physical/mental limitations (lack of experience) and

adverse physiological states (spatial disorientation, visual 

illusions, etc.).

As seen in other aviation operations (Shappell & 

Wiegmann, 1995, 1997, 2003a, 2003b, 2004; Wiegmann 

& Shappell, 1997, 2001a, 2001b, 2003) the majority of 

commercial aviation accident causal factors were found 

at the unsafe act level. Indeed, just over half of the accidents were associated with at least one skill-based error, 

followed by decision errors (36.7%) and violations of the 

rules and regulations (23.1%). Perceptual errors were 

much less common, accounting for roughly 7% of the 

accidents in the database. 

Because of the differences between air carrier and 

commuter operations (i.e., airframes, crew composition, size of the organization, etc.) it was anticipated 

that there would be differences in the pattern of human 

error observed - particularly where the unsafe acts of 

aircrew were concerned. However, a comparison of the 

unsafe acts committed during these operations (Figure 3) 

yielded very little disparity. In fact, the only significant 

difference involved skill-based errors, which were nearly 

twice as likely to have occurred during accidents involving 

commuter than air carrier aircraft (Χ2 = 17.368, p<.001; 

odds ratio = 1.982). On the surface, it did appear that 

slightly more violations were committed during accidents 

involving commuter than air carrier operations; however, 

the difference was not statistically significant.4 Likewise, 

the small differences observed for decision and perceptual 

errors did not reach statistical significance.

Similar to other civil aviation accident data we have 

reported (Shappell & Wiegmann, 2003a, 2003b, 2004; 

Wiegmann & Shappell, 2003), there was little variation 

3 The overarching category of violations was used rather than the subordinate 

categories of routine and exceptional violations because differentiating between 

the two, post hoc, is complicated by the fact that most investigations do not 

provide the detail necessary to make a reliable distinction between the two 

types of violations.

4 Given that Chi square analyses are strongly influenced by sample size, a 

conservative p-value of p<.001 was adopted to reduce the likelihood that 

spurious significant results would be obtained.

in the distribution of unsafe acts committed annually by 

aircrew flying either air carrier or commuter operations 

(Figure 4). When accidents occurred in either type of 

commercial operation, they were typically associated with 

more skill-based errors, followed by decision errors, violations, and perceptual errors, respectively. This was true 

even though the air carrier data had to be averaged over 

3-4 year blocks due to the small number of accidents in 

the database (Figure 4A). Moreover, with the exception 

of violations, which has shown a slight increase since the 

1993-1995 time frame, the annualized data were relatively 

flat, suggesting that there has been little impact on any 

specific type of human error over the last 13 years.

14 CFR Part 135 - Commuter Operations

Because of the relatively small number of air carrier 

accidents in the database related to aircrew/supervisory 

error, additional fine-grained analyses of those data were 

not possible. However, the same was not true for commuter operations. Therefore, a series of more detailed 

analyses were conducted using these data.

Visual Conditions

Given the relatively large percentage of accidents associated with physical conditions, in particular those associated 

with prevailing weather conditions and lighting, it seemed 

reasonable to begin with these two environmental causal 

factors. As can be seen in Figure 5A, just over 70% of the 

accidents occurred during visual meteorological conditions (VMC). Likewise, roughly 70% of the accidents 

occurred in broad daylight (Figure 5B).

To capitalize on the threat posed by both environmental causal factors, the two were combined to create 

a new variable that captured the “visual” conditions at 

the time of the accident. Specifically, two levels of visual 

conditions were created: 1) clear visual conditions, which 

included accidents that occurred during VMC and daylight conditions, and 2) impoverished visual conditions, 

which included accidents occurring during instrument 

meteorological conditions (IMC) or at twilight/night.

Unlike the results seen with weather and lighting conditions alone, when they were combined, the percentage 

of accidents occurring in clear visual conditions were 

only marginally higher than those occurring in visually 

impoverished conditions (Figure 5C). It would appear 

that, while weather and lighting conditions are important 

factors in aviation, their impact is potentially magnified 

when a pilot’s ability to see outside the aircraft is taken 

into consideration. 
9

0

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60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

14 CFR Part 121

14 CFR Part 135

Figure 3. HFACS unsafe acts by type of 

operation.

0

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60

70

80

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1990-1992 1993-1995 1996-1998 1999-2002

Skill-based

Errors

Perceptual

Errors

Decision

Errors

Violations

Percentage of Accidents

Year

A.

0

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90 91 92 93 94 95 96 97 98 99 '00 '01 '02

Skill-based

Errors

Perceptual

Errors

Decision

Errors

Violations

Percentage of Accidents

Year

B.

Percentages do not add up to 100%

Figure 4. Percentage of unsafe acts committed by 

aircrew during air carrier (Panel A) and commuter 

(Panel B) operations (by year).

0

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60

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80

90

100

VMC IMC

Percentage of Accidents

A.

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100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Clear

Impoverished

D.

0

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60

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90

100

Clear Impoverished

Percentage of Accidents

C.

0

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40

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60

70

80

90

100

Day Twilight

Percentage of Accidents

Night

B.

Figure 5. Percentage of commuter accidents by weather conditions (Panel A), 

lighting conditions (Panel B), visual conditions (Panel C) and visual conditions 

by unsafe acts (Panel D).
10

Naturally, one would expect the pattern of human error 

to be different during accidents in clear versus visually 

impoverished conditions. Indeed, when visual conditions 

were compared across the unsafe acts of aircrew, an interesting pattern of human error emerged. While skill-based 

errors were the most common error form observed during 

accidents in clear and impoverished conditions (Figure 

5D), violations were five times more likely to be attributed 

to accidents in visually impoverished conditions (Χ2 = 

92.332, p<.001; odds ratio = 5.077).

Upon closer examination (Table 3), intentional flight 

into IMC while operating under visual flight rules (i.e., 

VFR flight into IMC) accounted for nearly a third of the 

violations observed during impoverished visual conditions. In addition, the failure to adhere to procedures/

directives (violation), poor inflight planning/decision 

making (decision error), the loss of control in-flight (skillbased errors), and the failure to maintain sufficient airspeed 

(skill-based error) all were commonly cited as causes during 

accidents in visually impoverished conditions.

The failure to adhere to procedures/directives (violation) was also frequently seen among accidents in clear 

conditions, as was poor in-flight planning/decision-making (decision error). However, unlike impoverished visual 

conditions, commuter accidents occurring in the clear 

were often associated with the selection of unsuitable 

terrain (decision error) and the inability to compensate 

for winds (skill-based error).

Injury Severity

Previous investigations of GA accidents have shown 

distinct differences in the pattern of human error associated with fatal and non-fatal aviation accidents (Shappell 

& Wiegmann, 2003a, 2003b; Wiegmann & Shappell, 

2003). A similar examination of commuter accidents 

revealed that roughly 30% of all commuter accidents 

resulted in at least one fatality (Figure 6A). 

As with the findings regarding visual conditions, 

skill-based errors were associated with the majority of 

fatal and non-fatal accidents followed by decision errors, 

violations, and perceptual errors (Figure 6B). Of note 

however, violations were more than three times as likely to 

be associated with fatal accidents (Χ2 = 48.239, p<.001; 

odds ratio = 3.145).

Upon closer examination, it appears that causal factors such as intentional VFR flight into IMC (violation), 

poor in-flight planning/decision making (decision error), 

and control of the aircraft and airspeed (skill-based error) 

were the most frequently cited aircrew errors associated 

with fatal accidents (Table 4). In contrast, non-fatal 

accidents appear to be more closely associated with the 

failure to compensate for winds (skill-based error), loss 

of directional control on the ground (skill-based error), 

selection of unsuitable terrain (decision error), poor inflight planning/decision-making (decision error), and the 

failure to follow procedures/directives (violation).

Given the similarity in the pattern of human errors 

associated with visual conditions and injury severity (fatal 

Table 3. Commuter Unsafe Acts Fine-Grained Analysis by Clear vs. Impoverished.

SKILL – BASED ERRORS 

CLEAR IMPOVERISHED 

Subject N (%) Subject N (%) 

Compensation for Wind Conditions 42 (10.8) Aircraft Control 28 (10.6) 

Airspeed 38 (9.7) Airspeed 27 (10.2) 

Visual Lookout 32 (8.2) Clearance 21 (7.9) 

DECISION ERRORS 

CLEAR IMPOVERISHED

Subject N (%) Subject N (%) 

Unsuitable Terrain Selection 43 (21.5) In-Flight Planning/Decision 37 (24.3) 

In-Flight Planning/Decision 38 (19.0) Flight into Known Adverse Weather 11 (7.2) 

Pre-flight Planning/Decision 21 (10.5) Pre-flight Planning/Decision 9 (5.9) 

Violations

CLEAR IMPOVERISHED

Subject N (%) Subject N (%) 

Procedures/Directives 15 (23.8) Intentional VFR Flight into IMC 53 (30.1) 

Checklist 9 (14.3) Procedures/Directives 39 (22.2) 

Refueling 6 (9.5) Flight into Known Adverse Weather 10 (5.7) 

Note: Percentages in the table reflect the percentage of a given causal factor within the HFACS category (e.g., 

compensation for wind conditions accounted for 42 of 390 [10.8%] skill-based errors occurring in clear 

conditions). 
11

Table 4. Commuter Unsafe Acts Fine-Grained Analysis by Non-Fatal vs. Fatal.

SKILL – BASED ERRORS 

NON - FATAL FATAL

Subject N (%) Subject N (%)

Compensation for Wind Conditions 44 (9.6) Airspeed 35 (17.9) 

Directional Control 44 (9.6) Aircraft Control 23 (11.7) 

Visual Lookout 35 (7.6) Proper Altitude 16 (8.2) 

DECISION ERRORS 

NON - FATAL FATAL

Subject N (%) Subject N (%)

Unsuitable Terrain Selection 46 (19.3) In-Flight Planning/Decision 40 (35.1) 

In-Flight Planning/Decision 35 (14.7) Flight into Known Adverse Weather 9 (7.9) 

Planning/Decision 22 (9.2) Planning/Decision 8 (7.0) 

VIOLATIONS

NON - FATAL FATAL

Subject N (%) Subject N (%)

Procedures/Directives 23 (19.5) Intentional VFR Flight into IMC 37 (30.6) 

Intentional VFR Flight into IMC 20 (16.9) Procedures/Directives 28 (23.1) 

Checklist 12 (10.2) Aircraft Weight and Balance 9 (7.4) 

Note: Percentages in the table reflect the percentage of a given causal factor within the HFACS category (e.g., 

compensation for wind conditions accounted for 44 of 459 [9.6%] skill-based errors occurring in non-fatal commuter 

accidents).

0

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100

Non-Fatal Fatal

Percentage of Accidents

0

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Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Non-Fatal

Fatal

A.

B.

Figure 6. Percentage of fatal and non-fatal 

commuter accidents (Panel A) and percentage 

of fatal and non-fatal accidents by the unsafe 

acts (Panel B).
12

vs. non-fatal), it made sense to examine the combination of the two variables. As illustrated in Figure 7, the 

largest percentage of fatal commuter accidents occurred 

in visually impoverished conditions. In contrast, when 

the accident occurred in clear visual conditions, a much 

smaller percentage resulted in fatalities. Indeed, commuter 

accidents were over four times more likely to result in 

fatalities if they occurred in visually impoverished conditions (Χ2

 = 83.978, p<.001; odds ratio = 4.256).

Perhaps more important, skill-based errors were still 

the most frequently cited human error during fatal accidents in impoverished visual conditions (Figure 8). 

However, the differences observed in previous analyses 

between skill-based errors, decision errors, and violations

were much less obvious. Still, fully one-half of the fatal 

accidents occurring in visually impoverished conditions 

involved at least one violation – often intentional VFR 

flight into IMC (Table 5). Not surprising, given the 

environmental conditions at the time, poor in-flight 

planning (decision error) was also commonly cited among 

this subset of the data.

Regional Comparison

Our previous investigation of GA accidents (Detwiler, 

Hackworth, Holcomb, Boquet, Pfleiderer, Wiegmann, & 

Shappell, 2006) suggested that differences in the pattern 

of human error associated with commuter accidents in 

Alaska versus the rest of the U.S. might exist. However, 

unlike GA, our regional investigation of commuter aviation accidents revealed no significant differences between 

Alaska and the rest of the U.S. with regard to the various 

categories of unsafe acts. Even the fine-grained analysis 

of unsafe acts revealed similar patterns for commuter 

accidents occurring in Alaska and the rest of the U.S. 

(Table 6). For instance, the failure to maintain adequate 

altitude/clearance was the most frequently cited skill-based 

error in Alaska and the rest of the U.S. 

The only notable difference involved the type of violations and decision errors committed in Alaska versus 

the rest of the U.S. Specifically, while the most common 

violation occurring in the rest of the U.S. involved the 

failure to adhere to procedures and directives; intentional 

VFR flight into IMC was more common in Alaska. It was 

also noteworthy that the decision to take off or land on 

unsuitable terrain was observed more often in Alaska.

DISCUSSION

In the present study, we examined a variety of human 

and environmental factors associated with more than 

1000 commercial aviation accidents over a 13-year time 

frame. Given the sheer number of causal factors associated with these accidents, one might believe that there 

are literally thousands of ways to crash an aircraft. The 

results of this study, however, demonstrate that accidents 

appearing to be unique at first glance can be organized 

based upon underlying situational, demographic, and 

cognitive mechanisms of accident causation. In this way, 

previously unidentified trends in the accident record can 

be exposed.

Overall

Generally speaking, nearly 70% of the “commercial” 

aviation accidents occurring between 1990 and 2002 were 

associated with some manner of aircrew or supervisory 

error. However, the percentage varied slightly when air 

carrier (45%) and commuter (75%) aviation accidents 

were considered separately. This finding is consistent with 

results reported elsewhere (Li et al., 2001). However, 

while other studies typically focused on situational and 

demographic data, this study employed a human error 

framework (HFACS) to reveal the specific types of human 

error associated with commercial aviation accidents.

Figure 8. Percentage of unsafe acts committed 

by commuter aircrews during impoverished 

visual conditions that resulted in fatalities.

Figure 7. Injury severity by visual conditions.

0

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tal

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Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Figure 13. FAR 135 Unsafe Acts Under Impoverished Conditions 

Resulting in a Fatality
13

Table 5. Fine-Grained Analysis for Fatal 

Accidents Associated with Commuter 

Operations in Impoverished Conditions.

SKILL – BASED ERRORS 

 FATAL and IMPOVERISHED 

Subject N (%)

Airspeed 19 (16.0) 

Proper Altitude 16 (13.4) 

Aircraft Control 15 (12.6) 

DECISION ERRORS 

FATAL and IMPOVERISHED 

Subject N (%)

In-Flight Planning/Decision 26 (32.5) 

Flight into Known Adverse Weather 7 (8.8) 

Unintentional VFR Flight into IMC 5 (6.3) 

VIOLATIONS

FATAL and IMPOVERISHED 

Subject N (%)

Intentional VFR Flight into IMC 33 (31.7) 

Procedures/Directives 13 (12.5) 

IFR Procedure 11 (10.6) 

Note: Percentages in the table reflect the percentage of a given 

causal factor within the HFACS category (e.g., airspeed 

accounted for 19 of 119 [16%] skill-based errors occurring in 

impoverished conditions where a fatality occurred). 

Table 6. Commuter Unsafe Acts Fine-Grained Analysis for Alaska versus the 

Rest of the U.S.

SKILL – BASED ERRORS 

Alaska Rest of the U.S. 

Subject N (%) Subject N (%)

Altitude/Clearance 52 (20.4) Altitude/Clearance 66 (16.5) 

Compensation for Winds 29 (11.4) Aircraft Control 36 (9.0) 

DECISION ERRORS 

Alaska Rest of the U.S. 

Subject N (%) Subject N (%)

Unsuitable Terrain Selection 39 (32.0) In-Flight Planning/Decision 57 (24.8) 

In-Flight Planning/Decision 18 (14.8) Pre-flight Planning/Decision Making 21 (9.1) 

VIOLATIONS

Alaska Rest of the U.S. 

Subject N (%) Subject N (%)

Intentional VFR Flight into IMC 38 (42.2) Procedures/Directives 40 (26.8) 

Procedures/Directives 14 (15.6) Intentional VFR Flight into IMC 19 (12.8) 

Note: Percentages in the table reflect the percentage of a given cause factor within the HFACS causal 

category (e.g., compensation for wind conditions accounted for 29 of 255 [11.4%] skill-based errors 

occurring in Alaskan commuter accidents). 
14

Organizational Influences and Unsafe Supervision

Consistent with previous work (Wiegmann & Shappell, 

2001a), comparatively few commercial aviation accidents 

were associated with organizational and/or supervisory 

causal factors - particularly within the commuter aviation 

industry. In spite of this, a relatively large proportion of 

accidents involved issues related to operational processes. 

Causal factors associated with the remaining HFACS 

organizational causal categories, resource management and 

organizational climate, were rarely observed in the data. 

A closer inspection revealed that the particular type of 

operational process cited appeared to be dependent on the 

type of operation involved. Namely, air carrier accidents 

were typically associated with the manner in which procedures or directives were communicated assuming they 

existed at all. In contrast, commuter accidents were more 

often associated with a lack of organizational oversight. 

Exactly why this difference might exist requires a more 

in-depth investigation than what was performed here. 

However, the data do provide some insight into the 

types of organizational influences that have impacted 

commercial aviation safety.

Like organizational influences, causal factors attributed 

to middle-management centered on a single causal category (i.e., inadequate supervision) rather than the full 

range of unsafe supervision described within the HFACS 

framework. That being said, nearly 1 in 10 air carrier accidents were associated with some manner of inadequate 

supervision. However, unlike organizational factors, large 

differences were not observed between air carrier and 

commuter operations. Instead, when supervisors were 

identified as causal in the chain of events leading to an 

accident, issues such as the lack of general supervision/

oversight or the failure to provide adequate training were 

usually identified.

Nevertheless, a larger question looms over the commercial accident data. Namely, “Does the current accident 

data reflect the scope of the organizational/supervisory 

problem within commercial aviation, or is it possible that 

issues associated with middle- and upper-level management are under-reported?”

Consider, for example, a recently published report in 

which 48 accidents across the spectrum of civil aviation 

in India were examined using HFACS (Gaur, 2005). 

Of these, nearly half (21/48) involved aircraft operations similar to those reported here. Although it was not 

possible to separate their summary findings by type of 

operation, it is interesting to note that Gaur reported 

a large percentage of accidents were attributed, at least 

in part, to organizational influences (52%) and unsafe 

supervision (25%). Presumably, most of these were associated with Indian commercial aviation, since GA 

operations are often not associated with the upper tiers 

of HFACS (Wiegmann et. al., 2005). To the extent that 

management of U.S. air carriers can be compared with 

foreign-flagged air carriers, at least this research suggests 

that current accident investigations may not capture all 

the organizational influences associated with commercial 

aviation accidents. At a minimum then, a review of how 

investigators are trained on organizational and supervisory influences of accident causation may be in order. It 

might also prove beneficial to incorporate the use of a 

human error framework that includes supervisory and 

organizational components.

Preconditions for Unsafe Acts (Aircrew)

With a couple of notable exceptions, causal categories 

within the preconditions for unsafe acts were also lightly 

populated. One of those exceptions was the large proportion of accidents (particularly among commuter aviation) 

influenced by prevailing weather conditions and reduced 

visibility. This was not particularly surprising since studies 

like the one conducted by Baker, Lamb, Li, and Dodd 

(1993) reported similar results in their examination of 

commuter accidents between 1983 and 1988. However, 

what makes this particular finding noteworthy is that the 

problem appears to have persisted even though the FAA 

and its industry partners have gone to great lengths over 

the last several years to improve pilot skills and weather 

decision-making.

Likewise, a sizeable effort has been invested in crew 

resource management training, particularly within the 

air carrier industry. However, in the two decades since 

its implementation, the debate continues over whether 

or not these pioneering efforts have been effective (Salas, 

Burke, Bowers, & Wilson, 2001). After all, the findings 

here and elsewhere (U.S. General Accounting Office, 

1997; Wiegmann & Shappell, 2001a) suggest that failures 

of CRM still contribute to a large proportion of commercial aviation accidents.

Even so, there may be reason for guarded optimism. 

While on average nearly one in five air carrier accidents 

examined here were due, at least in part, to a CRM failure, 

the percentage dropped dramatically to just one out of 55 

accidents in 2002, and that one involved an air carrier. 

Whether this was a statistical “blip on the screen” or a 

sustained improvement in the area remains to be seen.

While previous efforts suggested that factors associated 

with the physical environment and crew resource management would be identified among the commercial data, it 

was surprising that other areas, in particular the condition 

of the operator (aircrew), were not identified in the accident 

record more often. The exception involved commuter 

aviation accidents, where a number of adverse mental states

(64 out of 839 accidents, or 7.2%) and physical/mental 

limitations (43 out of 839, or 4.6%) were observed.
15

In some ways, the fact that many commuter aviation 

operations are single-piloted may explain why adverse 

mental states played a more prominent role among these 

accidents. For instance, without a second set of eyes in 

the cockpit any distraction would likely be exacerbated 

and detract the pilot from the task at hand – flying the 

aircraft. Likewise, the aviation literature is ripe with 

examples where pressure, either self-induced or from 

management, has led pilots to accept risks beyond their 

abilities. At least one study suggests that this has been an 

issue with commuter aviation in Alaska (Conway, Mode, 

Berman, Martin, & Hill, 2005). 

Understandably then, diverted attention and pressure 

(whether self-induced or from management) were occasionally cited in the commuter accident record. Because 

of this, it seems that some manner of risk management 

training and/or simply reinforcing the basic tenets of 

aviation (i.e., aviate, navigate, and communicate – in 

that order) should be a component of any intervention 

strategy employed by the commuter aviation industry.

Perhaps more disconcerting than issues of attention 

and psychological pressure were the large number of 

commuter aviation accidents associated with the pilot’s 

lack of experience – something rarely seen among the air 

carrier accidents examined. Whether this represents a lack 

of flight hours or merely inexperience with a particular 

operational setting or aircraft remains to be determined. 

Still, flight hours alone may not be sufficient to overcome 

the lack of experience observed here. After all, flying 

straight and level in VMC will not prepare a pilot for the 

complexities of instrument flight or the dangers of flying 

in other potentially hazardous environments. 

Unsafe Acts of Operators (Aircrew)

As with our previous efforts involving civil and military 

aviation (Wiegmann & Shappell, 1997, 1999, 2001a, 

2001b), skill-based errors were the most prevalent form 

of aircrew error among the commercial aviation accidents 

examined. Particularly widespread were technique errors 

associated with handling or controlling the aircraft. More 

important, when the commercial data reported here were 

combined with our previous investigations of GA accidents (Wiegmann et al., 2005; Detwiler et. al., 2006) an 

interesting finding emerged. It appears that the percentage 

of skill-based errors associated with accidents increases 

systematically as one moves from air carrier (43%) to 

commuter (60%) to GA (73%) operations. 

At first glance, this would appear to suggest that pilot 

skill and proficiency are best among the air carrier industry 

and become progressively more suspect within commuter 

and GA. Recall that skill-based errors, by definition, occur 

during the execution of routine events (Reason, 1990; 

Rasmussen, 1982). Furthermore, once a particular skill 

is developed, it must be maintained through repetition 

and experience. Thus, most people would agree that GA 

pilots fly less and participate in fewer recurrent training 

sessions than their commercial counterparts. It stands 

to reason that their proficiency would be less than their 

commercial counterparts and may explain why skill-based 

errors are more prevalent among GA accidents.

However, the same cannot be said for commuter pilots 

that in some cases receive more flight time than air carrier 

pilots and may participate in the same level of recurrent 

training. Instead, the data seem to suggest that commuter 

aircrews fall somewhere in between air carrier and GA 

pilots with regard to proficiency – something that, if 

true, may necessitate additional regulations and currency 

requirements beyond what exists within the industry. 

On the other hand, the data may simply reflect wellknown differences in the sophistication of the aircraft 

being flown (e.g., was the aircraft instrument certified, 

was it outfitted with conventional instrumentation 

or technically advanced avionics). Or it could reflect 

operational requirements placed on the aircrews (e.g., 

flying very structured, well-planned operations versus 

comparatively less structured flights). Perhaps there is 

something still that has yet to be considered.

Regardless, skill-based errors were not the only error 

form identified within the commercial aviation database. 

Decision errors, violations, and, to a lesser extent, perceptual errors were found in a large proportion of the 

accidents examined. For example, decision errors were 

observed in roughly four out of every ten commercial aviation accidents, while violations and perceptual errors were 

observed in 23% and 7% of the accidents, respectively. 

Some have even argued that decision errors and violations 

are of the same ilk (i.e., both involve decisions by aircrew 

that go awry) and should actually be combined in the 

HFACS framework. If this were true, the combined causal 

category of decision error/violation would be roughly 

equivalent to that seen with skill-based errors. 

While on the surface, combining decision errors 

and violations may make sense, given that both involve 

“conscious decisions,” the motivation behind them, as 

well as the intervention strategies that have proven effective in the past, argue against it. As discussed earlier, 

violations represent the willful disregard for the rules and 

regulations and are often driven by intrinsic motivation, 

overconfidence, and other hazardous attitudes. In contrast, 

decision errors are often the result of a lack of knowledge 

and/or information, rather than one’s attitude. 

Therefore, while scenario-based training, in-flight 

planning aids, and education may improve pilot decisionmaking, these approaches have been largely ineffective in 

stemming violations. Instead, enforcing current standards 

and increasing accountability in the cockpit may be the 
16

only effective means to reduce violations of the rules – a 

tactic that is often difficult to employ in civil aviation. As 

a result, the FAA and the commercial aviation industry 

may have to look to other avenues to reduce violations 

such as the use of flight simulators that can demonstrate 

the hazards associated with violating the rules (Knecht, 

Harris, & Shappell, 2003).

Unlike skill-based errors, decision errors, and violations, perceptual errors contributed to the smallest percentage of commercial accidents, a percentage that was 

much less than that found in military research (Wiegmann 

& Shappell, 2003). However, given the non-tactical, 

non-aerobatic nature of commercial flight, this was not 

altogether unexpected. What’s more, a considerable effort 

has been brought to bear over the last several decades by 

the aerospace engineering and medicine communities 

to improve avionics, warning devices (ground collision 

avoidance systems), and awareness of perceptual errors 

due to visual and vestibular illusions. It would appear 

that those efforts have paid dividends. 

Still, it should also be noted that the differences observed 

between skill-based errors, decision errors, perceptual 

errors, and violations remained largely consistent across 

the 13 years of the study. The only possible exception 

was observed with air carrier accidents, where violations 

evidenced a small increase since 1993. Then again, some 

degree of caution should be taken in interpreting this 

particular finding, given that the air carrier data had to be 

collapsed into 3-4 year blocks due to the relatively small 

number of air carrier accidents occurring annually.

What this implies is that interventions employed in the 

1990s have had, at best, ubiquitous effects on the errors 

and violations committed by aircrew. Alternatively, it is 

quite possible that there has been no sustained impact of 

any particular intervention program. The latter should 

come as no surprise given that prior to this study, no 

comprehensive analysis of aircrew and supervisory error 

has been conducted using a theoretically derived framework of accident causation.

14 CFR Part 135 – Commuter Operations

One of the purposes of this study was to combine the 

power of traditional situational and demographic variables 

with a theoretically-based, human error framework to 

identify human error trends amid commercial aviation 

accidents. However, because of the sample size, only commuter aviation lent itself to this sort of analysis.

Visual Conditions and Injury Severity

With the development of sophisticated navigation 

instrumentation and other avionics, it is possible to 

fly safely in environments without any external visual 

cues. Yet, piloting an aircraft into visually impoverished 

environments without the necessary instruments or 

training can, and often does, lead to disaster. One needs 

to look no further than the accident data reported here 

to see the magnitude of this hazard to commuter aviation. That is, nearly one-half of all commuter accidents 

occurred in a visually impoverished environment. Of 

those, an alarming 70% resulted in fatalities. In contrast, 

only about 30% of the accidents that occurred in broad 

daylight resulted in a fatality.

Although interesting, this finding alone contributes 

little to our understanding of “why” aircraft crash in 

the weather or at night. However, when combined 

with HFACS, a distinguishable pattern of human error 

emerged. Indeed, while skill-based and decision errors were 

cited in a large proportion of these accidents, violations 

of the rules and regulations were five times more likely 

to occur during accidents in visually impoverished than 

in clear conditions. That is, intentional VFR flight into 

IMC, poor in-flight planning, and simply the failure to 

control the aircraft all were commonly associated with 

fatal accidents-particularly when they occurred in visually 

impoverished environments. What’s more alarming, many 

of these causal factors have been identified to some extent 

in the past (e.g., Baker, Lamb, Li, & Dodd, 1993).

So why is this still a problem and, more importantly, 

how could a professional pilot make such a decision to 

fly into hazardous weather? At least one study (Burian, 

Orasanu, & Hitt, 2000) suggests that pilots with less 

experience may “not trust what their eyes are telling 

them and so proceed on blindly” (p. 25). Referred to as 

plan continuation errors, Wiegmann, Goh, and O’Hare 

(2002) suggest that under certain conditions these errors 

are more often attributable to poor situation assessment 

than to motivational judgment, per se. In other words, 

sometimes experienced pilots simply misjudge the situation and make an honest mistake. Regardless, proper 

planning, both in the air and on the ground, is a critical 

component of flight safety. The solution may be to improve 

the quality of weather-related information to the pilot so 

that sound go/no-go decisions can be made.

However, it is one thing to “misjudge” weather information or make a bad decision, it is quite another 

to willfully fly into IMC without proper training or 

equipment. Such an act begs the question, “Why would 

someone take such an exceptional risk?” 

One possibility is social pressure. Indeed there are 

several examples of pilots being pressured by passengers 

or other aircrew to continue to their destination despite 

cues that they should do otherwise (Holbrook, Orasanu, 

& McCoy, 2003). In fact, at least for GA, the presence 

of passengers on board seems to influence the likelihood 

that an accident would be associated with VFR flight 

into IMC (Goh & Wiegmann, 2002). 
17

Still, social pressures cannot fully explain why a pilot 

would elect to fly VFR into IMC – particularly during 

cargo or repositioning flights where no passengers are 

on board. Alternatively, O’Hare and his colleagues (Batt 

& O’Hare, 2005; O’Hare & Owen, 1999; O’Hare & 

Smitheram, 1995) have offered an explanation structured 

around how pilots frame the situation of continuing or 

discontinuing flight into adverse weather. They found 

that pilots who framed diverting from a flight plan as a 

loss (e.g., loss of time, economic loss, or expense of effort) 

tend to continue flight into adverse weather; whereas 

those who frame a rerouting decision as a gain (e.g., in 

personal safety) tend to divert more. Indeed, gains and 

losses take on more meaning as pilots get closer to their 

destination.

Another possibility is that commuter pilots, on average, may not have the requisite experience to decide 

when a particular situation is beyond their ability. While 

this argument may hold for GA, where pilot experience 

varies from the novice to pilots with thousands of flight 

hours, commuter pilots typically have more experience 

than their GA counterparts well before their first paying 

passenger boards the aircraft. However, experience is a 

double-edged sword as others (e.g., Thomson, Onkal, 

Avcioglu, & Goodwin, 2004) have suggested that, as 

pilots gain experience through more flight hours, risk 

taking may also increase due to overconfidence and successful exposure to risky events. Put simply, experts may 

be more likely to take risks than novices. 

Regional Differences

In many ways, Alaska is the one of the world’s most 

demanding aviation environments, offering virtually every 

situation a pilot or operator might be confronted with. In 

a sense, there are very few situations experienced by pilots 

in the lower 48 states that have not been experienced by 

those in Alaska. Perhaps this is why few differences were 

observed in the pattern of human error associated with 

Alaska and the rest of the U.S.

However, one area where differences did exist was the 

violation of the rules and regulations; to be specific, VFR 

flight into IMC. Precisely why commuter pilots would 

be more prone to fly into adverse weather in Alaska than 

the rest of the U.S. is unknown, but at least one study 

(Conway, et. al., 2005) has shown that aircrews of highrisk operators in Alaska (those with a higher fatal crash 

rate than would be expected given the number of pilots 

they employed) differed from other operators in both 

experience and working conditions. On average pilots of 

high risk operators worked one hour more per day and 

10 hours more per week than control pilots. They were 

also more likely to fly into unknown weather conditions. 

Although their study did not identify any specific reason 

these pilots were more prone to take risks, it did suggest that factors such as “pilot fatigue and experience, 

financial pressures on operators, and inadequate weather 

information,” particularly in combination, may provide 

some clues.

Another area where regional differences existed 

involved takeoff and landing from unsuitable terrain. 

Although rarely associated with fatalities, these accidents 

are no less important given the staggering cost to recover 

an aircraft stuck on a sandbar or some other remote area. 

Unlike VFR flight into IMC, these accidents are much 

easier to understand because there are simply not many 

concrete runways and taxiways in Alaska. Instead, Alaskan commuter pilots may have to resort to frozen ice, 

sandbars, and other “natural runways” for support. Not 

surprising, what appears suitable from the air turns out 

to be unsuitable for aircraft when landed upon.

The obvious solution is to provide more suitable runways; pour more concrete, if you will. However, given the 

remoteness and harsh conditions of some of these areas, 

providing traditional runways would not be practical. 

Alternatively, some sort of training and awareness of 

what constitutes a suitable landing area, combined with 

the creation of more traditional runways, where possible, 

may be the only viable solution.

In light of the unique nature of the Alaskan environment, the FAA and Alaskan aviation community have 

joined efforts to employ a variety of safety programs aimed 

at reducing accidents associated with commuter operations. With programs like the FAA’s Circle of Safety and 

Capstone, and non-profit aviation safety organizations like 

the Medallion Foundation, it is hoped that improvements 

in Alaskan aviation safety will be realized. 

CONCLUSIONS

We are often told that sometimes the best studies ask 

more questions than they answer. If that sage wisdom is 

indeed true, then perhaps the present study was worthwhile. Regardless of one’s opinion of accident data and the 

current aviation accident investigation process, these data 

represent our best understanding of the underlying human 

error component of commercial aviation accidents. Even 

more, the results presented here represent the marriage 

of traditional demographic and human error analyses of 

commercial aviation (air carrier and commuter). While 

some of the findings may come as no surprise, they do 

provide data, where often only opinion existed. What’s 

more, they provide a foundation for the development, 

implementation, and quantifiable assessment of putative 

intervention and mitigation strategies.
18

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