Apf 2016 6 issue 1

Volume 6 / Number 1 / 2016 Editor-in-Chief Ioana Koglbauer Associate Editors Cristina Albuquerque AndrĂŠ Droog Hinnerk EiĂ&#x;feldt Harald Kolrep Monica Martinussen Michaela Schwarz Matthew J. W. Thomas

Aviation Psychology and Applied Human Factors

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Aviation Psychology and Applied Human Factors

Volume 6, No. 1, 2016 OfďŹ cial Organ of the European Association for Aviation Psychology (EAAP) and the Australian Aviation Psychology Association (AAvPA)

Editor-in-Chief

Ioana Koglbauer, Graz University of Technology, Institute of Mechanics, Kopernikusgasse 24/IV, 8010 Graz, Austria, Tel. +43 (316) 873-4111, E-mail journal@eaap.net

Associate Editors

Cristina Albuquerque, Lisbon, Portugal Andre´ Droog, Groningen, The Netherlands Hinnerk Eißfeldt, DLR, Hamburg, Germany Harald Kolrep, HFC, Berlin, Germany

Monica Martinussen, University of Tromsø, Norway Michaela Schwarz, Austro Control, Vienna, Austria Matthew Thomas, Westwood Thomas Associates, Goodwood, Australia

Editorial Board

Andrew Bellenkes, Monterey, CA, USA Robert Bor, London, UK Guy Boy, Melbourne, FL, USA Thomas Carretta, Dayton, OH, USA Sidney Dekker, Mount Gravatt, Australia Mike Feary, Moffett Field, CA, USA Shan Fu, Shanghai, PR China Kazuo Furuta, Tokyo, Japan Hans Giesa, Hamburg, Germany Don Harris, Coventry, UK Brent Hayward, Albert Park, Australia Brian Hilburn, Haddonfield, NJ, USA David Hunter, Peoria, AZ, USA Peter Jorna, Lage Vuursche, The Netherlands Hung-Sying Jing, Taiwan, ROC Wolfgang Kallus, Graz, Austria

Rob Lee, Canberra, Australia Wen-Chin Li, Cranfield, UK Carol Manning, Oklahoma City, OK, USA Dietrich Manzey, Berlin, Germany Lena Mårtensson, Stockholm, Sweden Peter Maschke, Hamburg, Germany Randy Mumaw, Seattle, WA, USA Peter Murphy, Franklin, Australia Jan Noyes, Bristol, UK Teresa Oliveira, Lisbon, Portugal Jan J. Roessingh, Amsterdam, The Netherlands Gideon Singer, Linkoping, Sweden Anthony Smoker, Lund, Sweden Mark Wiggins, Sydney, Australia Ann Williamson, Sydney, Australia

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Aviation Psychology and Applied Human Factors (2016), 6(1)

Ó 2016 Hogrefe Publishing

Contents Original Articles

The Effect of Mood on Performance in a Nonnormal Situation: Unscheduled Aircraft Evacuation

1

Morteza Tehrani and Brett R. C. Molesworth Safety Culture, Resilient Behavior, and Stress in Air Traffic Management

12

Michaela Schwarz, K. Wolfgang Kallus, and Kerstin Gaisbachgrabner A Flight Simulator Study of the Impairment Effects of Startle on Pilots During Unexpected Critical Events

24

Wayne L. Martin, Patrick S. Murray, Paul R. Bates, and Paul S. Y. Lee APAHF in Practice

An Application of the HFACS Method to Aviation Accidents in Africa

33

Isaac Munene Characteristics of General Aviation Accidents Involving Male and Female Pilots

39

Robert O. Walton and P. Michael Politano News and Announcements

The Best Paper Award: Aviation Psychology and Applied Human Factors (APAHF)

45

Meeting Report: How Safe is Your Change? Safety and Validation Workshop in Budapest

46

Katalin Nanaı´ Meeting Report: The 3rd European STAMP Workshop, Amsterdam, The Netherlands

48

Nektarios Karanikas and Robert J. de Boer

Ó 2016 Hogrefe Publishing

Meetings and Congresses

51

Aviation Human Factors Related Industry News

53

Aviation Psychology and Applied Human Factors (2016), 6(1)

Original Article

The Effect of Mood on Performance in a Nonnormal Situation Unscheduled Aircraft Evacuation Morteza Tehrani and Brett R. C. Molesworth University of New South Wales, School of Aviation, Sydney, NSW, Australia

Abstract: The effect of mood on performance in everyday situations is widely studied and the results commonly reveal a mood-congruence relationship. However, little is known about the effect of mood on performance in nonnormal situations such as those experienced during an unscheduled event. This study investigated whether induced mood (positive or negative) influenced performance during an unscheduled aircraft evacuation. Forty-five participants (15 female), with an average age of 21.90 (SD = 3.96) years, were randomly exposed to either positive or negative mood facilitation. Following this, all participants watched the same preflight safety video, and then had to conduct an unscheduled evacuation following a simulated water ditching. Participants exposed to a positive mood manipulator were found to commit fewer errors during the evacuation exercise and completed the evacuation in less than half of the time taken by participants who were exposed to a negative mood manipulator. In safety-critical environments such as aviation, these results highlight the advantages of creating an atmosphere or environment that induces positive moods.

Keywords: mood, performance, nonnormal situation, aviation, cabin safety

The effect of mood on performance is well documented.

Pleasant/positive moods such as happiness or elation have been shown to improve intellectual performance (i.e., verbal and quantitative ability; Albarracin & Hart, 2011), task interest (Hirt, Melton, McDonald, & Harackiewicz, 1996), self-perceived creativity (Montgomery, Hodges, & Kaufman, 2004), teamwork (Barsade, 2002), decisionmaking (Barsade & Gibson, 2007), and life meaningfulness (King, Hicks, Krull, & Del Gaiso, 2006). Within teams, pleasant moods (i.e., happy) have been shown to improve communication skills such as anticipatory communication patterns and detail of verbal responses (Pfaff, 2012). Happy people are also less sensitive to threats within their work environment, less defensive or cautious with their colleagues, and are more optimistic and confident (Cropanzano & Wright, 2001). By contrast, unpleasant/ negative moods such as sadness or sorrow have been shown to adversely affect: decision-making in terms of quantity of food eaten (Tice, Bratslavsky, & Baumeister, 2001); affective states, information processing, and task performance (Friedman, Forster, & Denzle, 2007); success and motivation of female rowers (Raglin, Morgan, & Luchsinger, 1990); and team processes, including team performance (Jordan, Lawrence, & Troth, 2006). However, the relationship between mood and performance is not always

Ă“ 2016 Hogrefe Publishing

in a mood-congruence direction. For example, Lount (2010) found that while a pleasant mood helped improve trust between group members, it harmed trust in intergroup interactions. These results are consistent with intergroup diversity behavior in the presence of mass panic. Moreover, Drury and colleagues (2009) propose that in the presence of a crowd and a panic situation, individuals offset the risk of death and injury from helping their own group members by reducing cooperative behavior. This in turn increases competition for an emergency escape (Drury et al., 2009). Forgas (1991) also found that a negative mood such as dysphoria motivated individuals to perform, and facilitated in self-servicing interpersonal choices as well as improved the memory for specific events.

Mood and emotions are closely linked. Emotions are defined as an affective state (i.e., feeling) that is orientated toward a specific event such as a gift or award and are experienced for a short period of time (i.e., ranging from seconds to hours; Newton, 2013). Mood similarly is defined as an affective state (i.e., feeling); however, this state lasts for an extended period of time, often counted in days, and is attributable to circumstances, such as pressure at work, as opposed to a single object or event (Russell, 2003). Examples of positive affective states are joy,

Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11 DOI: 10.1027/2192-0923/a000090

2

contentment, and relief. Examples of negative affective states are anger, fear, disgust, and shame (Eznack, 2013).

Researchers investigating the relationship between mood and performance commonly focus on everyday situations; situations that could be described as routine and/or non-life threatening. For example, Jordan, Lawrence, and Troth (2006) found that in a teamwork situation, negative mood reduced team performance in terms of team cohesion and decision-making. Negative mood in this case was measured by using a 10-item structured mood scale (attitude, interested, alert, excited, enthusiastic, inspired, proud, determined, strong, and active) called Positive and Negative Affect Schedule (PANAS), developed by Watson, Clark, and Tellegen (1988). Zohar (1999) also found that in the daily life of military jump masters, their mood was related to the daily hassles they faced, as well as fatigue and self-reported workload. Zohar employed a combination of scales to assess participants’ mood. For negative mood assessment he used the PANAS scale, while for fatigue he used the fatigue scale in McNair and colleagues’ Profile of Mood State (POMS) questionnaire (encompasses six factors: Depression, Vigor, Anger, Tension, Confusion, and Fatigue; McNair, Lorr, & Droppleman, 1971). Hence, what remains unknown, and is the central aim of the present research, is how mood state affects performance in situations that are not commonly experienced.

Take, for example, an emergency evacuation of an aircraft. Flying remains the safest mode of transportation (Savage, 2012). According to Savage, an individual is 48 times more likely to be fatally injured in a motor vehicle accident than in a commercial aircraft accident. Despite these statistical data, prior to every flight passengers are briefed about the safety features of the aircraft they boarded, and told how to respond in the unlikely event of an emergency. The aim of the present research is to examine the effect of mood on performance during an unscheduled emergency aircraft evacuation (i.e., nonnormal situation).

Mood takes the form of a moderating factor, thereby influencing behavior. For optimum performance, moderating factors need to act in the positive, hence enhancing rather than hindering performance. Parker, Reason, Manstead, and Stradling (1995) found precisely this when they surveyed 1,600 drivers in an attempt to better understand the reasons behind abnormal driving habits (i.e., lapses, errors, and violations). The results revealed that the best predictors of scores on the error factor were related to the susceptibility of driving to mood. Respondents who reported their driving was affected by their mood also reported more lapses and rated themselves as more errorprone than those with fewer driving lapses. Similar results have been found with tasks such as proofreading where Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

participants in a negative mood state commit more errors than their more neutral or happier colleagues (Ellis, Ottaway, Varner, Becker, & Moore, 1997).

In time-critical or high-hazard situations, such as a rapid disembarkation during an unscheduled (i.e., emergency) evacuation of an aircraft, counterproductive behavior has the potential to cause serious consequences. This is precisely what occurred during an emergency evacuation of a Boeing 737 at Manchester International Airport in 1985, resulting in the loss of 55 lives. During the evacuation, which was initiated in response to an engine fire, passengers’ behavior varied from chaotic and disorderly (i.e., climbing over seats), to erroneous (i.e., lack of knowledge on how to operate over wing exit; no formal instructions provided), ultimately causing congestion at two exit points and subsequently their blockage, exposing passengers to deadly smoke and toxic fumes (Air Accidents Investigation Branch, 1988).

In order to prevent precisely these types of incidents, aviation authorities around the world such as the American Federal Aviation Administration and the Australian Civil Aviation Safety Authority require all passengers to be briefed about the safety features of the aircraft they are on board prior to every flight, and for all new aircraft introduced into an airline’s fleet it must be demonstrated that all passengers can be evacuated within 90 s using only half of the available emergency exits (Federal Aviation Regulations [FARS], 14 CFR 23.803; Civil Aviation Order [CAO] 20.11).

However, as Muir, Bottomley, and Marrison (1996) have illustrated, expedient egress from an aircraft relies not only on the availability of exits, but also on how passengers behave in such circumstances. In fact, they found the most effective method to induce a serious blockage at an exit point was to provide financial incentives in the form of a bonus payment for the first 50% of volunteers to evacuate the aircraft, suggesting motivation plays a leading role in shaping passenger behavior in an aircraft emergency.

Whether mood affects behavior under similar conditions remains unknown.

Mood can be manipulated in a variety of ways. For example, Samuels and other authors found that colors can affect mood (Samuels, 1999; Stone & English, 1998). Moreover, in a study with 112 university students, they simply manipulated the color of the partitions between the work stations, and found that following a basic computer task, students who were exposed to the blue partition perceived the temperature to be cooler than those who were exposed to red partitions. The same students also felt calmer, and perceived their privacy to be higher than the students with the red partitions (Stone & English, 1998). Similarly music has also been found to affect mood, which in turn can impact on behavior (Moon, Kim, Lee, & Kim, 2014; Zwaag et al., 2012). Moreover, Krumhansl (1997) found that rapid Ó 2016 Hogrefe Publishing

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

dance-type rhythm music had a positive effect on mood (i.e., happy), while music with a slow tempo and constant pitch adversely affected mood (i.e., sad). Richards and Whittaker (1990) used pictures as a method to affect mood.

Simply by exposing participants to three images, such as images depicting peace or beauty (e.g., baby and two scenic views) or images depicting death or destruction (e.g., severed head, scared face, and football hooligans), and asking them to critique the images, they were able to manipulate mood (images of peace and beauty instilled a positive mood, whereas images of death and destruction instilled a negative mood).

The present study extended the research in the area of mood and performance, and investigated whether mood could be used to manipulate performance in a nonnormal situation, namely, an unscheduled aircraft evacuation.

It was hypothesized that participants in the positive mood manipulation condition would commit fewer errors and complete the evacuation quicker than participants in the negative mood manipulation condition.

Method Design Overview The research was conducted in two stages. The first stage tested the efficacy of the mood manipulator, while the second stage tested the effect of the mood manipulator in situ, namely, its effect on performance during an unscheduled evacuation of an aircraft. While both stages could have been incorporated into one experimental sequence, it would have required the measurement of mood three times, as opposed to two; the third time would be part way through the experimental sequence, thereby disrupting the natural flow of events during the unscheduled aircraft evacuation, hence jeopardizing the applied objective (i.e., ecological validity) of the research.

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Material The material consisted of: an information sheet, consent form, demographics questionnaire (i.e., age, gender), six photographs (three unpleasant and three pleasant images) serving as the mood manipulators, a photograph rating sheet, and a mood measurement scale (Profile of Mood State–Short form; POMS-SF).

Mood Manipulation The mood manipulating stimulus was consistent with that used by Richards and Whittaker (1990), and contained three unpleasant images (e.g., depicting scenes of violence) and three pleasant images (e.g., depicting scenes of flowers) along with a photograph rating sheet, namely, three questions designed to ensure the participants were engaged in the photographs (e.g., photographs pleasant or unpleasant, attracted their attention, saw professionalism in each photograph). Participants’ responses on the photograph rating sheet were reviewed as opposed to scored, owing to the subjective nature of the questions as well as the objective of the exercise (focus was on engagement and reflection on the images).

Mood Measurement Participants’ mood was measured using a shortened version of the POMS survey (i.e., POMS-SF; Terry, Lane & Fogarty 2003). The long form of the POMS survey (original survey) has been extensively used in a variety of settings including: hospitals, universities, and outcare patient facilities (Curran, Andrykowski, & Studts, 1995; Shacham, 1983; Terry et al., 2003; Zohar, 1999). In contrast to the long version that has 65 items covering six factors (e.g., tension–anxiety, depression–dejection, anger–hostility, fatigue–inertia, vigor– activity, and confusion–bewilderment) the short version has only 37 items with the same number of factors; however, it can be administered in approximately half the time without compromising the scale (Shacham, 1983). Shacham (1983) also investigated the validity of the new shorter scale through the conduct of a series of correlational analyses between each factor on the long scale and the short scale, which revealed a very high correlation (each factor above r = .95).

Stage 1 Procedure Participants Fourteen students and university staff (eight female), with an average age of 33 (SD = 14.33) years, participated in Stage 1 of the research. No reimbursement was provided and the average time to complete this stage was 5 min.

The research, including the stimuli for both stages of the study (i.e., mood manipulation and unscheduled aircraft evacuation exercise), was approved in advance by the University of New South Wales ethics panel. Ó 2016 Hogrefe Publishing

Participants were recruited from the University of New South Wales. They were provided with an information sheet and a consent form. Following this, participants were randomly assigned to one of two groups (negative or positive mood manipulation group) and asked to complete the 37item POMS–SF questionnaire. Depending on which group they were randomly assigned, they were provided with either three unpleasant or three pleasant photographs and asked to concentrate on each photograph for 30 s and rate them by Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

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M. Tehrani & Brett R. C. Molesworth, Mood and Performance

completing the photograph rating form. After they had completed the photograph rating form, a second POMS questionnaire was administered as a final step of the study. The total time taken to complete Stage 1 was approximately 5 min.

Results In order to determine whether the two mood manipulators were effective, mood scores prior to the mood manipulator were compared with mood scores following the mood manipulator for each experimental condition (positive vs. negative mood manipulation). However, before this, it was important to ensure that each participant evaluated the photographs as asked. This was achieved by reviewing the material written by each participant on the photograph rating sheet to ensure that they looked at and thought about the pictures presented. All participants wrote meaningful comments, and as a result no participant was excluded. Mood was calculated based on Shacham’s (1983) Total Mood Disturbance (TMD) scores for the POMS-SF using the formula:

Depression Dejection þ Tension Anxiety þ Anger Hostility þ Fatigue Inertia þ Confusion

Participants Forty-five university students (15 female), with an average age of 21.90 (SD = 3.96), years completed the research.

On average, participants had flown three single-leg sectors in the past 12 months and nine single-leg sectors in the past 5 years. No participant had reported being involved in an in-flight emergency1. All participants were reimbursed with a $10 bookshop gift voucher for their time. As noted in Stage 1, the research was approved in advance by the University of New South Wales ethics panel.

Design The research was designed to examine the effect of mood on performance during a nonnormal situation. Two independent variables featured; one repeated measures factor and one between-groups factor. The repeated measures factor Evacuation contained two levels (before vs. after), likewise did the between-groups factor Mood (negative vs. positive). Two dependent variables were employed, namely, number of errors during the nonnormal and unscheduled evacuation, and time taken to egress the aircraft. For all statistical procedures, α was set at .05.

Bewilderment þ ð24 Vigor ActivityÞ: Owing to the relatively small sample size, a nonparametric test was employed, that is, a Wilcoxon signed-rank test.

The results revealed that both mood manipulators were effective in positively influencing participants’ mood in the desired direction. Specifically, TMD scores in the positive mood manipulation group decreased from 24.47 to 22.76 – lower scores on TMD equals more positive mood; z (N = 6) = 2.20, p = .028. Similarly, TMD scores in the negative mood manipulation group increased from 21.77 to 22.67 – higher score on TMD equals more negative mood; z (N = 8) = 1.96, p = .050. This result indicates that the mood manipulators were effective in influencing participants’ mood in the desired direction.

Stage 2 Having established the efficacy of both the positive and negative mood manipulators, the next step was to test the effect of these mood manipulators on participants’ performance during an unscheduled evacuation of an aircraft; hence Stage 2 of the present study.

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Material The documentations consisted of: an information sheet, consent form, demographics questionnaire (i.e., age, gender), Macquarie dictionary and thesaurus, flight status information sheet (i.e., description about the flight), and photograph rating sheet (consistent with Stage 1). In addition, a Panasonic DMC-FS20 video camera and tripod (to record the evacuation event) was used to record participants.

Aircraft Cabin A mock aircraft cabin, reflecting a Boeing 737-800 singleaisle configuration, was constructed within the research facilities on campus. The aircraft set-up comprised 15 seats with five rows, using molded high-back plastic seats, one of which was modified as to be akin to an aircraft seat by fixing a seat belt and compartment below the seat to store a life jacket. The aircraft cabin also contained one over-wing emergency exit (type III) and one door (also used as an emergency exit during flight). All of the 15 seats and nominated emergency exits were set up on the port side of the passenger cabin. Seat pitch for the passenger rows was set according to the actual aircraft dimensions at 80 cm and

Owing to technical issues, data pertaining to flight frequency and in-flight emergency were lost. As a result, these questions were re-administered at the time of writing this manuscript and not all students who originally completed the research could be contacted (31 out 45 re-answered these questions).

Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

Ó 2016 Hogrefe Publishing

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

Emergency Exit

ROW 12

ROW 11

ROW 10

Emergency Row Pitch 99 cm

Legend:

Passenger Seat

Candidate’s Seat

ROW 9

Emergency Exit

ROW 8

Unobstructed Pathway 50 cm

Seat Pitch 80 cm

150 cm

Unobstructed Pathway 50 cm

5

Emergency Row Pitch 99 cm Front of Aircraft

Shortest Route to Emergency Exit

Figure 1. Experimental design layout of cabin.

for the emergency exit row at 99 cm (slightly wider) in accordance with the United States Federal Aviation Regulations (FAA FARS, 14 CFR 25.813, 2000). Identical seat spacing (pitch) was employed to reflect as close as possible that present in commercial aircraft.

The focus of the present research was on the economy class (see Figure 1), with two emergency exits. One emergency exit was located immediately behind the participant’s seat, as an over-wing exit, and the second exit was located two rows in front of the participant. Hence, the strategic placement of the emergency exits provided participants with a choice of exits to use. The emergency exit features, in terms of size and signage were a direct replication of those present on aircraft. All emergency exits were marked with EXIT signs. Other materials for the study included one aviation life jacket (permission granted from airline to use the aviation life jacket), and a pre-take-off safety briefing video (4 min 30 s). The same mood manipulator (photographs) and measurement (POMS questionnaire) were employed as in Stage 1.

Procedure Participants were recruited from the student population (undergraduate and postgraduate) of a university. The method entailed only one participant completing the study at any time. All participants were informed that the study was concerned with examining the effects of mood on performance in an aviation setting. They were briefed both verbally and through the information sheet that they would be asked to participate in an unscheduled evacuation of an Ó 2016 Hogrefe Publishing

aircraft cabin. However, they were not informed when this event would occur.

Each participant was randomly assigned to one of two groups (unpleasant or pleasant image group) and asked to read an information sheet and sign a consent form. Participants were then asked to complete the 37-item POMS-SF questionnaire. Following this, and depending on which group they were randomly assigned to, each participant was provided with either three unpleasant or three pleasant photographs and asked to concentrate on each photograph and rate them by completing the photograph rating form.

After they had completed the photograph rating form, the simulated flight commenced by directing the participant to a nominated seat, asking them to read the flight status information sheet (informed participant that they were about to take a flight on an aircraft departing from Sydney, Australia, for Melbourne, Australia, with a planned track over water), and having them watch the pre-take-off safety briefing video. The safety briefing video reminded the participant/passenger about the procedures regarding an overwater emergency, the location of the life jackets, and how to use the life jacket in case of an emergency. All participants were provided carry-on luggage, in the form of a backpack. The preflight safety video mentioned putting all personal items either in the overhead compartment or below the seat in front. As there was no overhead storage in the mock aircraft cabin, participants placed their belongings under the seat in front.

The facilitator moved to his seat and waited for 30 s, simulating the take-off roll. After take-off, the facilitator stood up and started to yell, “Ladies and gentlemen, we have an emergency. We have ditched, we have to evacuate the Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

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aircraft immediately,” and subsequently shouted, “Evacuate, evacuate, evacuate, high heels off!” three times.

The correct response for the candidate should have been: pull the life jacket out from the seat and place it on, buckle the straps according to the pre-take-off safety video, and approach the nearest exit. The entire exercise from the beginning of take-off until approaching the exit was recorded to determine the exact time taken and the number of errors committed during the procedure. Following the unscheduled evacuation, participants were required to complete a second POMS-SF questionnaire. Finally, the participants were thanked for their contribution and presented each with a $10 bookshop gift voucher. The total time each participant took to complete the experiment was approximately 30 min.

Results Since the main aim of the present research was to examine the effect of mood on performance in nonnormal situations, it was important to establish that the random allocation of participants to each group was successful, as well as the effectiveness of the mood manipulators. As a result, a mixed repeated measures ANOVA was conducted with Evacuation (before vs. after) as the repeated measures factor and Mood (negative vs. positive) as the between-groups factor. Consistent with Stage 1, TMD score featured as the dependent variable. With the ANOVA test assumptions satisfactory, the results revealed a main effect for Evacuation, F(1, 43) = 43.29, p < .001, an interaction between Evacuation and Mood, F(1, 43) = 22.64, p < .001, as well as a between-groups effect for Mood, F(1, 43) = 15.64, p < .001.

The main effect (repeated measures) for evacuation indicates that mood changed as a result of the mood manipulation (TMD prior = 25.26 [SD = 2.53] and TMD post = 28.13 [SD = 4.51]; lower scores of TMD equals more positive mood), while the between-groups main effect relating to Mood indicates that overall the negative mood group obtained a higher TMD score compared with the positive mood group. It should be noted that a lower TMD score equals a more positive mood. In terms of the Evacuation Mood interaction, a series of simple effects analyses were conducted in order to determine where the significance lay.

As can be seen in Figure 2, TMD scores prior to the mood manipulation in the negative mood group (25.86, SD = 2.74) were similar to the TMD score of the participants in the positive mood group (24.63, SD = 2.18), as determined by an independent samples t test with α adjusted to .025 (Bonferroni adjustment .05/2) to control for the repeated use of the dependent variable; t (43) = 1.66, p = 052. This result suggests that the random allocation of participants Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

32 31

Negative Positive

30 29 28 27 26 25 24 TMD Before

TMD After

Figure 2. Participants’ TMD score distributed across mood group, before and after the mood manipulation. TMD = Total Mood Disturbance.

to each group was successful. By contrast, a statistically significant difference was present between the TMD scores after mood manipulation between the negative mood group (30.73, SD = 4.27) and the positive mood group (25.41, SD = 2.89), t (43) = 4.97, p < .001. This result suggests that the mood manipulation was effective wherein participants in the negative mood group had a higher TMD score than participants in the positive mood group.

No statistically significant difference was evident between TMD scores for the positive mood group before (24.63, SD = 2.18) and after the mood manipulator (25.41, SD = 2.89) as determined by a dependent samples t test with α set at .025; t (22) = 2.19, p = .04. However, a statistically significant difference was evident between TMD scores for the negative mood group before (25.86, SD = 2.74) and after the mood manipulator (30.73, SD = 4.27) as determined by a dependent samples t test with α set at .025; t (22) = 6.35, p < .001. This result indicates that the mood manipulation was most effective in adversely affecting participants in the negative mood group, and importantly it was in the intended direction (adversely affected mood).

Table 1 provides a detailed breakdown for each factor comprising the POMS-SF scale, including the TMD score.

As can be seen from this table, mood scores increased on each of the six factors after mood manipulation for participants in the negative mood group. By contrast, scores on four of the six factors largely remained unchanged for participants in the positive mood group, after mood manipulation. The two factors that changed, namely, Tension and Confusion, increased. Given the nature of the experiment, that is, completing an unscheduled evacuation of an aircraft (nonnormal exercise) following the manipulation of mood, these results are hardly surprising. What remains unknown, however, is the extent to which these changes can be Ó 2016 Hogrefe Publishing

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

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Table 1. Mood state before and after exposure to mood manipulation, including standard deviation and effect size for each experimental group Positive

Negative

Mood state

Before (SD)

After (SD)

Effect Size

Before (SD)

After (SD)

Effect Size

Depression

0.27 (0.50)

0.32 (0.61)

0.09

0.32 (0.43)

1.10 (0.96)

1.13

Vigor

1.52 (0.97)

1.52 (0.99)

0.00

1.36 (0.82)

1.37 (0.75)

0.01

Anger

0.23 (0.42)

0.24 (0.49)

0.02

0.29 (0.40)

1.12 (0.99)

1.17

Tension

0.46 (0.46)

0.97 (0.78)

0.82

1.03 (0.88)

1.73 (0.96)

0.87

Confusion

0.41 (0.40)

0.71 (0.66)

0.57

0.81 (0.67)

2.53 (1.18)

1.86

Fatigue

0.77 (0.73)

0.69 (0.72)

0.11

0.86 (0.83)

1.62 (1.09)

0.79

24.63 (2.18)

25.41 (2.89)

0.31

25.86 (2.74)

30.73 (4.27)

1.39

TMD

Notes. Total Mood Disturbance (TMD) score calculated using the formula Depression–Dejection + Tension–Anxiety + Anger–Hostility + Fatigue–Inertia + Confusion–Bewilderment + (24 – Vigor–Activity). Effect size calculated using Cohen’s d.

Figure 3. Error checklist employed to assess participant performance during simulated evacuation exercise Did the participant: 1.

Forget the life jacket and was to evacuate the aircraft without it?

Yes h

No h

2.

Know the location of the life jacket – i.e., able to find it?

Yes h

No h

3.

Pull out the life jacket?

Yes h

No h

4.

Put on the life jacket correctly – placing the rounded side of the jacket behind the head and the squared side in front?

Yes h

No h

5.

Tie the strap properly – putting the strap around the waist circumference?

Yes h

No h

6.

Attempt to inflate the life jacket prior to exiting the aircraft?

Yes h

No h

7.

Attempt to carry any personal items during the evacuation – i.e., bags, laptop computer, etc.?

Yes h

No h

8.

Choose the nearest emergency exit?

Yes h

No h

9.

Have any mishap during evacuation – i.e., tripped, slipped, lost balance?

Yes h

No h

10.

Ask any questions regarding Steps 1–8 after commencing evacuation – “Evacuate, evacuate, evacuate”?

Yes h

No h

Note. The entire process from the commencement of evacuation until completion took ___________seconds.

attributed to the unscheduled evacuation or the mood manipulation, a point that is elaborated on in the discussion section.

Having established that the mood manipulation was effective, the next step of the analysis involved examining performance between the two experimental groups. Performance could be measured in two ways, the first involving total time to complete the evacuation exercise and the second involving number of errors committed during the evacuation. In terms of the latter, an error checklist was created through a task decomposition method (Stanton, Salmon, Walker, Baber, & Jenkins, 2005), based on the information contained in the preflight safety briefing. It should be recalled that the purpose of the preflight safety briefing is to inform/educate passengers on how to behave in the unlikely event of an emergency.

In doing so, it allows the flight attendants to perform other critical roles to facilitate in the safety of all passengers. Some of these roles include: identify which emergency door/s have to remain closed (owing to the wind or fire), deploy and secure slides, inflate the life raft (during ditching), facilitate in the quick disembarkation (less than 90 s), and assist people who are not mobile or carry small children. In total, a maximum of 10 possible errors were identified.

Figure 3 outlines these errors and as can be seen from this table, the errors largely focus on identifying and using the life jacket appropriately. A behavior that was counter to Ó 2016 Hogrefe Publishing

exiting the aircraft in the shortest possible time was also encountered as an error. Therefore, behaviors such as failing to choose the closest exit, experiencing a mishap while evacuating (i.e., tripping), or asking for instructions were also classified as an error. As can be seen in Figure 4, only three participants (all from the negative mood manipulation group) committed an error classified under point nine of the error checklist (e.g., mishap during evacuation, specifically all lost balance), while two participants in the negative mood manipulation group committed an error each classified under point ten, namely, asking which exit they should evacuate from (n = 1 participant) and how they should fit on their life jacket (n = 1 participant). However, a total of six participants (three from each group) failed to remove their life jacket from beneath the seat, and hence approached the exit without a life jacket. Since the simulated emergency involved ditching over water, the participants who failed to remove their life jacket from their seat were reminded of this and subsequently went back to collect their jacket – a procedure that is common practice on commercial aircraft where no other flotation devices (i.e., life raft or floating emergency slide) are on board (as advised by a cabin safety manager with 30 years’ flying experience).

With the assumptions of normality and homogeneity of variance met, the results of the independent t test indicated Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

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Number of Errors Committed

14

Figure 4. Total number of errors committed per mood manipulation group distributed across error category as per error checklist.

12 10 8 6 4 2 0 1

2

3

4

5

6

7

8

9

10

Error Category (as per Error Checklist) Negative Mood Manipulation Group

Positive Mood Manipulation Group

a statistically significant difference between participants in the positive mood manipulation group and participants in the negative mood manipulation group in terms of time taken to complete the emergency evacuation, t (43) = 3.64, p = .0005. The total time taken to complete the evacuation for participants in the positive mood group was 37.50 s (SD = 19.63 s), while participants in the negative mood group took almost twice as long (66.09 s, SD = 31.41 s).

The second analysis compared performance based on the number of errors between the two experimental groups.

Using the t test for unequal variances because of violation of the assumption of homogeneity of variance, a statistically significant difference was found between the positive mood manipulation group and the negative mood manipulation group, in terms of number of errors committed, t (43) = 5.62, p < .001. Participants in the positive mood manipulation group committed on average less than one error per exercise (.36 errors, SD = 0.66) while participants in the negative mood manipulation group committed approximately seven times as many errors during the same exercise (2.57 errors, SD = 1.75).

In order to examine whether prior exposure to preflight safety briefings affected participants’ performance in terms of number of errors or egress time (dependent variables), a series of correlational analyses were conducted between these two variables and the number of flights participants had flown in the previous 12 months and 5 years. The results of the correlational analysis failed to reveal any relationships, largest r, r(31) = .224, p = .226.

Discussion The effect of mood on performance in normal situations/ conditions has been widely examined and the results commonly reveal a mood-congruence relationship. For example, salespeople with upbeat demeanor have been found Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

to encourage purchasing behavior (Pugh, 2001) while lawyers using an aggressive and angry tone have been found to obtain compliance from their rivals (Pierce, 1995). A common theme throughout much of the research in this area is the focus on behavior in normal everyday situations. Hence, what remained unknown is whether the mood performance relationship extended to situations that are rarely experienced, such as that tested in the present research, namely, an unscheduled evacuation of an aircraft.

The results from the present research suggest that the effects of mood on performance are similar irrespective of the context or situation in which performance is examined.

Moreover, performance in a nonnormal situation such as an unscheduled evacuation of an aircraft revealed that individuals in the positive mood manipulation group outperformed individuals in the negative mood manipulation group, in terms of the time to complete the evacuation. Similarly, participants in the positive mood manipulation group committed notably fewer errors than participants in the negative mood manipulation group during the same evacuation exercise. This effect appeared largely as a result of the negative mood manipulation, as no statistical differences were noted between before and after manipulation of mood in the positive mood manipulation group; however, mood did change in the desired direction (discussed further in the Application and Future Research section). These results suggest that mood is an imperative human attribute that can be used as a tool to improve performance and safety. While these results are not new – recall that Muir and colleagues were able to manipulate egress time (negatively affect egress time) in an emergency evacuation through the use of financial incentives – they do offer airlines some insight into simple and effective methods to positively influence passengers’ behavior in the unlikely event of an emergency.

Specifically, the results illustrate that a simple exercise such as the presentation of three positive or negative images followed by a short reflection on those images was enough to manipulate mood in the desired direction. Ó 2016 Hogrefe Publishing

M. Tehrani & Brett R. C. Molesworth, Mood and Performance

This positive result not only supports previous research in this area (Richards & Whittaker, 1990), it also highlights that the benefits of mood manipulation are not limited to everyday situations but also to situations that are rarely experienced, and for some unimaginable.

As briefly stated in the results section, the tension– anxiety and the confusion–bewilderment mood states of the participants in the positive mood group prior to manipulation were unexpected. Recall that participants were randomly allocated to each group (positive or negative mood group). Such a process should guard against this, which may correct itself by simply increasing the sample size.

Despite this unexpected result, the results do, however, trend in the predicted direction given the experimental manipulation. As would be expected, experiencing an unscheduled evacuation of an aircraft, albeit a mock situation, produces increased levels of tension. However, being in a positive mood reduces the extent to which this spikes.

Application and Future Research From an applied perspective, these results have important implications. Moreover, consider the situation this study was attempting to replicate, namely, an emergency ditching shortly after take-off and not long following the preflight safety brief; a situation similar to that recently experienced

by US Airways flight 1549 that made an emergency landing in the Hudson River (National Transportation Safety Board, 2010). Hence, based on the results of the present research it would seem that the delivery of this brief is an opportunity to not only educate passengers about how to perform in an emergency, but also to positively manipulate their mood and ultimately their performance if something untoward were to occur. However, this remains untested and is an area for future research.

Future researchers could also examine other simple and nonevasive methods of manipulating mood in such an environment. For example, it is well known that certain colors, namely, cool colors such as yellow have a calming effect (Samuels, 1999; Stone & English, 1998). Similarly, too little light or too much light can also adversely affect mood (Kuller, Ballal, Laike, Mikellides, & Tonello, 2006) as well as certain music (Moon et al., 2014; Zwaag et al., 2012).

Hence, whether strategically using colors, light, or music in certain areas of an aircraft can manipulate mood and ultimately performance in both normal and nonnormal situations remains unknown.

The present research also largely employed young university students (average age 21.90 in Stage 2) with the majority of the students being male. Despite some studies conducted by scholars such as Phillips, Smith, and Gilhooly (2002), illustrating that older adults have greater executive

Ó 2016 Hogrefe Publishing

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function (i.e., judgment, control, and planning) impairment than young adults when their mood is manipulated, it remains unknown whether this effect is present during an emergency situation as tested in the present research, and hence is another area for future research.

It also needs to be acknowledged that the experimental sequence likely impacted participants’ mood in Stage 2 of the present research. Recall Stage 1 tested the efficacy of the mood manipulators in isolation, which proved effective in the manipulating participants’ mood in the desired direction. However, following the unscheduled evacuation Stage 2, participants’ TMD score in the positive mood manipulation group did not vary statistically from their pre-test score. In addition, their subscores on the Tension and Confusion factors notably increased (in addition, negative mood manipulation amplified participants’ mood in the negative mood manipulation group), which was also unexpected. Hence, it is conceivable that the unscheduled evacuation adversely affected participants’ mood. Therefore, future research should examine alternate nonevasive measures for mood to appropriately document this impact.

However, at present there are no known tests that fit this description; nonetheless when these become available, this is an area for future research.

Future research could also be extended to examine the validity of the preflight safety briefing. While the briefing was central to the present research, whether its effectiveness in conveying safety-critical information remains strong or there are better alternatives remains unknown.

Limitations While the results of the present study reflect favorably on the relationship between positive mood and performance in a nonnormal situation, they are not without their limitations. The most obvious being that the research was conducted in a mock setting as opposed to a real aircraft cabin. For ethical reasons the latter could not be employed; however, whether performance would have differed if the study was conducted in an aircraft remains unknown.

One nonevasive method that may assist, in part validating these results, would involve the use of simulation models that have been developed based on actual aircraft evacuation data (see Galea, 2006; Miyoshi, Nakayasu, Ueno, & Paterson, 2011).

Attention should also be drawn to the sensitivity of the mood scale employed in the present research. While the POMS questionnaire has been used extensively (Shacham, 1983; Terry et al., 2003), it must be acknowledged that it is a self-reported scale, and as with all self-reported scales, responses can be manipulated. Although there is no evidence from the present research to suggest participants

Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

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manipulated their responses on this scale, it does need to be noted that this could have occurred and future research should consider employing alternate mood measures to guard against this.

Conclusion In conclusion, this study examined the relationship between mood and performance in aviation during a nonnormal situation (i.e., emergency). This research followed Richards and Whittaker’s (1990) mood manipulation methodology by examining participants’ mood before and after a cabin emergency egress. The results reflect that in normal situations positive mood improved performance in terms of error reduction and time to complete task, while negative mood reduced performance. For the aviation industry and other safety-critical industries, the results suggest organizations and personnel within these industries should aim to create an environment that encourages a positive mood state, thereby facilitating in performance if something untoward were to occur.

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Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. doi: 10.1037/0022-3514.54.6.1063 Zohar, D. (1999). When things go wrong: The effect of daily work hassles on effort, exertion and negative mood. Journal of Occupational and Organizational Psychology, 72, 265–283. doi: 10.1348/096317999166671 Zwaag, M., Dijksterhuis, C., de Waard, D., Mulder, B., Westerink, J., & Brookhuis, K. (2012). The influence of music on mood and performance while driving. Ergonomics, 55(1), 12–22. doi: 10.1080/00140139.2011.638403 Received January 17, 2014 Revision received August 12, 2015 Accepted November 24, 2015 Published online May 3, 2016 Brett R. C. Molesworth UNSW School of Aviation, Room 205c Old Main Building Sydney NSW 2052 Australia Tel. +61 2 9385 6757 Fax +61 2 9385 6637 E-mail b.molesworth@unsw.edu.au

Morteza Tehrani (MSc), a Chief Civilian Aviation Instructor with the Royal Australian Air Force, is completing his postgraduate studies in Human Factors at UNSW Aviation, Sydney, Australia. His research interests are in aviation safety and human factors focusing on nonnormal situations. Previous appointments include: military pilot and senior instructor Human Factors at QANTAS.

Brett Molesworth (PhD) is a senior lecturer in the School of Aviation at UNSW, Sydney, Australia. He holds a commercial pilot license (CPL) with an advanced aerobatics rating and is also a registered psychologist. His research interests focus on aircrew as well as cabin crew behavior in both normal and abnormal situations.

Aviation Psychology and Applied Human Factors (2016), 6(1), 1–11

Original Article

Safety Culture, Resilient Behavior, and Stress in Air Traffic Management Michaela Schwarz,1 K. Wolfgang Kallus,2 and Kerstin Gaisbachgrabner2 1

Austro Control GmbH, Vienna, Austria

2

Department of Psychology, University of Graz, Austria

Abstract: In today’s rapidly changing air traffic management (ATM) environment, safety culture and organizational resilience are seen as key enablers for effective safety management. Under normal conditions a positive safety culture is known to be reflected in proactive behavior and to serve as indirect indicator of organizational resilience. But how are safety culture development and resilient behavior affected by psychological stress? This study aims at relating safety culture to resilient behavior and psychological stress of 282 air traffic controllers, air traffic safety electronics personnel, and meteorologists. Results demonstrate that safety culture across different occupational groups is difficult to assess, but that facets of safety culture can be meaningfully related to resilient behavior. Structural equation modeling indicates that psychological stress has a positive effect on resilient behavior and a negative effect on safety culture development. Findings are discussed in the safety management context of the ATM system including air traffic control, engineering, and meteorological services. Finally, conclusions are drawn with a view on providing an initial position for future studies.

Keywords: air traffic management, behavior, organizational resilience, safety culture, stress

The European air traffic forecast predicts air traffic to be doubled by 2035 (EUROCONTROL, 2013a) requiring air navigation services (ANS) to adapt to a greater demand and increased safety and efficiency targets. In the safety management context ANS services include air traffic control, engineering (communication, navigation, and surveillance), and meteorological services (European Commission, 2011).

Over the past decades the operational environment has evolved from procedural air traffic control, manual repair and maintenance of technical systems, and human weather observations to highly automated electronic flight management, technical control, and monitoring systems as well as automatic weather forecasts. As a consequence, air traffic management (ATM) operators (air traffic controllers, air traffic safety electronics personnel, and meteorologists) heavily rely on the support of electronic data and automated workflows to ensure continuously safe and efficient operations.

Changing Safety Management Perspectives In response to the changing environment, the complexity of safety management systems (SMS)1 has also increased.

ANS system2 functions are no longer bimodal (working or not working) and human performance is considered a key success factor to manage safety (EUROCONTROL, 2013b, 2014). While traditional safety management approaches define safety as “freedom from unacceptable risk” (safety-I), the complementary view (safety-II) relates to safety as “the ability to succeed under varying conditions” (Hollnagel, 2011, p. xxxix; Hollnagel, 2014). SafetyI assumes that “safety-critical systems need protection from unreliable humans – by more procedures, tighter monitoring and automation” (Dekker, Hollnagel, Woods, & Cook, 2008, p. 2). Safety-II considers human skills and ability as potential resources to obtain more efficiency and safety by managing both normal and unexpected situations as well as normal and degraded modes of systems. Regulators have picked up the notion of safety-II mandating air navigation service providers (ANSPs) to implement a proactive safety management approach (EUROCONTROL, 2009; European Commission, 2011; International Civil Aviation Organization [ICAO], 2013a) and regularly assess their safety culture (European Commission, 2011). “This new approach is based on routine collection and analysis of data using proactive [actively seeking out hazards] as well as

A safety management system (SMS) is “a systematic approach to managing safety, including the necessary organizational structures, accountabilities, policies and procedures.” (ICAO, 2013a, p. xii).

2 The term system here goes beyond the technical hard- and software referring to the ANS organization as “an interconnected set of elements [e.g. mission, vision, policies and procedures and processes] that is coherently organized in a way that achieves something” (e.g. safety and efficiency) (Meadows, 2008, p. 11).

1

Aviation Psychology and Applied Human Factors (2016), 6(1), 12–23 DOI: 10.1027/2192-0923/a000091

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reactive [analysis of past outcomes/events] methodologies to monitor known safety risks and detect emerging safety issues” (ICAO, 2013a, p. 2-2). Supporting the new safety management approach, this paper focuses on assessing individual behavior reflecting organizational resilience.

Resilience Engineering and Resilient Behavior The first question in this paper is related to how organizational resilience can be measured on an individual level based on a newly developed Inventory to Assess Behavior Toward Organizational Resilience in Air Traffic Management (I-BORA; Heese, Kallus, & Kolodej, 2013). System theory experts (Hollnagel, 2010, 2014; Hollnagel, Pariès, Woods, & Wreathall, 2011; Rasmussen, 1997) propose resilience engineering as a new approach to safety management that “combines previous results from high reliability organizations, expert cognitive systems, and adaptive systems theory” (Woods, 2006b, p. 2239). According to Hollnagel (2014) resilience engineering is considered as a “scientific discipline that focuses on developing the principles and practices that are necessary to enable systems to function in a resilient manner” (p. 183). Resilience is hereby defined “as the intrinsic ability of a system to adjust its functioning before, during or after changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions” (Hollnagel, 2011, prologue). An alternative way of achieving system resilience is exploring the concept of resilience among individuals in their workplace within an organization. The idea is borrowed from the fundamental principle of self-similarity in fractal geometry (Mandelbrot, 1977) assuming that components (of a whole) appear structurally similar to the whole across several magnifications.

In the organizational context resilience means an organization’s “ability to bounce back from untoward events” (Weick & Sutcliffe, 2007, p. 71). Weick (1988, 1993) first applied lessons learnt from child psychology (Block, 1950; Garmezy, Masten, & Tellegen, 1984; Letzring, Block, & Funder, 2005) to the organizational context. He analyzed the death of 13 firefighters in the Mann Gulch bush fire disaster in 1949 in the US to understand why organizations unravel. Asking himself what could make organizations more resilient, he suggested four sources of resilience: 1. Improvisation and bricolage; 2. Virtual role systems; 3. Attitude of wisdom; and 4. Respectful interaction.

Building on Weick’s concepts, Mallak (1998) studied resilient behavior in the health-care context. He identified six Ó 2016 Hogrefe Publishing

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factors to be considered in research targeted at finding dimensions of resilient organizations and behaviors of resilient individuals. These were: 1. Goal-directed solution seeking; 2. Avoidance; 3. Critical understanding; 4. Role dependence; 5. Source reliance; and 6. Resource access.

Somers (2009, p. 12) picked up Mallak’s (1998) elements focusing on “organisational structures and processes that build organisational resilience potential” to develop the Organizational Resilience Potential Scale (ORPS). Following Weick (1993) and Mallak (1998, 2006) several authors have looked into measuring resilient behavior in highreliability organizations (Kolodej, Reiter, & Kallus, 2013; Reiter, 2011; Somers, 2009; Välingkangas, 2010). However, existing instruments and associated components were not found to be reliable for the ATM environment. Therefore, there was a need to develop a new instrument, the I-BORA.

Resilient Behavior and Safety Culture The second question is concerned with the relation of resilient behavior and safety culture. Akselsson, Koorneef, Stewart, and Ward (2009) investigated resilience aspects of safety culture based on the dynamic safety model by Rasmussen (1997) postulating that “safety culture can be seen as counterforce against migration towards and beyond the limit of safe operation” (p. 5). The term safety culture goes back to the investigation of the 1986 Chernobyl disaster (International Atomic Energy Agency [IAEA], 1986) and was defined by the Civil Air Navigation Service Organisation (CANSO, 2009) as: The enduring value, priority and commitment placed on safety by every person and every group at every level of the organisation. Safety culture reflects the individual, group and organisational attitudes, norms and behaviours related to the safety provision of air navigations services. (p. 11) In his model, Rasmussen (1997) referred to certain boundaries (economic failure, unacceptable workload, functionally acceptable performance) and a “margin for safety” (safety buffer) in which organizations or systems operate.

New equipment and changed working procedures, for example, may affect human behavior and stretch the system toward its boundaries (design envelope). Akselsson et al. (2009, p. 5) believed that a robust safety culture may stabilize the system and ensure safety and efficiency.

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Woods (2006a) further postulated that assessing an organization’s safety culture may serve as an indirect indicator for organizational resilience and can be considered as an anchor point to engineer resilience into a system. The second question in this paper therefore seeks to answer how resilient behavior is related to safety culture.

Safety culture can be assessed based on different constituent components (Cooper, 2000; Heese, 2012; Mearns, Kirwan, & Kennedy, 2009; Mearns et al., 2013; Reason, 1997; Shorrock, Mearns, Laing, & Kirwan, 2011; Wiegmann, Zhang, von Thaden, Sharma, & Mitchell, 2002) or different safety culture maturityÒ3 levels (Fleming, 2000; Hudson, 2003). Safety culture in ANSPs is commonly assessed on an individual level through “attitude and opinion questionnaires, interviews, focus groups, field observations, artefactand case analysis as well as dialog” (Patankar, Brown, Sabin, & Bigda-Peyton, 2012, p. 27). For more details on safety culture development and assessment, the interested reader is referred to Heese (2012) and Schwarz and Kallus (2015).

Resilient Behavior, Safety Culture, and Stress The third question investigates the relationship between resilient behavior, safety culture, and stress. The idea was triggered by Fogarty (2005), who found that psychological stress mediated the impact of safety climate4 on errors in a sample of 150 helicopter maintenance engineers in the Australian Army. Wreathall (2006) further defined resilience as “the ability of an organization (system) to keep, or recover quickly to, a stable state, allowing it to continue operation during and after a major mishap, or in the presence of continuous significant stresses” (p. 275). Based on this definition and the reviewed literature, resilient behavior is assumed to be especially relevant under significantly stressful conditions.

Psychological stress is defined as “the total assessable influence impinging upon a human being from external sources and affecting it mentally,” causing psychological strain, which is defined as “the immediate effect of mental stress on the individual (not the long-term effect) depending on his/her individual habitual and actual preconditions, including individual coping styles” (International Organization for Standardization [ISO 10075-1, 1991, p. 4). Kallus (1995) highlights the importance of recovery as a subordinate concept to psychological stress. Recovery is defined “an inter- and intraindividual multilevel (e.g., psychological, 3 4

M. Schwarz et al., Safety Culture in ATM

Figure 1. Stress Process with Recovery Loop (Kallus & Kellmann, 2015, p. 28).

physiological, social) process in time for the reestablishment of personal resources and their full functional capacity” (Kallus & Kellmann, 2015, p. 34). The relationship between psychological stress, recovery, and resources is demonstrated in the so-called recovery–stress balance model (Hacker & Richter, 1998; Hacker & Sachse, 2014; Kallus, 1995, 2015; Kallus & Uhlig, 2001). According to this model, stress should always be counterbalanced with recovery, because “an accumulation of stress with insufficient opportunity for recovery leads to a compromised psychophysiological state” (Straussberger, 2006, p. 68). Figure 1 explains the relationship between psychological stress and recovery.

Following the regulatory mandate regarding safety management system requirements and the new view on safety (succeeding under varying conditions), this paper seeks to investigate the relationship between safety culture development and resilient behavior and how they are affected by psychological stress (as one result of varying conditions).

The following three research questions were formulated to provide initial directions for future studies in this area.

Research Questions Within the context of the existing literature, this paper aims to answer the following questions: Research Question 1 (RQ1): Can resilient behavior be reliably measured in ATM operators?

Research Question 2 (RQ2): How is resilient behavior related to safety culture?

Research Question 3 (RQ3): How are resilient behavior and safety culture affected by stress and recovery?

The term Safety Culture Maturity is a registered trademark of The Keil Centre Limited.

The term safety culture is often used interchangeably with safety climate. According to Mearns and Flin (1999) it is therefore important to distinguish between safety culture as an “enduring trait” related to people’s behavior and safety climate as “a snapshot of the current state of safety” related to people’s perceptions of safety (p. 5).

Aviation Psychology and Applied Human Factors (2016), 6(1), 12–23

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Method The method is based on a questionnaire study conducted within a European ANSP in March/April 2012.

Dependent and Independent Variables Research Questions 1 (measurement of resilient behavior in ATM) and 2 (correlations between resilient behavior and safety culture) aim at exploring the strength of a possible association. Research Question 3 looks into a possible relationship between psychological stress (independent variable) and resilient behavior and safety culture (dependent variables).

Sample and Subsamples Participants were 282 out of 500 licensed ATM staff (55% en-route and terminal air traffic controllers, 26.6% air traffic safety electronics personnel, and 16% meteorologists; 2.4% unknown5) across 10 different sites equaling a representative response rate of 56.4%. The majority of participants were male and within the 26–35-year and 36–45-year age group. Participants reported an average length of 15–29 years of full-time service. The overall sample was divided into three subsamples for further analysis: n = 155/336 (46% response rate) controllers, n = 75/96 (78% response rate) air traffic safety electronics personnel, and n = 45/68 (66% response rate) meteorologists.

All three occupational groups provide operational safety through air traffic control, alerting and information services, incident management, maintenance and repair of technical communication, navigation and surveillance equipment, as well as weather observation, forecast, and advising services. The three occupational groups are comparable based on their hierarchical organization (head of department, regional/unit managers, supervisors/team coordinators, and operational teams), their working environment (shift rotation, operations room, technical interface) and their organizational values (safety before efficiency, capacity/punctuality, and sustainability). All three occupational groups cooperate under one safety policy and safety management system founded under one organization-wide safety culture.

Measures The questionnaire package consisted of three different questionnaires assessing resilient behavior, safety culture 5

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development, and psychological stress as outlined in the following.

Inventory to Assess Behavior Toward Organizational Resilience in ATM (I-BORA) The first choice for assessing behavior toward organizational resilience was the Resilience Scales developed by Mallak (2006) and validated in the health-care context.

The Resilience Instrument was tested in a pilot study (Schwarz, 2015) but did not produce sufficient reliability values in the ATM sample. Therefore, the authors decided to develop a new instrument that was based on an early version of the already validated German Inventory of Resilient Behavior at the Place of Work (REVERA; Kolodej, Reiter, & Kallus, 2013; Reiter, 2011). The newly developed I-BORA was originally based on 20 questions from an initial version (96 items on 12 subscales) of the REVERA (Kolodej, Reiter, & Kallus, 2013; Reiter, 2011). The questions were selected based on their subscales: 1. Goal-Directed Solutions (= solving problems proactively and continuously seeking improvements in the job); 2. Bricolage and Improvisation (= managing unexpected situations through intuition and creative solutions); 3. Role System (= temporarily taking on roles and responsibilities); 4. Resources (= the ability to access knowledge, information, personnel/contacts); and 5. Avoidance/Skepticism (= evading risky tasks/ situations).

These five subscales were chosen because they overlapped content wise with the already validated Organisational Resilience Potential Scale, ORPS (Mallak, 1998, 2006; Somers, 2009). The items were translated into English and were related to a 7-point frequency answer scale from 0 (= never) to 6 (= always) asking for behavior within the past 7 days and nights. The frequency scale was chosen because it reflects actual behavior and experience related to a certain timeframe. Research shows that a timeframe referring to the past 7 days/nights is the best predictor of behavior within the next 7 days (Kallus, 2010). An example item of I-BORA is: “In the past seven days and nights I achieved a good result by improvising” (component: improvisation).

The full item set of I-BORA and an initial check on reliability and results from principal component analysis can be found in the publication by Heese, Kallus, and Kolodej (2013). There were 12 items on four components confirmed in the initial analysis and reliability check: 1. Goal-directed solutions (α = 0.79); 2. Flexibility (α = 0.63);

Seven participants did not provide information regarding their occupational group and associated data were excluded from analysis at group level (missing data).

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3. Improvisation (α = 0.67); and 4. Availability of resources (α = 0.71).

Two out of four components achieved sufficient Cronbach’s α values of > .70. It should be mentioned here that the results from the 2013 study were reviewed by Schwarz, Kallus, and Jiménez (2015). Based on an additional factor analysis restricting the number of factors to be extracted to four instead of six, the item structure was changed. In the 2015 study, Goal-Directed Solutions (i3, i4, i5, i8, i9) and Flexibility (i1, i2) remained unchanged. However, Improvisation consisted of two items (i12, i13), which in 2015 changed to three items (i6, i12, i13), and Availability of Resources comprised two items (i19, i20), which was increased to four items (i10, i17, i19, i20). The final items set consisted of 14 instead of 12 items. Six items (i7, i11, i14-i16, and i18) were excluded owing to insufficient reliabilities. In addition, Availability of Resources was renamed as Access to Resources based on existing literature.

Safety Culture Development Questionnaire (SCDQ) Safety culture was assessed using the Safety Culture Development Questionnaire6 (SCDQ-28; Heese, Kallus, Artner, & Marek, 2011; Schwarz & Kallus, 2015) based on a safety culture question database developed by the CANSO (2009).

Another option for assessing safety culture would have been the EUROCONTROL Safety Culture Measurement Toolkit (SCMT; Gordon, Kirwan, Mearns, Kennedy, & Jensen, 2007; Mearns et al., 2009; Mearns et al., 2013; Shorrock et al., 2011). The SCDQ was the preferred choice because the SCMT was restricted for use by EUROCONTROL member states and the underlying research and findings were targeted at members of the broader CANSO region.

The final version of the SCDQ consists of 28 statements regarding people’s attitudes and opinions toward operational safety on a 4-point Likert scale ranging from strongly disagree and disagree to agree and strongly agree and a not applicable category. The 28 statements are allocated to five facets of safety culture including: 1. Informed culture (α = 0.82; = sharing safety information/safety communication); 2. Reporting and learning culture (α = 0.72; = raising safety concerns/reporting mistakes so the organization can learn from them); 3. Just culture (α = 0.39; = fair treatment of employees); 4. Flexible culture (α = 0.84; = understanding risks and adapting to change); and 5. Management’s safety attitudes (α = .84; = management commitment to safety).

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For example: “My immediate manager listens to our views on safety” (component: management’s safety attitudes).

The SCDQ validation followed a standard questionnaire development procedure (Kallus, 2010) and was based on the same dataset underlying this paper (N = 282). All components except just culture achieved sufficient Cronbach’s α values of > 0.70 for internal consistency (Schwarz & Kallus, 2015).

Recovery-Stress Questionnaire (RESTQ) Psychological stress and recovery was measured using 24 statements of the English Recovery-Stress Questionnaire for Athletes (RESTQ-24A – basic module, short version) referring to the previous 7 days and nights on a 7-point frequency scale (Kallus, 1995; Kallus & Kellmann, 2015; Kellmann & Kallus, 2001). The 24 items relate to two main dimensions covering 12 scales associated with Overall Stress and Overall Recovery. Overall Stress consists of two subdimensions: Social–Emotional Stress (containing 1 = General Stress, 2 = Emotional Stress, and 3 = Social Stress) and Performance-Related Stress (4 = Conflicts/ Pressure, 5 = Fatigue, 6 = Lack of Energy, 7 = Somatic Complaints).

Overall Recovery is the second dimension (8 = Success, 9 = Social Recovery, 10 = Somatic Relaxation, 11 = General Well-Being, 12 = Sleep Quality). For example, “In the past seven days and nights I felt under pressure” (subdimension: performance-related stress).

The basic module of the RESTQ in German was successfully validated in various team and individual sports across large German samples (Kallus & Kellmann, 2015). The reliability of the English RESTQ-24A (24/7) is reported based on a sample of 133 sport coaches with Cronbach’s α values between r = .86 and .93.

Procedure All three questionnaires were distributed to all ATM staff in paper–pencil format. Online distribution was not possible, because not all operational staff had access to a personal computer during shifts. Executive managers, unit chiefs/regional managers, and staff unions were included in the survey announcement and fully supported the initiative. Participants received an English version and a translated version for reference in their mother tongue (German). They were asked to complete the English version during paid working hours. No other incentives were received.

The Safety Culture Development Questionnaire (SCD-Q) is based on an initial version referring to a Safety Culture Maturity Questionnaire proposed by the Civil Air Navigation Organisation (CANSO). The name was changed, because the term safety culture maturityÒ is a trademark of the UK Keil Centre (Fleming, 2000).

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Table 1. Descriptives and correlation for components underlying behaviour towards organisational resilience Components

k

M

SD

1

2

3

4

1. Goal-directed Solutions

5

3.40

1.21

2. Flexibility

2

3.41

1.70

.452**

3. Improvisation

3

2.44

1.21

.440**

.309**

4. Access to Resources

4

4.56

0.97

.301**

.176**

.241**

1

Overall Score

14

3.45

0.76

.726**

.802**

.666**

.005

1 1 1

Notes. **Significant correlation at .01 level, *Significant correlation at the .05 level; listwise: N = 281; k = number of items.

Table 2. Cronbach’s Alpha values for internal consistency related to behaviour towards organisational resilience across three different sub samples k

α ALL N = 282

α Controllers n = 155

α Engineers n = 75

α Meteorologists n = 45

1. Goal-directed Solutions

5

.79

.73

.84

.86

2. Flexibility

2

.63

.68

.67

.50

3. Improvisation

3

.63

.51

.69

.78

4. Access to Resources

4

.70

.64

.54

.67

Components

Notes. Values that achieved a sufficient Cronbach’s Alpha of .70 for internal consistency (Tabachnick & Fidell, 2007) are marked in bold. Listwise: N = 280.

Data Analysis Data were transformed considering inverted answer formats and underwent missing values and outlier analysis.

Not applicable answers in the SCDQ were recoded as missing values. Missing values occurred in less than 16% of the cases. Therefore, missing values were generally excluded listwise (complete case analysis) unless otherwise stated.

Data underwent reliability and correlation analysis using IBM SPSS Statistics Version 21.0. Structural equation modeling (SEM) was calculated in IBM SPSS AMOS. For SEM, missing values were replaced using the expectationmaximization (EM) method in SPSS (Schafer, 1997).

Results

traffic safety electronics personnel, meteorologists) in comparison with the overall sample (N = 282). Values that achieved a sufficient Cronbach’s α of 0.70 for internal consistency (Tabachnick & Fidell, 2007) are marked in bold.

Research Question 1 related to whether Resilient Behavior can be assessed reliably across three different ATM staff groups was partly confirmed. Two out of four components (goal-directed solutions and access to resources) could be reliably assessed. Flexibility and improvisation were reduced to two and three questions and did not achieve a sufficient Cronbach’s α value of .70. It was attempted to merge flexibility and improvisation together, because scales with only two items are not acceptable. However, Cronbach’s α values for internal consistency remained unchanged. It was therefore decided to keep them separate subject to further validation in future studies.

Resilient Behavior Across Three Different ATM Staff Groups (RQ 1)

Resilient Behavior and Safety Culture (RQ 2)

Research Question 1 asked whether resilient behavior can be assessed reliably in ATM operators. To answer Research Question 1, reliability coefficients for all four dimensions of resilient behavior were calculated across all three subsamples and compared with the overall reliability scores. Descriptives and item correlations are reported in Table 1. Goal-directed solutions, flexibility, and improvisation demonstrated significantly positive correlations with each other. Access to resources correlated negatively with the other three components. The uncorrected correlations with the overall score show that all components except access to resources can be meaningfully summarized to resilient behavior.

Table 2 reports reliability coefficients (Cronbach’s α) for the three different occupational groups (controllers, air

Research Question 2 looked into the relationship between resilient behavior and safety culture. Pearson correlations between the four components describing behavior toward organizational resilience (I-BORA: 14 items) and the five safety culture (SCD-Q: 28 items) scales were calculated.

Results are presented in Table 3. Informed culture was significantly positively related to access to resources and negatively related to flexibility. A significant positive correlation was found between just culture and goal-directed solutions. Flexible culture and improvisation were negatively related. Informed culture and improvisation were marginally related ( p < .09). Results cannot be firmly interpreted, because just culture, flexibility, and improvisation did not reach Cronbach’s α value for internal consistency of 0.70.

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Table 3. Correlations between Behaviour towards Organisational Resilience and Safety Culture Development Scales Components

Informed Culture

Reporting/Learning Culture

Just Culture

Goal-directed solutions

.023

.117

t

Flexible Culture

Mgmt.’s Safety Attitudes

.185*

.008

.012

Flexibility

.121*

.060

.044

.047

.069

Improvisation

.106t

.092

.052

.125*

.086

Access to Resources

.333**

.059

.076

.069

.098

Notes. **Significant correlation at .01 level, *Significant correlation at the .05 level; t = significant trend at the .09 level; Listwise: N = 280.

Table 4. Pearson correlations between behaviour towards organisational resilience, safety culture and psychological stress/recovery (relevant correlations marked in grey) RESTQ-24A (Short Version) Overall Stress

I-BORA-14

SCDQ-28

Overall

k

Social-Emotional Stress

1

Goal-directed solutions

5

.184**

Performance-Related Stress .250**

Recovery .205**

2

Flexibility

2

.135*

.134*

.137*

3

Improvisation

3

.172**

.199**

.068

4

Access to Resources

4

.366**

.480**

.219**

1

Informed Culture

7

.218**

.281**

.135*

2

Reporting and Learning Culture

5

.138*

.094

.061

3

Just Culture

2

.161**

.101

.124*

4

Flexible Culture

8

.282**

.211**

.085

5

Management’s Safety Attitudes

6

.199**

.076

.016

Notes. Significant correlations at .01 level are marked in bold. **Significant correlation at .01 level, *significant correlation at the .05 level; Listwise: N = 280.

I-BORA (14) = Inventory to assess Behaviour towards Organisational Resilience, SCD-Q (28) = Safety Culture Development Questionnaire, REST-Q (24A) = Recovery-Stress Questionnaire.

No significant correlation between the overall behavior toward organizational resilience and overall safety culture was found, r (280) = .006, p = .921. Research Question 1 can therefore only be answered by a significant positive correlation between informed culture and access to resources and a small significant positive correlation between overall safety culture and access to resources, r (281) = .16, p = .005.

The Effect of Psychological Stress and Recovery (RQ 3) Research Question 3 focused on how psychological stress and recovery affect resilient behavior and safety culture.

Pearson correlation results are reported in Table 4. Results demonstrated significant positive correlations between overall stress and overall recovery. All dimensions of behavior toward organizational resilience correlated positively with the stress dimensions except for access to resources, which correlated negatively with psychological stress and positively with recovery. Safety culture scales correlated generally negatively with overall stress and positively with overall recovery. Based on the correlation results it is suggested that behavior toward organizational resilience and safety culture are significantly related to overall stress and overall recovery.

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In order to get a first indication on the causal relationship between the three concepts, the four components describing behavior toward organizational resilience, the five safety culture scales, and all 12 recovery/stress scales were entered into an SEM. According to Tabachnick and Fidell (2007), the overall SEM model did not reach an acceptable model fit, w2 = 422.1 (78/174; N = 282) p = .000; CFI = .90, NFI = .86, CMIN/df = 2.43, RMSEA = .071. The normed fit index (NFI) as well as the comparative fit index (CFI) missed the required cutoff of .95. The root mean square error of approximation (RMSEA) is supposed to produce values of .06 or less to indicate a good-fitting model. The ratio of the w2 to the degrees of freedom (CMIN/df ), at 2.43, lay just within the limits (2–3).

Although the overall model fit was unacceptable, results in Figure 2 are presented as an initial starting point for future research. The most relevant paths are printed in bold. Path coefficients can be interpreted as standardized regression coefficients or simple correlations. Social– emotional stress was found to have a positive effect on behavior toward organizational resilience (.25) and a negative effect on safety culture ( .42). Recovery was found to have a small negative effect on safety culture but a large positive effect on behavior toward organizational resilience (.60), which in turn affected safety culture positively (.11).

The effect of performance-related stress on safety culture was too small to be interpreted. Ó 2016 Hogrefe Publishing

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Figure 2. Structural Equation Model for Behaviour towards Organisational Resilience, Safety Culture Development and the Recovery-Stress Balance (CFI = .91, NFI = .86, CMIN/df = 2.43 RMSEA = .071, significant Chi2). Relevant path coefficients printed in bold.

Discussion It was postulated that through the principle of self-similarity (Mandelbrot, 1977) it is possible to draw conclusions from resilient behavior on safety culture. This approach is considered one alternative to the school of resilience engineering focusing on assessing resilience on a systemic/functional level (Cook & Rasmussen, 2005; Dekker et al., 2008; Hollnagel, 2010, 2012, 2014; Woods, 2006a). Safety culture was considered to have an indirect influence on behavior toward organizational resilience (Woods, 2006a), which, in turn, was deemed especially relevant in stressful conditions where systems operate close to an operational boundary (Rasmussen, 1997).

RQ 1: Assessing Organizational Resilience at Individual Level Research Question 1 asked whether behavior toward organizational resilience (I-BORA) derived from existing literature (Kolodej, Reiter, & Kallus, 2013; Mallak, 1998, 2006; Reiter, 2011; Somers, 2009) can be reliably assessed (Cronbach’s α > .70) in ATM operators including controllers, air traffic safety electronics personnel, and meteorologists. Research Question 1 was answered partly for goal-directed solutions, which proved reliable (Table 2) across all three subsamples. Goal-directed solutions was Ó 2016 Hogrefe Publishing

related to proactive problem solving and continuously seeking improvements in the job, which was also confirmed (Table 1) to be relevant for the ability to flexibly cope with unexpected situations (Flexibility) and the ability to improvise (Improvisation). Improvisation could be reliably assessed for meteorologists (Cronbach’s α = .78), but neither for controllers nor for air traffic safety electronics personnel. Considering the degree of uncertainty in the forecasting of weather phenomena by nature and the nature of work being less procedural, improvisation may be required more often in meteorological tasks compared with controller or engineering tasks. Both flexibility and improvisation have been identified as sources of organizational resilience before (Kolodej et al., 2013; Mallak, 1998, 2006; Somers, 2009), therefore it is recommended to further develop them in future studies. Access to resources including relevant knowledge and information, personal contacts, as well as general resources (e.g., budget, time, equipment, procedures) to adequately perform tasks showed sufficient Cronbach’s α values for internal consistency on the overall level.

RQ 2: Resilient Behavior and Safety Culture Research Question 2 was inspired by Woods (2006a) and Hollnagel (2010), who suggested a theoretical relationship Aviation Psychology and Applied Human Factors (2016), 6(1), 12–23

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between Resilience and Safety Culture. No significant correlation between Overall Resilient Behavior and Safety Culture was found. However, some Resilient Behavior did correlate significantly, but not strongly, with some Safety Culture facets except for Management’s Safety Attitudes. Management’s Safety Attitudes related to active encouragement of safe work practices and safety improvements in the organization by management. The positive correlation between Access to Resources and Informed Culture highlighted that ensuring staff have adequate knowledge to perform tasks and can access certain information and personal contacts to manage issues increases safety communication in the organization.

Access to Resources is therefore considered a key factor for keeping ATM systems both safe and resilient (Hollnagel, 2014). The second positive correlation was between GoalDirected Solutions and Just Culture. Although Just Culture, being based on only two items, did not have sufficient internal consistency, this result is an important indication that an open reporting environment and clear knowledge about acceptable and unacceptable behaviors enables GoalDirected Solutions. Based on this indication, staff are more likely to consider problems as a challenge and exchange ideas regarding improvements with colleagues in a fair and just environment. The European Commission (2011, 2014) mandated regular reporting on Just Culture implementation as one of the three main safety performance indicators in ANSPs, and representatives from the European Air Industry recently signed a European Corporate Just Culture Declaration (2015) aimed at delivering a safer aviation system. These Just Culture initiatives will benefit from further developing the assessment of Just Culture in relation to safety and resilience in future studies.

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and also directly affects safety culture. Safety culture is negatively affected especially by social–emotional stress.

According to Kallus and Kellmann (2015), this dimension is related to frequent irritation, aggression, arguments, restraints, and lack of humor, while performance-related stress is associated with mental stress, imbalance, and work-related fatigue. However, no effect of performancerelated stress on safety culture was found. Based on these findings it can be concluded that safety culture has the potential to indirectly influence resilient behavior and that stress and recovery plays an important role. It is therefore recommended that Air Navigation Service Providers and regulators consider stress and recovery in the safety culture implementation and improvement.

A Word on Methodological Limitations In this paper, two newly developed instruments related to safety culture development (SCDQ) and behavior toward organizational resilience in ATM were used (I-BORA). Both instruments used the same sample for an initial check on reliability and factor analysis (cf. Schwarz & Kallus, 2015).

Another limitation was the sample size of N = 282, which was just under the general rule of thumb of N = 300 for delivering fair–good results for SEM and factor analysis (Tabachnick & Fidell, 2007, p. 683), and that sample sizes across occupational groups differed. Future safety culture studies should, therefore, explore using incentives to increase the response rate and reduce sample size differences to reflect the general population.

RQ 3: The Effect of Psychological Stress

Conclusion and Practical Relevance

Research Question 3 postulated that resilient behavior and safety culture are related to psychological stress and recovery based on the recovery–stress balance model (Kallus, 1995) and inspired by findings from Fogarty (2005). Correlational analysis confirmed the findings of Fogarty (2005), who suggested that safety culture will be negatively affected by stress and positively affected by recovery. Resilient behavior on the other hand was positively related to stress and recovery. That positive relationships can be interpreted as behavior reflecting organizational resilience is especially relevant in stressful conditions (e.g., unexpected events) and recovery phases from stressful events; however, it is less relevant in normal working conditions (e.g., expected events). Access to resources was found to relieve stress and ensure recovery. Results from SEM, despite fit indices being unacceptable, suggested that resilient behavior is largely positively affected by both stress and recovery

This paper demonstrated that the I-BORA is not yet robust enough for measuring organizational resilience at an individual level across different occupational groups. In addition, this explorative study provided little evidence that through the principle of self-similarity (Mandelbrot, 1977) it is possible to draw conclusions from resilient behavior on safety culture. Initial results on the reliability of the instruments used demonstrated that safety culture and resilient behavior are difficult to measure in the current operative environment and further work is needed to improve validity and reliability. However, access to appropriate resources (Hollnagel, 2014) seems an important element of resilient behavior, becoming especially relevant in unexpected and stressful conditions. If these situations do not occur, resilient behavior is less urgent. Results also point to the assessment of stress and recovery as critical for ensuring high safety levels in ATM. In the context of

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existing safety culture research (Zohar, 2010) there is still a strong need for more empirical data related to organizational resilience, safety culture, and their relationship with stress to allow for a critical scientific discussion. With respect to safety culture measurement, EUROCONTROL (Mearns et al., 2009, 2013; Shorrock et al., 2011) has led the way in reliably assessing safety culture using the Safety Culture Measurement Toolkit including a standard questionnaire and workshop techniques. Future research should also focus more on everyday individual and team behaviors in ATM applying interview and behavioral observation techniques in addition to existing survey methods. Aside from academia, it is necessary to translate empirical data into practical guidelines and interventions (e.g., safety training programs) to improve existing safety management systems and overall ATM safety performance. With this study, the first attempts to demonstrate the link between resilient behavior and safety culture development have been successful, but there is a long journey ahead of us. In order to be effective this journey must include encouraging the regulator to link the effectiveness of safety management standards (CANSO, 2009; European Commission, 2011; ICAO, 2013a; 2013b) to organizational resilience and safety-relevant behavior on the job.

Acknowledgments The authors would like to thank the Austrian Research Promotion Agency (FFG) in cooperation with the Austrian Air Navigation Service Provider and the University of Graz for the funding and realization of this project. The authors are indebted to all operational teams for their important contributions made by completing the questionnaire package. Special thanks go to all unit chiefs and regional managers for their outstanding personal commitment in getting the message across to their troops and following up on response rates.

The opinions expressed in this paper are those of the authors and are not necessarily those of their parent or affiliated organizations.

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Michaela Schwarz (Heese) Austro Control GmbH Schnirchgasse 11 1030 Vienna Austria Tel. +43 5 1703-1038 E-mail michaela.schwarz@austrocontrol.at Michaela Schwarz (PhD) has been working as Senior Aviation Human Factors Expert for more than 10 years and is currently affiliated with Austro Control (Vienna). Her main interest relates to studying safety-relevant behavior in air traffic management. Since 2014 acts as Secretary General of the European Association for Aviation Psychology.

DDr. K. Wolfgang Kallus has been the Head of work, organizational, and environmental psychology at the University of Graz since 1998. His main research interest focuses on psychophysiological measurements in aviation including spatial disorientation training for pilots, human factors in maintenance, and safety culture in air traffic management.

Kerstin Gaisbachgrabner (PhD) is affiliated with the Department of Workand Organisational Psychology and the Department of Health Psychology at the university of Graz. Her main research interest and doctoral dissertation is related to the psychological and somatic influences of long working hours on the hypothalamic – pituitary –thyroid axis.

Received March 14, 2015 Revision received December 18, 2015 Accepted December 19, 2015 Published online May 3, 2016

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Original Article

A Flight Simulator Study of the Impairment Effects of Startle on Pilots During Unexpected Critical Events Wayne L. Martin, Patrick S. Murray, Paul R. Bates, and Paul S. Y. Lee Department of Aviation & Logictics, University of Southern Queensland, Brisbane, Australia

Abstract: Recent aircraft accidents have implicated startle as contributory, or directly causal, in situation outcome. The startle reflex is a ubiquitous response to surprising stimuli, which results in aversive movement and attentional orienting. Fear-potentiated startle, where a startling stimulus is experienced in the presence of conditions that are appraised as harmful or threatening, has the effect of initiating and exacerbating the stress response, particularly where threat persists, such as during an aircraft emergency. The deleterious effects of this stress response on cognitive function are discussed. Results from startle research in a B737 flight simulator showed considerable cognitive impairment in approximately one third of participants.

Keywords: startle, simulator study, surprise, stress

A number of recent aircraft accidents have highlighted the potential effects of startle in accident causation. Air France Flight 447 (BEA, 2012) and Colgan Air Flight 3407 (NTSB, 2010), in particular, are accidents where investigators have specifically noted the effects of impaired performance following startle as likely contributors to the negative outcome. Other accidents such as Pinnacle Airlines Flight 3701 (NTSB, 2007), Turkish Airlines Flight 1951 (Dutch Safety Board, 2010), and West Caribbean Airways Flight 708 (BEA, 2006) are examples where startle was likely to have been a contributory factor, although accident investigators did not specifically record it as a causal factor.

Startle is a ubiquitous response to unexpected stimuli that is common to all mammals (Simons, 1996). It can be generated in all modalities, although auditory, visual, and somatosensory startle are the most commonly experienced (Simons & Zelson, 1985). The physical startle response is a simple reflex action that generally commences with an eye blink and develops into an aversive movement away from the stimulus, while at the same time orienting the attentional mechanisms to the startling source (Davis, 1984; Landis & Hunt, 1939; Lynn, 1966). Cognitive processing quality may also be affected and is discussed later in this section.

Experiments over the last three decades involving humans, rats, primates, and other species have examined the effects of startle on the body, with considerable Aviation Psychology and Applied Human Factors (2016), 6(1), 24â&#x20AC;&#x201C;32 DOI: 10.1027/2192-0923/a000092

attention paid to the neural and physical architecture of the startle reaction (e.g., Davis, 1984, 1992; Grillon, Ameli, Woods, Merikangas, & Davis, 1991; Lang, Bradley, & Cuthbert, 1997; Vrana, Spence, & Lang, 1988). Results have shown than an extensive and elaborate reaction occurs throughout the body, particularly when the startle occurs in conjunction with a conditioned stimulus that has been associated with some fearful or threatening experience. Fear conditioning studies have shown that when startle is experienced in the presence of threat, or perceived threat, then the severity of the startle is greatly exacerbated, creating a reaction generally known as fear-potentiated startle (Bradley, Moulder, & Lang, 2005; Brown, Kalish & Farber, 1951; Davis, 1989, 1992; Grillon et al., 1991; Lang et al., 1997; LeDoux, 2000; Vrana, Spence, & Lang, 1988). This enhanced reaction, which involves the arousal of stress circuits within the body, extends well beyond the simple reflex reaction and engenders significant changes in the nervous system, endocrine system, and the workings of the brain (LeDoux, 1996, 2000).

The fear-potentiated startle is initiated by inputs from the sensory organs (Davis, 1992). With the exception of smell, this sensory information is routed via the sensory region of the thalamus (LeDoux, 1994, 2000). These signals are then routed to both the medial prefrontal cortex (Garcia, Vouimba, Baudry, & Thompson, 1999; Groenewegen & Uylings, 2000; LeDoux, 1996; McEwen, 2007) and to the Ă&#x201C; 2016 Hogrefe Publishing

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basolateral complex of the amygdala (LeDoux, 1996, 2000, 2006; Maren, 2001).

While the amygdala analyzes the new stimuli very rapidly, the information also undergoes more elaborate cortical processing, which some time later proceeds back to the amygdala for modification or reinforcement of the original emotionally salient information (Davis, 1992; LeDoux, 1994, 2000). This dual-path sensory analysis creates a gap between the almost instantaneous evaluation of emotional valence that occurs in the amygdala and the cortical evaluation of the same information that takes in excess of 500 ms (Åsli & Flaten, 2012), commonly leading to false alarm startles.

The amygdala is a site of emotional memory, where actual rudimentary memory of emotionally significant associations are either stored or accessed, allowing very rapid assessment of all incoming sensory data for emotional valence (LeDoux, 1996, 2000, 2003, 2006; Maren, 1999). This assessment, which is commonly known as primary appraisal (Lazarus, 1966; Lazarus & Folkman, 1984), rapidly establishes whether the incoming stimuli are considered benign, positive, or irrelevant, or whether it suggests possible harm, threat, or challenge (Lazarus & Folkman, 1984; Lazarus, 1966). Appraisals occur continuously, using both precortical and postcortical assessment, to determine emotional significance, and may be accompanied by secondary appraisal of appropriate coping measures (Lazarus & Folkman, 1984; Monat & Lazarus, 1991).

When a startling stimulus elicits the startle reaction, a very rapid process is initiated, which has been shown to manifest itself in humans in as little as 14 ms (Davis, 1984). Signals sent from the central nucleus of the amygdala travel to the nucleus of the reticulus pontis caudalis (RPC), near the top of the brain stem, which in turn initiates the motor responses of the startle reflex (Davis, 1984).

At the same time the brain’s attentional resources are automatically directed toward the source of the startling stimulus in an autonomic attempt to identify the supposed danger. This orientation response is a defensive mechanism that also limits irrelevant cues (Lynn, 1966).

Concurrent with these processes is the fight-or-flight reaction, which initiates rapid arousal of the sympathetic anervous system (SNS) via the hypothalamic–pituitary– adrenocortical (HPA) axis (Gray, Carney, & Magnuson, 1989; Papadimitriou & Priftis, 2009). This process begins introducing epinephrine (adrenaline) and other hormones to the bloodstream, raising heart rate, blood pressure, and activating other emergency response processes (Ullrich-Lai & Herman, 2009). At the same time, signals from the amygdala travel to the lateral hypothalamus, which initiates the stress response, which is a more holistic activation of the SNS. Where the original startling stimulus has been processed cortically and deemed to be a false alarm, signals are sent Ó 2016 Hogrefe Publishing

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from the prefrontal cortex to the amygdala to modify the original appraisal (Delgado, Nearing, LeDoux, & Phelps, 2008; Sotres-Bayon, Cain, & LeDoux, 2006). Fear extinction circuits (Milad & Quirk, 2002) then allow the body to return to a state of homeostasis (Ellis, Jackson, & Boyce, 2006; Johnson, Kamilaris, Chrousos, & Gold 1992; McEwen, 2007; Stratakis & Chrousos, 1995; Tsigos & Chrousos, 2002) under the influence of the parasympathetic nervous system.

The fear-potentiated startle that ensues from the combination of startling stimulus and the perception of threat or fear, causing a fully developed stress reaction, has been shown by research to result in significant cognitive disruption in most people (Eysenck, Payne, & Derakshan, 2005), with experiments by Thackray and Touchstone (1970), Vlasak (1969), and Woodhead (1959, 1969) showing deleterious effects on cognitive and psychomotor performance for up to 30 s following startle. Simons (1996) describes this process as a period where the brain is searching the environment, trying to make sense of what is going on, at the expense of constructive thought. This time without organized thought will vary with startle strength, but has implications in time-critical situations.

The stress response associated with fear-potentiated startle can be severely problematic. Several researchers have explored the relationship between anxiety, stress, and cognition, with consistent results showing impaired information processing under the influence of stressors (e.g., Bishop, 2009; Edland, 1989; Eysenck, Derakshan, Santos, & Calvo, 2007; Eysenck, Payne, & Derakshan, 2005; MacLeod, 1996; Salthouse, 2011). While stress restricts cue sampling and narrows the perceptive field (Bacon, 1974; Combs & Taylor, 1952; Easterbrook, 1959), it has also been shown to reduce the capacity of the working memory (Baddeley, 1972; Eysenck, & Calvo, 1992; Lavric, Rippon, & Grey, 2003; Wickens, Stokes, Barnett, & Hyman, 1991).

Other effects noted by research include a failure to think beyond prescribed procedures, which may reduce problemsolving capabilities during complex, multifaceted, or ambiguous situations (Broadbent, 1971). Problem solving and decision making are also affected through perseveration with a favored option, indecision, or hypervigilance under stressful conditions (Flin, Salas, Strub, & Martin, 1997; Serpell, Waller, Fearon, & Meyer, 2009; Svenson & Maule, 1993; Zakay, 1993). Perseveration and attentional narrowing have also been shown to contribute to a pattern of cognitive narrowing or tunneling, which can be dangerous during critical decision making (Dehais, Tessier, Christophe, & Reuzeau, 2010; Woods, Johannsen, Cook, & Sarter, 1994).

The combined effects of attentional tunneling and perseveration could have a significant influence on the successful handling of an aircraft emergency (Woods et al., 1994) and accidents such as Ansett New Zealand Flight 703 (TAIC, 1995), United Airlines Flight 173 Aviation Psychology and Applied Human Factors (2016), 6(1), 24–32

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(NTSB, 1978), and Eastern Airlines Flight 401 (NTSB, 1973) where attention was focused on a landing gear problem are not atypical.

Internationally, regulatory agencies and airlines are increasingly becoming more cognizant of the effects of startle and surprise during unexpected critical events and are encouraging or implementing recurrent training programs that incorporate surprising events. To date, however, there has been little research in an operational context that quantifies the potential cognitive effects of startle among airline pilots. Research that exposes pilots to surprising critical events and quantifies the performance decrements that ensue is virtually nonexistent, and the literature on startle in an operational context would be enhanced by further studies. The aims of the current study were to further the literature on pilot startle effects by conducting an experiment that quantified performance decrements following a startling stimulus in a safety-critical phase of flight.

Flight Simulator Experiment With Pilots To investigate the effects of startle on pilots during unexpected critical events, a flight simulator study was conducted in a B737 Flight Simulator Training Device. The objectives of the study were: (a) to create startle reactions in pilots; and (b) to quantify the effects of startle on pilot performance during a go-around. It was hypothesized that a strong startle immediately prior to a decision-making task (go-around) would engender delays in cognitive processing that would affect the timeliness and conduct of the go-around procedure. The hypothesis proposed that on the approach, that is, an approach with no startle, the missed approach would be commenced at or close to the manufacturer’s guidelines for altitude lost on a missed approach (200 ft decision height – 30 ft altitude loss = 170 ft minimum altitude on go-around) (Boeing, 2012).

The experimental scenario, that is, the approach with a startling stimulus, hypothesized that there would be a delay in commencing the missed approach procedure due to some startle-induced information-processing delay or possibly some other cognitive impairment manifestations such as in decision making.

Selection of the simulator would preferably involve the highest level D simulator, but these are not easily accessible and expensive to use. Considering the exploratory nature of this study, it was decided to use a fixed-base simulator.

Early research into the effects of flight simulator motion cues (e.g., Flexman, 1966; Koonce, 1974; Roscoe, 1980) suggested that simulator motion was an important cue for pilot interpretation of spatial position and directional cues; however, later research (e.g., Burki-Cohen, Go, & Longridge, 2001; Go, Burki-Cohen, & Soja, 2000; Hays, Aviation Psychology and Applied Human Factors (2016), 6(1), 24–32

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Jacobs, Prince, & Salas, 1992; Lee & Bussolari, 1986) indicates that the visual cues generated in a modern (post2000) flight simulator are so realistic that the absence of motion cues is less critical. Indeed, the visual fidelity of a modern high-quality fixed-base flight simulator (FBS) approaches that of a full-flight simulator (FFS) with very accurate computer-generated visual acuity.

Burki-Cohen et al. (2003) showed that motion cues, while useful for identification purposes during engine failure asymmetry type situations, were not a prerequisite in other general maneuvers. The go-around maneuver that was used in this study was largely conducted on instruments, without the need for sensory motion cues and none of the pilots who participated in the experiment admitted any decrement in performance associated with a lack of simulator motion. The exercise was conducted predominantly in cloud with visual meteorological conditions only available on departure and below 100 ft above ground level (AGL) on each approach. The lack of visual cues in cloud and the total immersion in the task at hand appear to have contributed to the lack of sensitivity to motion in the FBS.

Method A study was conducted using a Mechtronix B737NG fixedbase simulator with a group of B737NG type rated pilots of varying experience and age levels (n = 18, 17 male, one female). Average total experience was 8,724 hr (SD = 453) with a mix of captains and first officers (C = 11, F = 7). Average time on type was 4,166 hr (SD = 2,179 hr). Average age was 40.4 years (SD = 8.67) and participants were all active B737NG pilots from three Australasian airlines. An experienced B737NG type rated pilot acted as the support pilot not flying for each research participant, but only supported passively following the startle stimulus.

The research participant was in the pilot flying role from the left seat. The experiments were conducted between 09:00 a.m. and 5:00 p.m. local time and all pilots reported being well rested. Each pilot was involved for a maximum of 1 hr.

The experiment involved two hand-flown instrument landing system (ILS) approaches where the weather was such that a missed approach would be required on reaching the decision altitude (DA; 220 ft above mean sea level [AMSL], 200 ft AGL). The pilots were told that there would be at least one startling stimulus at some stage during the 40-min exercise; however, no indication was given as to when the startle would occur.

On the first approach a startling stimulus (cargo fire warning bell and coincident loud bang) were administered at 240 ft AGL (40 ft above the DA). Following this Ó 2016 Hogrefe Publishing

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Table 1. Quantitative experiment data Candidate

Stimulus altitude radio AGL

App. 1 minimum altitude on go-around (with stimulus)

App. 2 minimum altitude on go-around (no stimulus)

Reaction delta App. 1 from App. 2

240 ft 240 ft 240 ft

170 170 170

170 140 190

0 30 20

240 240 240 240 240 240 240

160 150 150 150 150 140 140

170 180 160 170 170 170 160

10 30 10 20 20 30 20

140 150 180 150 190 166

54 84 124 150 190 48.8

a b c d* e f g h i j k l* m* n o p q r

Data excluded from analysis ft ft ft ft ft ft ft

Data excluded from analysis Data excluded from analysis 240 240 240 240 240 Averages

ft ft ft ft ft

86 66 56 0 0 117.2

Notes. AGL = above ground level. App. = approach. *The data from participants d, l, and m were excluded from this analysis. Participant d produced a substandard missed approach performance on the second approach, which was not able to be fairly compared with the missed approach performance following startle. Participants l and m were excluded from the analysis because their missed approach was commenced prior to reaching the decision altitude.

approach pilots were vectored for a second approach.

There was no stimulus on the second approach; however, pilots were required to commence a standard missed approach when they failed to become visual at the minima.

Height loss during this second missed approach was used as the comparator against the missed approach following startle.

Results Table 1 provides an overview of the available data. Note that the cloud base was set at 100 ft AGL (100 ft below DA), and in three cases the approach was continued in instrument meteorological conditions (IMC) so far below DA that the pilots concerned became visual. In all three of these cases the approach was unstable, with two of the three being badly unstable, resulting in Enhanced Ground Proximity Warning System (EGPWS) sink rate warnings. One of these badly unstable approaches eventually resulted in a very late decision to abort the landing and go around for another approach.

The data show that the mean difference between minimum altitude on the approach with startle and the approach without startle was 48.8 ft. This equates to an average delay of approximately 5 s in commencing the missed approach following startle, compared with that without startle (based on a rate of descent of 10 ft/s, which was the approximate rate of descent based on a standard threedegree glide path at the average approach speed). Ó 2016 Hogrefe Publishing

Table 2. Reaction delta (App. 1) versus age Reaction delta (166’ vs. App. 1)

Age

4 4 4 6 6 16 16 16 16 26 26 34 54 80 100 110 166 166

35 35 40 33 30 37 28 33 50 57 35 41 51 46 49 29 46 52

Note. App. = approach.

Analyses were conducted to establish statistical relationships between age and rank on Approach 1 reaction delta.

Table 2 shows the relationships between age and reaction delta (Approach 1). Note an average Approach 2 minimum altitude of 166 ft was used as the comparative altitude.

A moderate correlation was shown between age and the startle performance observed on Approach 1 (r = .41; see Figure 1).

The plot shows that with one exception, the moderate-to poor performances were from pilots in the 46–57-year age group.

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Table 3 and Figure 2 show the relationships between rank and reaction delta (Approach 1). Note an average Approach 2 minimum altitude of 166 ft was used as the comparative altitude.

An independent t test was conducted to establish whether rank was a significant factor in the performance results. This showed that rank was not significant in the results (p < .05).

Table 4 shows the relationships between hours flown (total flying experience) and reaction delta (Approach 1).

Note an average Approach 2 minimum altitude of 166 ft was used as the comparative altitude. There was a minimal correlation between experience and the startle performance observed on Approach 1 (r = .13), and the relationship was not significant ( p < .05; see Figure 3).

Approach 1 Delta in Feet

180 160 140 120 100 80 60 40 20 0 -20 25

30

35

40

45

50

55

60

Age in Years

Figure 1. Approach 1 delta versus age in years.

Table 3. Reaction delta (App. 1) versus rank Reaction delta (166’ vs. App. 1)

Rank

4 4 4 6 6 16 15 16 16 26 26 34 54 80 100 110 166 166

Discussion

C F C C F F C C F C C F C F C F C C

Some recent accidents have highlighted the potential effects of startle during unexpected critical events as occurred during Air France Flight 447 and Colgan Air Flight 3407, two accidents where startle has received some scrutiny and was included as a potential causation factor. In these two accidents – where the pilots inappropriately pulled up following startling stimuli, and other similar accidents – ineffective, inappropriate, or even nonexistent efforts to maintain aircraft control following startle have been likely contributors to situation outcome.

The flight simulator experiment aimed to explore the effects of startle on pilots who were exposed to a startling stimulus immediately prior to a critical decision-making task. The results were consistent with earlier research that

Note. App. = approach. C = captain. F = first office.

180

Figure 2. Approach 1 delta versus rank (captain, first officer).

Approach 1 Delta in Feet

160 140 120 100 80 60 40 20

0 -20

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

Participant by Order of Delta Captain First Officer

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Table 4. Reaction delta (App. 1) versus experience

in the data, even if it actually bounded their performance to DA – 200 ft. In future replications of this study, while still needing to replicate realistic conditions as much as possible, and regardless of simulator fidelity, it may be better to have a higher DA (perhaps 500 ft AGL) and a zero cloud base, so as to reduce the chances that participants could prevaricate long enough to become visual.

Reaction delta (166’ vs. App. 1)

Experience (total hours)

4 4 4

6,400 4,600 13,600 5,000 2,600 9,000 5,300 8,500 8,000 19,800 8,500 6,000 10,200 15,000 14,000 3,040 6,000 11,500

6 6 16 15 16 16 26 26 34 54 80 100 110 166 166

Conclusion

Note. App. = approach.

180 160 140 120 100 80 60 40 20 0 -20

0

5000

10000

15000

20000

Figure 3. Reaction delta (Approach 1) versus experience.

showed individual variation in responses and recovery from startle, with some people only slightly affected and recovering quickly, while others were badly affected and took some time to recover.

The effects of experience (both total flying hours and time on type) appeared to have little correlation with startle response. Similarly, the effects of age were not significant in the data, although a couple of the worst performers were notably some of the older participants. Rank also showed no significant correlation with startle response, with both captains and first officers among the worst performers.

The effects of gender were not examined owing to a disproportionately small number of female participants (n = 1).

A surprise finding was that the two pilots who landed off the first approach (following startle) may have had their decision making influenced by the fact that they became visual with the ground at 100 ft AGL. The inclusion of their data in the dataset was justified because their performance was badly affected, even below 100 ft, and it was considered important to reflect their impaired decision making Ó 2016 Hogrefe Publishing

Results of a flight simulator experiment showed that around one third of pilots (n = 7) were so badly affected that their performance following startle resulted in a considerable delay as indicated by lost altitude on a go-around decision, or, worse, with some continuing on very unstable approaches to the point where flight safety was impacted.

Correlations between age, rank, experience, and poststartle performance were statistically insignificant ( p < .05).

Real-life responses to unexpected stimuli during aircraft accidents have suggested that some pilots can be badly affected by the effects of startle. Given that approximately one third of the pilots in the simulator experiment were badly affected, it is likely that some generalizing to the pilot population would suggest that startle may indeed have been a greater factor in previous accidents.

Further studies into the effects of startle in operational contexts are warranted, particularly in the aviation industry, which is a high-risk, sometimes error-intolerant paradigm, with significantly negative consequences likely following failure to recover from unexpected and undesired aircraft states.

While laboratory testing of animals has been used widely to expand the understanding of startle effects, further testing of humans to better quantify the cognitive and psychomotor effects of startle would benefit the knowledge of effects in aviation and other disciplines. This could provide useful data for accident investigation, accident prevention, and pilot education.

The effects of startle have been highlighted in a number of recent aircraft accidents and regulatory agencies around the world have started to provide both guidance material and requirements for future regulatory compliance for startle and surprise event training. Both the Federal Aviation Administration (FAA) and European Aviation Safety Agency (EASA) are calling for the incorporation of surprise event training in recurrent training programs within the next few years. Additionally, a number of airlines have started incorporating surprise event training into their recurrent programs under the International Civil Aviation Organisation (ICAO) guidance provided on evidence-based training.

Exposure to such training, if done in a constructive manner, Aviation Psychology and Applied Human Factors (2016), 6(1), 24–32

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is likely to enhance pilot management of future surprising events and is therefore to be encouraged.

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Received April 8, 2015 Revision received December 18, 2015 Accepted December 19, 2015 Published online May 3, 2016 Wayne L. Martin University of Southern Queensland Brisbane Australia Tel. +61 41 652-8479 E-mail Wayne.Martin@usq.edu.au

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W. L. Martin et al., Pilot Startle Simulator Study

Wayne Martin (BAvMan, MAvMgmt, MBus, PhD, FRAeS) is a postdoctoral researcher and lecturer at the University of Southern Queensland and a B777 Pilot with a major Australian airline. He runs the annual PACDEFF Aviation Human Factors Conference and chairs the IPTC LOC-I workstream.

Patrick Murray (MAvMgmt, MAP, FRAeS, FCILT, FAIM) is Professor of Aviation and Logistics at the University of Southern Queensland, Australia. Previously holding senior positions in the military, a major airline, and the Australian aviation regulator, he is an active flying instructor and researches in evidence-based training and airline safety.

Aviation Psychology and Applied Human Factors (2016), 6(1), 24â&#x20AC;&#x201C;32

Paul Bates (BSc, PhD, FRAeS) is Professor and Head of Aviation and Logistics at University of Southern Queensland, Australia. He is also Executive Chairman of The International Aviation Training and Education Organisation and a member of IPTC task force outreach team and ICAO NGAP Outreach Committee. His research includes aviation safety and communities of practice.

Seung Yong (Paul) Lee (BAv, Mav) is a senior lecturer at the University of Southern Queensland, Australia. Before becoming a full-time academic, he was a flight instructor for almost 10 years. He is currently pursuing his PhD in the field of human factors with a particular focus in general aviation.

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APAHF in Practice

An Application of the HFACS Method to Aviation Accidents in Africa Isaac Munene Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

Abstract: The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

Keywords: aviation, human factors, Africa, aviation accidents, HFACS

Aviation safety in the Africa region has continued to be a concern for the International Civil Aviation Organization (ICAO) and the industry as a whole. According to the ICAO’s 2012 accident statistics, Africa had an accident rate of 5.3 per one million departures with 3% of the worldwide traffic distribution (ICAO, 2013). The accidents are a result of varying factors.

Human error, it has been suggested, accounts for 70–80% of all aviation accidents (O’Hare, Wiggins, Batt, & Morrison, 1994; Shappell & Wiegmann, 1997). Human performance considerations in aviation include the flight crew, ground crew, air traffic controllers, and other supporting organizational functions when an accident investigation is conducted. Formal reports are published to document the probable cause and contributory factors. It has been observed that detailed safety information in Africa is not always available with the exception of South Africa, considered Africa’s most progressive country (Rinefort & Petrick, 2012).

The Kenya Civil Aviation Authority (KCAA) has recognized the challenge of improving Africa’s aviation safety record while continuing to nurture and develop the industry. The director general of KCAA noted that the Africa region has a small number of aircraft compared with other ICAO regions. He further attributed the high accident rates to the continued use of older aircraft by African operators (Kioko, 2013). Aircraft operators in Africa also face the

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challenge of an immature supply chain as well as a lack of availability of spares and qualified mechanics to support maintenance. From a US Government Accountability Office (GAO; 2009) report, it was observed that African countries were experiencing a lack of qualified aviation professionals.

Most professionals have left for opportunities with better compensation in Asia and the Middle East. In addition to this shortage, the ICAO’s Universal Safety Oversight Audit Programme (USOAP) further revealed technical qualifications, training policies, and training documentation were insufficient to ensure safer operations (ICAO, 2008).

Africa’s aviation industry and its people are as diverse as the possible contributors to Africa’s safety record. The continent consists of 54 countries, over 1,000 different language groups, and close to one billion people. Most of the countries in Africa are among the poorest in the world (Krabacher, Kalipeni, & Layachi, 2011).

Considering the prevalence of human error in aviation accidents and the broad attribution of accidents to the deficiency in personnel skills in Africa, it is critical to understand the contributory factors to the continent’s accidents in order to address them appropriately. To achieve this, the Human Factors Analysis and Classification System (HFACS) was applied to aviation accidents associated with operators in three countries, namely, South Africa, Kenya, and Nigeria. This research seeks to address an existing gap in the analysis of Africa’s aviation accidents.

Aviation Psychology and Applied Human Factors (2016), 6(1), 33–38 DOI: 10.1027/2192-0923/a000093

34

HFACS HFACS is a commonly used analysis tool, based on Reason’s theory (Reason, 1990), and developed by Shappell and Wiegmann (2000) for classifying both active and latent failures that contribute to human error (Wiegmann & Shappell, 2001a). In Reason’s “Swiss cheese” model, four levels of human error are described: a. unsafe acts, b. preconditions for unsafe acts, c. unsafe supervision, and d. organizational influences.

Shappell and Wiegmann (2000) recognized that Reason’s Swiss cheese model did not define what the “holes in the cheese” were and by applying aviation accidents from the US Naval Safety Center they developed the initial framework, Taxonomy of Unsafe Operations. The taxonomy was later refined with accident data from the US Army and US Air Force Safety Centers in addition to the National Transport Safety Board (NTSB) and Federal Aviation Administration (FAA) resulting in what is now HFACS. The HFACS framework consists of the four aforementioned levels and they are further divided into the lower categorizes as described by Wiegmann and Shappell (2001b).

For the analysis, the detailed description of each category as presented by Shappell and colleagues (2007, p. 230) was used. The classification of human factors enables the identification of common factors that lead to accidents and allows for the development and application of the appropriate corrective actions. Corrective actions can be developed for the adaptation by an individual, a manager, or the organization as needed. The identified factors provide the additional benefit of a baseline with which future performance can be compared so as to determine if the corrective actions or programs were effective.

HFACS can be applied to the selected African aviation accidents since it was previously applied to both military aviation and civil aviation accidents around the world: Shappell and Wiegmann (1997) to US military accidents; Hooper and O’Hare (2013) to Australian military accidents; Gaur (2005) to civil aircraft accidents in India; Shappell et al. (2007) to US commercial aviation; and Lenné, Ashby, and Fitzharris (2008) to Australia’s general aviation crashes.

Method Civil aviation accident data were obtained from databases maintained by the respective aviation organizations or authorities of the three countries: Kenya’s Ministry of Transport and Infrastructure (2014), Air Accident

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I. Munene, Aviation Accidents in Africa

Investigations Department; South Africa Civil Aviation Authority (South African Civil Aviation Authority, 2014); and Nigeria’s Ministry of Aviation, Accident Investigation Bureau (2014). Owings to the limited number of reports that were available, reports for all accidents occurring between January 2000 and September 2014 in Kenya and Nigeria were retrieved. For South Africa, which has a more robust reporting and investigation system (Rinefort & Petrick, 2012), reports for accidents that occurred between January 2013 and September 2014 were obtained for analysis.

The accident reports, which follow the ICAO reporting standards, are made available to the public and are a culmination of the efforts of local and international specialists from the local aviation authority, airframe manufacturer, engine manufacturer, and foreign civil aviation authorities as invited. Each report of an incident, in which the flight safety was affected and/or resulted in damage, in loss of the hull, or in the death of any of the occupants, was reviewed. The reports were verified to have a complete narrative of the incident together with the Findings and Recommendations sections containing the causal and/or contributory factors included. Incidents not related to human factors, for example, structural failure, bird strikes, and mechanical failure, were omitted. Owing to the limited number of accident and incident reports, all phases of flight and types of aircraft were considered. Of the 72 accidents reports obtained from the respective accident investigation organizations, a total of 55 were retained for further analysis.

With the limited scope of this study, coding was performed by the researcher, who has graduate training in human factors and experience in aircraft system safety. Using the Findings and Recommendations information on the reports, all the identified human causal and contributory factors were classified using HFACS. No additional factors were identified or introduced into the analysis as the causal factors in the original investigation report were considered appropriate and adequate for the analysis. The classifications were made in tabular format in Microsoft Excel and closely followed the descriptions of Shappell et al. (2007) and using the examples of Shappell and Wiegmann (2000) as guidance.

Results A total of 55 of the 72 civil accident investigation reports involving aircraft occurring within Africa irrespective of ownership or country of registration were selected for analysis. The numbers per country were as follows: Kenya, 11 of 14; Nigeria, 10 of 13; and South Africa, 34 of 45. These reports were considered to have one or more human factors as causal and contributory factors. In line with Shappell and Wiegmann’s (1997) observation, 76% of African aviation Ó 2016 Hogrefe Publishing

I. Munene, Aviation Accidents in Africa

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Table 1. African aviation accidents associated with each HFACS category Human factor level and subcategory

N (out of 55)

%

Skill-based errors

31

56.4

Decision errors

10

18.2

6

10.9

11

20.0

20

36.4

4

7.3

Unsafe acts of pilot operators

Perceptual errors Violations Preconditions for unsafe acts Physical environment Technological environment Physical/mental limitations

0

0.0

Adverse mental states

2

3.6

Adverse physiological states

1

1.8

Crew resource management

6

10.9

Personal readiness

1

1.8

to accidents. These were followed by the technological environment, adverse mental states, adverse physiological states, and personal readiness categories. The physical and mental limitations category was not observed in any of the accidents analyzed. In some accidents the combination of decision or perceptual errors and a degraded physical or operational environment led to accidents. Pilots in most of these cases made the decision to continue into the deteriorating weather conditions, which led to a loss of situational awareness and eventual crash. For Nigeria in particular, CRM was found to be a major contributing factor. The crew in multiple African accidents did not coordinate their responsibilities for the flight especially in adverse weather, became distracted from their responsibilities, or were indecisive when agreement was necessary for the continued operation of the aircraft.

Unsafe supervision Inadequate supervision

2

3.6

Planned inappropriate operations

4

7.3

Failure to correct known problem

1

1.8

Supervisory violations

0

0.0

Resource management

2

3.6

Organizational climate

1

1.8

Organizational process

5

9.1

Organizational influences

accidents were related to human factors. The human factors were classified by their level and subcategory and summarized as shown in Table 1.

Unsafe Acts Of the 55 accidents analyzed where unsafe acts of the pilot operators were observed, 56.4% (31) of them exhibited skill-based errors, making it the most common category of human factor failure. These errors were observed in a majority of South Africa’s accidents (82%), an indicator of its prevalence in Africa’s accidents. In the reports, descriptions for these errors included poor airmanship, poor technique, and failure to maintain flying speed or poor handling technique. In this category, skill-based errors were followed by violations, decision errors, and perceptual errors. Violations were observed in 36% of Kenya’s accidents with instances where the pilots did not follow company procedures or policies for operations, exceeded the aircraft manufacturer’s demonstrated performance capability, or failed to prepare adequately for the flight they performed.

Preconditions for Unsafe Acts In the preconditions for unsafe acts level, the physical environment (36.4%) and crew resource management (CRM; 10.9%) were the most common human factors contributing Ó 2016 Hogrefe Publishing

Unsafe Supervision At the unsafe supervision level, planned inappropriate operations (7.3%) and inadequate supervision (3.6%) were the most observed human factor categories. Violations committed by supervisors were not observed from this collection of accident reports. As an example, planned inappropriate operations were observed in an accident where the flight crew made the decision to leave for their destination airport late and attempted to make a landing while it was unlit. The inability of an organization’s management to ensure that a pilot was adequately trained to fly as the pilot-in-command and of the national civil aviation to maintain adequate surveillance over an airline’s operations indicate a deficiency in supervision that resulted in accidents.

Each country was found to have one of the human factor levels being prevalent as shown in Figure 1. In a majority of South Africa’s accidents, skill-based errors were a contributing factor; for Kenya, it was flight in an adverse physical environment, and for and Nigeria, crew resource management or teamwork. It is also important to note that flight into an adverse physical environment or weather was observed more frequently in the accidents for the three African countries than any other category.

Organizational Influences The organization process was the most observed category for the organization influences level at 9.1% and was followed by resource management and organizational climate.

In some of the accidents, the organizations were seen not to have provided adequate training in the delivery, handling, and storage of fuel, resulting in either fuel starvation or incorrect fuel being used for a flight. Additionally, one organization failed to provide the required resources for a pilot to make a landing at an airstrip known to have wildlife Aviation Psychology and Applied Human Factors (2016), 6(1), 33–38

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I. Munene, Aviation Accidents in Africa

Figure 1. Classification of human factors by country. SA = South Africa.

CRM = crew resource management.

Table 2. Civil aviation accidents by category Type of operation Commercial (passenger)

Total 17

Commercial (cargo)

3

Aerial work

4

Flight training

6

Private/personal

25

Total

55

crossing or occupying the runway. In one accident where poor teamwork of the crew was observed, the organizations’ safety culture was seen to be a factor, with the crew performing a flight even when concerns about the suitability of the aircraft for flight were raised.

The accidents were distributed over different types of flight operations as shown in Table 2. Commercial passenger operations include airline flights and charter flights.

Africa has a vast tourism industry that requires scheduled and unscheduled flights to game reserves, for example, the Masai Mara. Commercial cargo operations are composed of both scheduled airline operations and unscheduled operations for humanitarian relief efforts.

The high number of skill-based errors observed in private, flight training, and aerial work accidents in some instances occurred in combination with decision and perceptual errors. Pilots neglected to observe caution by allowing students to operate an aircraft while their skill level did not match that required for the planned activity. In one accident, the pilot simulated an engine at low altitude from which the student pilot was unable to recover. For the commercial operations, both passenger and cargo, requiring more experience or rigorous training, an even mix of the following unsafe acts was observed: decision errors, skillbased-errors, perceptual errors, and violations. For this set of accidents, CRM was considered a contributing factor for one of the occurrences in Kenya and five in Nigeria.

Aviation Psychology and Applied Human Factors (2016), 6(1), 33–38

South Africa’s commercial aircraft accidents involved small to mid-size aircraft in contrast to the Kenyan and Nigeria accidents, which had more mid-size and large aircraft.

Discussion ICAO through its USOAP program has over the last few years worked to assist African countries in improving their aviation safety records by exposing the shortfalls in licensing, airworthiness, and aircraft operations that need improvements (Tsiige, 2009). Licensing is a means of monitoring a pilot’s qualifications to operate an aircraft. The high number of skill-based errors shows a possible shortfall in training or training technique. The skills-based errors (28) were all observed in accidents for South Africa; however, this might not be an issue associated to one region only, but can be attributed to the low number of accident reports retrieved for the other two countries. Since South Africa is a leading destination for flight training in Africa, the observations made can be used to improve training. About 20% of these errors were a result of the pilot operator failing to maintain the required flying speed leading to an aerodynamic stall.

In 24% of the skills-based error incidents, the pilot lost control of the aircraft during landing, in a wind-shear condition, during take-off, or flying in turbulent weather. The majority of these accidents (76%) occurred in a private or flight training environment. Improved training techniques, targeted to the individual or private owner, when implemented should address the high number of accidents.

CRM is an essential part of safety management systems (SMS). ICAO has led the adoption of SMS best practices, procedures, and systems in Africa, which were not previously part of its training process. The SMS process also requires a high degree of accountability since it outlines the minimum standards that operators must meet Ó 2016 Hogrefe Publishing

I. Munene, Aviation Accidents in Africa

(Van Dyke, 2006). In Kenya’s accident that occurred in Douala, the crew did not coordinate their duties or adhere to flight monitoring resulting in the loss of control. The captain of the flight, a 52-year-old male pilot with 8,682 hr of flight experience had difficulties coordinating with the first officer, a 23-year-old male pilot with 831 hr. The disparity in experience and seniority, combined with a strong and heightened ego, was seen as a factor in the accident (Republic of Cameroon, 2007). The CRM-related accidents in Nigeria constituted 50% of the total reports reviewed.

The pilot’s decision to take off in an aircraft that was not completely airworthy, fly into adverse weather, or make unstabilized approaches for landing over the concerns or without the acknowledgement of the other crew members resulted in an accident. Improved CRM training will be required to continuously improve Africa’s accident record.

Violation and perceptual errors round up the top contributors to Africa’s accidents. Perceptual errors involve flying in poor weather, at night, or in other degraded environments. Violations seen in Africa’s flight operations included a flight with adequate fuel that resulted in the starvation of the engines and loss of the aircraft. In three instances, the pilot operated the aircraft beyond its capabilities or ignored operating procedures for the mission. The violations were seen across all three countries and operations. The inability of the pilot to perceive the safety implications of exceeding an aircraft’s capability can be countered by effective SMS training in risk assessment.

This study is a useful starting point for understanding the human factors that contribute to Africa’s accidents. The results of this study are limited by the low number of reports from Nigeria and Kenya; however, South Africa’s accident reports support the conclusion that the human factors contributing to accidents are the unsafe acts of the pilots and preconditions for unsafe acts. The impact of organizational influences and unsafe supervision is lower than the previously discussed categories. To address these areas, an improved flight training and safety culture will be required.

The reliability of the study is limited by (a) coding being performed by a single individual (as a result of limited reports) and (b) the reports coming from different countries and investigative authorities each with different methodology and experience in accident investigation and human factors. Considering the limitations of the study, future research is recommended on human factors as contributory factors in accidents occurring in South Africa.

Conclusion To achieve a reduction in Africa’s high accident rate, it will require the collective effort of the operators, the individual Ó 2016 Hogrefe Publishing

37

states, their respective regulatory authorities, and the international aviation industry. This research sought to identify the contributory human factors to the selected accidents.

The selection of the three countries’ accident data aimed to capture the diversity of African aviation. The limited number of accident reports for Kenya and Nigeria shows a need for African countries to follow South Africa’s lead in having a robust aviation safety organization that investigates and documents the accidents. The low number of accident reports from Kenya and Nigeria is a limitation for the study and therefore is considered an initial step in understanding human factors as contributors to accidents in Africa. A larger dataset and multiple coders should be used in future research.

The application of the HFACS methodology retrospectively to the aviation accident reports retrieved from the Kenyan, Nigerian, and South African authorities facilitated the identification of the human factors contributing to the accidents. Human factors contributed to 55 of the 72 (76%) of the accidents analyzed, consistent with previous studies that attributed the same to 70–80% of accidents occurring in the industry. Skill-based errors were found to be the most common in South Africa, CRM in Nigeria, and violations in Kenya. Although the accidents exhibited different human factor classifications, especially in the unsafe acts by pilot operators’ level, it was noted that adverse weather played a major role in the accidents. Pilots either made the decision to perform operations in adverse weather or were unable to manage the aircraft when they found themselves in it. Addressing the unsafe acts by pilots through training, the application of risk management techniques, and enhancing the application of CRM techniques can facilitate the reduction in Africa’s accident numbers.

References Accident Investigation Bureau, Nigeria. (2014). Reports and publications. Retrieved from http://www.aib.gov.ng/publication. php Gaur, D. (2005). Human factors analysis and classification system applied to civil aircraft accidents in India. Aviation, Space, and Environmental Medicine, 76, 501–505.

Hooper, B. J., & O’Hare, D. P. (2013). Exploring human error in military aviation flight safety systems using post-incident classification systems. Journal of Aviation, Space and Environmental Medicine, 84(8), 803–813. doi: 10.3357/ ASEM.3176.2013 International Civil Aviation Organization. (2008). Skills shortage in aviation fields in Africa. Special Africa-Indian Ocean (AFI) regional air navigation meeting, Durban, South Africa (Report SP AFI/08-WP/35).

International Civil Aviation Organization. (2013). State of global aviation safety. Retrieved from http://www.icao.int/safety/ State%20of%20Global%20Aviation%20Safety/ICAO_SGAS_ book_EN_SEPT2013_final_web.pdf

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Kioko, H. K. (2013). Speech notes, Aviation suppliers and stakeholders’ convention 2013. Retrieved from http://www.afraa.org/ index.php/mediacenter/publications/events/91-keynoteaddress-on-behalf-of-colonel-rtd-hilary-k-kioko-directorgeneral-kenya-civil-aviation-authority-kcaa/file Krabacher, T., Kalipeni, E., & Layachi, A. (2011). Africa: Global studies (13th ed.). New York, NY: McGraw-Hill.

Lenné, M. G., Ashby, K., & Fitzharris, M. (2008). Analysis of general aviation crashes in Australia using the Human Factors Analysis and Classification System. International Journal of Aviation Psychology, 18, 340–352.

Ministry of Transport and Infrastructure, Kenya. (2014). Air accident investigation reports. Retrieved from http://www. transport.go.ke/AirReports.html O’Hare, D., Wiggins, M., Batt, R., & Morrison, D. (1994). Cognitive failure analysis for aircraft accident investigation. Ergonomics, 37, 1855–1869.

Reason, J. (1990). Human error. New York, NY: Cambridge University Press.

Republic of Cameroon. (2007). Technical investigation into the accident of the B737-800 registration 5Y-KYA operated by Kenya Airways that occurred on the 5th of May 2007 in Douala (Decision No. 099/PM). Douala, Cameroon: Author.

Rinefort, F. C., & Petrick, J. A. (2012). The challenge of managing safety in Africa. International Journal of Business and Social Science, 3, 19–23.

Shappell, S., & Wiegmann, D. (1997). A human error approach to accident investigation: The taxonomy of unsafe operations.

International Journal of Aviation Psychology, 7, 269–291.

Shappell, S. A, & Wiegmann, D. A. (2000). The Human Factors Analysis and Classification Systems – HFACS, (Report DOT/FAA/ AM-00/7). Washington, DC: Government Printing Office.

Shappell, S., Detwiler, C., Holcomb, K., Hackworth, C., Boquet, A., & Wiegmann, D. (2007). Human error and commercial aviation accidents: An analysis using the human factors analysis and classification system. Human Factors, 49, 227–242.

South African Civil Aviation Authority. (2014). Accident and incident reports. Retrieved from http://www.caa.co.za/Pages/Accidents %20and%20Incidents/Aircraft-accident-reports.aspx Tsiige, A (2009). The Africa regional review on transport (Report for UNECA). Retrieved from http://www.uneca.org/fssdd/.../AfricaRegionalReviewReport-on-Transport.pdf U.S. Government Accountability Office. (2009). International aviation: Federal efforts help address safety challenges in Africa, but could benefit from assessment and better coordination,

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(Report GAO-09-498). Washington, DC: Government Printing Office.

Van Dyke, D. (2006, May). Management commitment: Cornerstone of aviation safety culture. Paper presented at The John Molson School of Business Concordia University. Montreal, CA. Wiegmann, D., & Shappell, S. (2001a). Human error perspectives in aviation. International Journal of Aviation Psychology, 11, 341–357.

Wiegmann, D. A., & Shappell, S. A. (2001b). A human error analysis of commercial aviation accidents using the Human Factors Analysis and Classification System (HFACS) (Report DOT/FAA/ AM-01/3). Washington, DC: Government Printing Office.

Received April 17, 2015 Revision received January 22, 2016 Accepted January 22, 2016 Published online May 3, 2016 Isaac Munene Embry-Riddle Aeronautical University 600 South Clyde Morris Blvd.

Daytona Beach FL 32114 USA Tel. +1 (817) 280-5163 E-mail munenei@my.erau.edu

Isaac Munene (M. Aeronautical Science, Av. Safety and Management) is a reliability and maintainability engineer at Bell Helicopter Textron International in Fort Worth, TX, and a PhD candidate in Aviation (Aviation Safety and Human Factors) at Embry-Riddle Aeronautical University. He has experience in aircraft system safety.

Ó 2016 Hogrefe Publishing

APAHF in Practice

Characteristics of General Aviation Accidents Involving Male and Female Pilots Robert O. Walton1 and P. Michael Politano2 Embry-Riddle Aeronautical University – Worldwide, Berlin, Germany

1 2

The Citadel, The Military College of South Carolina, Charleston, SC, USA

Abstract: Studies examining aviation accidents have not found differences in accident rates by gender, although there may be gender differences in the types of accident. One study examined accident rates of male and female private pilots and found that males were more likely to have accidents related to inattention or poor planning while female pilots were more likely to have accidents due to mishandling of the aircraft. This research analyzed the National Transportation Safety Board’s (NTSB) aviation accident database system to examine the severity of injury and aircraft damage in general aviation accidents by gender. The data indicated that female pilots have accidents with higher aircraft damage and personnel injury rates at lower levels of training and experience compared with male pilots, but they then have significantly fewer accidents compared with male pilots at higher levels of experience.

Keywords: aviation, gender, NTSB

General aviation (GA, 14 U.S. Code of Federal Regulation [CFR] Part 91) is defined as “the operation of civilian aircraft for purposes other than commercial, passenger, [or cargo] transport, including personal, business, and instructional flying” (Bazargan & Guzhva, 2011, p. 962), and accounts for 94% of all air-related accidents in the United States with a fatality rate of 1.31 per 100,000 flight hours (Li & Baker, 2007; Shao, Guindani, & Boyd, 2014). Shao et al. (2014) noted that this is 82 times higher than the rate for commercial air carriers, whose accident rates have dramatically declined over the last decade.

The causes of aviation accidents can be categorized into three broad categories, human, environment, and aircraft, with human error making up 95% of the accidents (Houston, Walton, & Conway, 2012). Since the majority of accidents are caused by human error, this has been the focus of most published research. Aircraft crash rates based on the gender of GA pilots are similar to motor vehicle gender crash rates, which reflects the higher fatality rates of male drivers and pilots (Baker, Lamb, Grabowski, Rebok, & Li, 2001). Baker et al. (2001) suggested that males are more likely to take risks than females are, a difference existing between males and females from grade school to college.

Previous studies have not supported, generally, any real differences in abilities between male and female pilots.

For example, an examination of intelligence as measured Ó 2016 Hogrefe Publishing

by the adult Wechsler scale among Air Force pilots did not find any significant differences by gender (Kratz, Poppen, & Burrourghs, 2007). Other studies examining aviation accidents have not found differences in accident rates by gender (Bazargan & Guzhva, 2011; Caldwell & LeDuc, 1998; McFadden, 1996; Mitchell et al., 2005; Puckett & Hynes, 2011; Vail & Ekman, 1986), although there may be gender differences in the types of accident as suggested

by Baker et al. (2001).

The study by Baker et al. (2001) examined both male and female pilots born between 1933 and 1942 and examined aircraft accidents of this sample between 1983 and 1997. The sample consisted of older mature pilots who have different flying experience than younger less experienced pilots. The Baker et al. (2001) study manually coded the accidents based on National Transportation Safety Board (NTSB) reports, and worked with a limited dataset. Baker and colleagues (2001) found that in examining accident rates of private pilots, males were more likely to have accidents related to inattention or poor planning (e.g., ignoring weather conditions, taking unnecessary risks) while female pilots were more likely to have accidents due to mishandling the aircraft (e.g., panic maneuvers, ignoring the kinetics of the aircraft), with the type of accident related to the severity of injury and the amount of damage to the aircraft.

Famed female pilot Amelia Earhart had an accident due to mishandling an aircraft when she ground looped her Aviation Psychology and Applied Human Factors (2016), 6(1), 39–44 DOI: 10.1027/2192-0923/a000085

40

Lockheed Electra on take-off on March 20, 1937, at Ford Island field in Hawaii on her first attempt to fly around the world. Once the aircraft was repaired she tried once again to fly around the world, but vanished over the Pacific Ocean. When comparing pilot error accidents by phase of operations, Vail and Ekman (1986) found that females had lower accident rates (and lower fatality rates) than males in all areas except for taxiing. Although suggested in jest (at least we hope), Vail and Ekman (1986) stated that “perhaps for greater safety there should be two pilots in every cockpit, a male for taxiing and a female for every other phase of operation” (p. 302).

In a more recent study, Bazargan and Guzhva (2011) examined the impact of gender, age, and experience of GA pilots involved in aviation accidents. Using the publicly available NTSB’s aviation accident database system, called the Enhanced Accident Data Management System (eADMS), Bazargan and Guzhva (2011) found that less experienced pilots are more likely to make an error that causes an accident, but found no difference with respect to gender in the likelihood of being involved in an accident.

Bazargan and Guzhva (2011) also found that while less experienced pilots are more likely to be involved in an accident, the likelihood of pilots to be involved in a fatal accident increased with pilot experience. The authors confirmed Vail and Ekman’s (1986) findings that GA female pilots are less likely to be involved in fatal accidents than their male counterparts.

Fatal accidents would, in general, also accompany more damage to an aircraft. A study conducted by the General Aviation Safety Council (2010) found that pilots with under 100 hr are more likely to have a fatal accident than a minor accident; between 100 and 1,000 hr, fatal and minor accidents are about the same, while fatal accidents diminish for pilots with over 1,000 hr, in contrast to findings by Bazargan and Guzhva (2011).

GA accident studies as a whole have one flaw, in that the majority of studies “aggregate all 14 CFR Part 91 operations inclusive of pilots holding various licenses as well as trainees” (Shao, Guindani, & Boyd, 2014, p. 371). Similarly, in many studies “accidents for single and multiple engines are grouped despite the fact that the latter carry an increased risk of fatality” (Shao et al., 2014, p. 371). While this study does not satisfy the first limitation noted by Shao et al., we did examine accidents divided by number of engines.

This study analyzed the publically available NTSB aviation accident database system (eADMS) to examine the severity of injury and aircraft damage in GA accidents by gender. The objective of this study was to determine whether male and female pilots differ in the severity of injury during a crash and the amount of damage to the aircraft. The research question for this project was: Are there Aviation Psychology and Applied Human Factors (2016), 6(1), 39–44

R. O. Walton & P. M. Politano, Characteristics of Aircraft Accidents

gender differences in the severity of injury and aircraft damage in GA accidents?

This study extends and updates the research by Baker et al. (2001) and Bazargan and Guzhva (2011) on the characteristics of GA accidents based on gender. The Baker et al. study was limited to 144 crashes involving mature male and female pilots from a birth cohort of pilots born between 1933 and 1942. Bazargan and Guzhva (2011) examined accidents between 1983 and 2002 found in the NTSB database at that time. The NTSB database used in this study contained over 74,000 records of aviation accidents and incidents between 1982 and 2014.

Method Participants This research utilized the NTSB aviation accident and incident database system (eADMS) from 1982 to 2014 to examine differences in the severity of accidents by gender. The NTSB data set uses very strict coding instructions, which are published, for each accident (damage to plane or injury to individuals) and incident (all other events) using a briefof-accident or brief-of-incident format. Each brief includes NTSB findings, which are coded by sequence of events such as occurrences, phases of flight, weather conditions, sky conditions, age and gender of pilot, etc. Of particular interest in these data for this study were number of flight hours for the pilots, degree of damage to the plane (none, minimal, substantial, destroyed), and degree of bodily injury (none, minor, serious, fatal; NTSB, 1998). All data in the database were substantiated information that was documented in the factual report or was available in other accessible documents or publications (NTSB, 1998).

There were 74,686 entries in the database. For this study, commercial (14 CFR part 121 and 135) operations were excluded as were home-built aircraft and gliders, leaving a total of 61,312 events. There were 56,284 (96.09%) male pilots and 2,287 (3.91%) female pilots. The average age for males pilots was 45.35 years (SD = 14.49) and for female pilots, 39.06 (SD = 13.74). There was a statistically significant age difference, t(57,829) = 20.22, p < .001. Consistent with the studies by Bazargan and Guzhva (2011) and Li et al. (2003) the variable for age was divided into six categories: younger than 20 years, 20–29, 30–39, 40–49, 50–59, and older than 60 years.

The mean number of flight hours for male pilots was 2,844.04 (SD = 4,977, sk = 3.59). For female pilots, the mean was 1,305.13 (SD = 3395.52, sk = 6.57). Because of skew, the difference between male and female pilots’ flight hours was examined using a Mann–Whitney U test and was Ó 2016 Hogrefe Publishing

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Table 1. Experience categories (CAT I, II, III, IV, V) by damage levels for male and female pilots expressed as percentages FAR Part 61 Categories

CAT I Male/Female

CAT II Male/Female

CAT III Male/Female

CAT IV Male/Female

CAT V Male/Female 28.8/18.2

No damage

7.6/21.8*

10.9/32.7*

36.4/21.8*

16.4/5.5*

Minor damage

9.4/17.3

10.4/19.2*

29.9/42.7

19.2/15.4

31.1/15.4*

13.6/32.2*

16.0/21.1*

38.7/33.6*

15.6/5.5*

16.1/5.5*

7.6/16.7*

15.8/21.5*

41.2/42.1

17.8/13.3*

16.6/6.4*

Substantial damage Aircraft destroyed

Notes. *Significance of the difference in proportion, z > 1.96, p < .05.

significant, U(58,571) = 4.31, p = .001 (median hours for male pilots was 879, for female pilots, 280).

The total number of flight hours was used as an indicator of pilot experience and divided into five categories based on FAR Part 61 guidance, again consistent with Bazargan and Guzhva (2011). The five pilot experience categories where: Category I (new pilots) for pilots that had 99 or fewer hours of total flight time, Category II (moderate experience) for pilots with 100–299 hrs of flight time, Category III (fairly experienced) for pilots with flight times between 300 and 1,999 hrs, Category IV (very experienced) for pilots with flight times between 2,000 and 4,999, and Category V (most experienced) for pilots with more than 5,000 hrs.

Differences between male and female pilots were analyzed using the test for significance of a difference between two independent proportions. Proportional comparisons were used to compensate for the disparity in the number of male versus female pilots.

Results Damage to the plane for males and females combined was examined across FAR Part 61 experience levels. Experience Category (CAT) III had the highest level of incidents/ accidents resulting in no damage to the aircraft (34.6%) with CAT I the lowest at 8.3%. CAT V had the highest level of minor damage to the aircraft (30.8%) with CAT I the lowest at 9.8%. For substantial damage, CAT III was the highest (38.3%) with CAT I again the lowest at 14.3%.

And for destruction of the aircraft, CAT III was again the highest (41.2%) with CAT I the lowest at 7.9%.

Injuries were also examined across experience levels for males and females combined. CAT III had the most incidents/accidents with no injuries (36.7%), with minor injuries (41.3%), with serious injuries (40.9%), and with fatalities (41.6%). CAT III was the lowest for incidents/accidents with no injuries (15.4%), and CAT I was the lowest for minor injuries (11.8%), serious injuries (9.9%), and fatalities (6.8%).

When injury was examined by gender without regarding the experience level of the pilots, females had significantly higher rates than males (60.7% and 56.8%, respectively) of

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incidents/accidents reported with no injuries (z = 3.718, p < .05). Females again had significantly higher rates than males of incidents/accidents where minor injuries were reported (17.3% and 14.3%, respectively, z = 3.92, p < .05) and, while not significant, of serious injuries (10.3% and 10% respectively, z = .36, p > .05). Females, however, had significantly lower rates than males of incidents/accidents with reported fatalities (11.7% and 18.8% respectively, z = 8.59, p < .05).

Damage to the aircraft was then examined by gender as shown in Table 1, based on FAR Part 61 experience levels.

Compared with male pilots, female pilots had a significantly higher proportion of incidents/accidents that did not result in any damage to the aircraft through CAT II; female pilots were significantly lower than male pilots for CAT III and IV, and lower, but not significantly, for CAT V. Nearly the same pattern was seen for female pilots regarding incidents/ accidents that resulted in minor damage to the aircraft – a significantly higher proportion of female pilots than male pilots for CAT II, higher but not significantly so for CAT III, lower but not significantly so for CAT IV, and significantly lower for CAT V. This pattern continued for incidents/ accidents where substantial damage to the aircraft was reported – a significantly higher proportion of female pilots than male pilots for CAT I–CAT III, but significantly lower than male pilots for CAT IV and V. A significantly higher proportion of female pilots than male pilots was seen for destruction of the aircraft at CAT I and II, a higher proportion but not significantly so for CAT III, and a significantly lower proportion than male pilots for CAT IV and V. The same pattern was seen when genders were examined for injury by FAR Part 61 experience levels as shown in Table 2. Female pilots had a significantly higher rate of incidents/accidents with no injuries at CAT I and II (34.0 and 20.3%, respectively) compared with male pilots (14.9 and 15.9%, respectively). For CAT III, as significantly lower proportion of female pilots than male pilots was seen in the no-injury grouping (31.9 and 37.1%, respectively) and this continued through CAT IV and V for no injuries (female pilots at 7.8 and 6%, respectively, compared with male pilots at 15.6 and 16.5%, respectively). A significantly higher proportion of female pilots than male pilots was found for CAT I and II in incidents/accidents with minor injuries, serious injuries, and fatalities. For CAT III, there was a

Aviation Psychology and Applied Human Factors (2016), 6(1), 39–44

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R. O. Walton & P. M. Politano, Characteristics of Aircraft Accidents

Table 2. Experience categories (CAT I, II, III, IV, V) by injury levels for male and female pilots expressed as percentages FAR Part 61 Categories

CAT I Male/Female

CAT II Male/Female

CAT III Male/Female

CAT IV Male/Female

CAT V Male/Female

No injury

14.9/34.0*

15.9/20.3*

37.1/31.9*

15.6/7.8*

16.5/6.0*

Minor injury

10.9/31.3*

16.3/19.9*

41.8/36.4*

15.1/7.3*

15.9/5.1*

Serious injury

9.6/16.27*

15.8/30.2*

41.2/37.0

17.1/9.8*

16.4/6.8*

Fatality

6.6/13.8*

15.0/21.3*

41.7/43.3

18.0/13.4

18.7/8.2*

Notes. *Significance of the difference in proportion, z > 1.96, p < .05.

significantly lower proportion of female pilots than male pilots in incidents/accidents with no injuries and minor injuries, and lower, but not significantly so, in severe injuries and fatalities. For CAT IV and CAT V, there was a significantly lower proportion of female pilots than male pilots in the rate of incidents/accidents with minor and severe injuries and fatalities with the exception of fatalities in CAT IV – the proportion of females was lower than males, but not significantly so.

Since aircraft complexity may be a factor, gender differences were examined for aircraft with two engines and retractable landing gear. Unfortunately, the number of female pilots dropped beyond the level of statistical analysis when both conditions were imposed. When the data were filtered only for two engines, sufficient numbers were returned in most experience categories for analysis. There was a significantly higher proportion of female pilots than male pilots in substantial damage at CAT I (1.23 and .56%, z = 1.97, p < .05). For CAT III, there was a significantly lower proportion of female pilots than male pilots on substantial damage (14.81 and 23.12%, respectively, z = 4.55, p < .05) and destruction of the aircraft (12.56 and 27.92%, respectively, z = 4.90, p < .05). There were no significant differences between female and male pilots at any damage level in CAT IV. For CAT V, there was a significantly higher proportion of female pilots than male pilots on substantial damage (53.79 and 45.70%, respectively, z = 3.71, p < .05) and destruction of the aircraft (56.74 and 38.66%, respectively, z = 5.23, p < .05).

When two-engine aircraft were examined by gender for level of injury, there was a higher proportion of female pilots than male pilots in CAT III for incidents/accidents with no injuries (1.79 and 13.49%, respectively, z = 2.60, p < .05). There was again a significant difference between male and female pilots for minor injuries at CAT III with male pilots being higher (18.12 and 10.10%, respectively, z = 2.01, p < .05). The only other significant differences were with fatalities in CAT III, with higher proportions of male pilots than female pilots (29.29 and 14.77, respectively, z = 4.76, p < .05) and in CAT V with higher proportions of female pilots than male pilots (54.74 and 38.52, respectively, z = .485, p < .05).

Additional analyses indicated that female pilots in CAT III and above were significantly more likely to fly under visual flight rules (VFR) conditions than male pilots were Aviation Psychology and Applied Human Factors (2016), 6(1), 39–44

(z = 9.73, male pilots = 42.2% under VFR, female pilots = 66.4% under VFR). Female pilots in CAT III and above were significantly more likely to fly under clear weather conditions than male pilots were (z = 2.19, male pilots flying in clear conditions = 61.1%, with female pilots = 63.6%). Moreover, female pilots in CAT III and above were significantly more likely to fly under daylight conditions than male pilots were (z = 6.77, male pilots flying under daylight conditions = 86.9%, female pilots = 91.7%).

Male pilots, proportionally, were significantly more likely to fly multi-engine aircraft compared with female pilots (z = 2.87, male pilots flying multi-engines = 16.8% and female pilots = 13.5%). Male pilots, proportionally, were significantly more likely to fly retractable landing gear aircraft compared with female pilots (z = 4.98, male pilots flying retractable gear = 35.5% and female pilots = 28.3%). There was no significant proportional difference in filing flight plans between male and female pilots in CAT III and above (z = .486, male pilots = 21.9% and female pilots = 22.6%).

Discussion Using the large NTSB eADMS database, differences in aircraft accidents for FAR Part 91 pilots were examined.

Previous research has not supported, generally, any real differences between male and female pilots particularly in accident rates by gender (Bazargan & Guzhva, 2011; Caldwell & LeDuc, 1998; Mitchell et al., 2005; Puckett & Hynes, 2011; Vail & Ekman, 1986). However, in this study, differences were found when experience was taken into account, with female pilots significantly higher in accidents at lower experience levels as compared with male pilots, and female pilots significantly lower in accidents at higher experience levels when compared with male pilots. Some research has suggested that these differences are the result of gender – that is, males are likely to be impulsive, take risk, and be less likely to plan the flight as compared with female pilots (Baker et al., 2001; Jonas, 2001). This research did not clarify aircraft accident types by gender as suggested by Baker et al. (2001).

These data seem to suggest that female pilots at higher levels of experience do fly under better conditions, that is, Ó 2016 Hogrefe Publishing

R. O. Walton & P. M. Politano, Characteristics of Aircraft Accidents

clear skies, VFR conditions, during daylight hours, and during minimal weather, but not significantly more so than male pilots. By contrast, male pilots at higher levels of experience fly more complicated aircraft with multi-engine and/or retractable landing gear. There does seem to be some difference between male and female pilots regarding damage or injury when twin-engine aircraft are examined.

However, there does not seem to be the clear gender trend noticed in the general data and, indeed, the results seem more sporadic and without a clear pattern. Finally, the trend in the data suggest, in accordance with Baker et al., that female pilots tend to engage in less risky behavior.

The stereotypic beliefs identified by Mitchell et al. (2005) that females are less capable than males at all levels do not appear to be supported by this research, with some equivocation that could be entertained relative to twin-engine aircraft. The twin-engine aircraft data need further exploration to see if a discernable pattern emerges. Current analysis of twin-engine aircraft was hampered by small numbers that precluded any examination of some categories of damage and injury by experience level.

Conclusion and Limitations Heinrich’s triangle, using the analogy of an iceberg model, indicates that for every aviation accident there are potentially many more incidents, hazardous conditions, and unsafe acts that could have occurred, but by happenstance did not become an accident. For every accident, there could be as many as 30 incidents, 300 hazardous conditions, and potentially over a 1,000 unsafe acts (Strachan, 2015). While the actual number of incidents, hazardous conditions, and unsafe acts can never be known, the fact is that the number of recorded aviation accidents is only the tip of the iceberg, with many unsafe acts that lurk under the water’s surface.

Researchers must continue to dig into the details of known accidents so as to understand why they happened and to develop processes, along with the aviation industry, to reduce any unsafe acts that could lead to an aviation accident. As part of this understanding, gender differences in aviation accidents need to be explored so that corrective measures and training can be tailored to these differences.

One limitation of this study is that it is based on archival data that could have errors in input over the course of the collection period. Additionally, the large disparity between the number of male and female pilots placed some constraints on analysis techniques. Because of the limited number of female pilots in the commercial aviation industry who are involved in accidents, this study was limited to GA accidents.

Future research might focus on the age of pilots and the interaction of age and flying hours as an indicator of Ó 2016 Hogrefe Publishing

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experience. In addition, future research should examine causes and phase-of-flight factors that might contribute to accidents.

References Baker, S. P., Lamb, M. W., Grabowski, J. G., Rebok, G., & Li, G. (2001). Characteristics of general aviation crashes involving mature male and female pilots. Aviation, Space, and Environment Medicine, 72(5), 447–452.

Bazargan, M., & Guzhva, V. S. (2011). Impact of gender, age and experience of pilots on general aviation accidents. Accident Analysis and Prevention, 43, 962–970. doi: 10.1016/j.aap.

2010.11.023 Caldwell, J. A., & LeDuc, P. A. (1998). Gender influences on performance and recovery sleep in fatigued aviators. Ergonomics, 41, 1757–1770.

Aviation Safety Council. (2010). A study of fatal stall or spin accidents to UK registered light aeroplanes 1980 to 2008. Kent, UK: Author.

Houston, S. J., Walton, R. O., & Conway, B. A. (2012). Analysis of general aviation instructional loss of control accidents.

Journal of Aviation/Aerospace Education and Research, 22(1), 35–49.

Jonas, G. (2001, July). Report on aviation accidents lets the stereotypes fly. Kingston Whig – Standard. Retrieved from http://search.proquest.com.ezproxy.libproxy.db.erau.edu/docview/ 352841044?accountid=27203 Kratz, K., Poppen, B., & Burrourghs, L. (2007). The estimated fullscale intellectual abilities of U.S Army aviators. Aviation, Space, and Environment Medicine, 78, 261–267.

Li, G., Baker, S., Grabowski, J., Qiang, Y., McCarthy, M., & Rebok, G. (2003). Factors associated with pilot error in aviation crashes.

Aviation, Space, and Environment Medicine, 72(1), 52–58.

Li, G., & Baker, S. P. (2007). Crash risk in general aviation. Journal of American Medical Association, 297, 1596–1598.

McFadden, K. L. (1996). Comparing pilot-error accident rates of male and female airline pilots. International Journal of Management Science, 24(4), 443–450.

Mitchell, J., Kristovics, A., Vermeulen, L., Wilson, J., & Martinussen, M. (2005). How pink is the sky? A cross-national study of the gendered occupation of pilot (Unpublished manuscript). University of Western Sydney, Australia.

National Transportation Safety Board (NTSB). (1998). Aviation coding manual for the Enhanced Accident Data Management System (eADMS) database. Retrieved from http://www.ntsb.gov Puckett, M., & Hynes, G. E. (2011, March). Feminine leadership in commercial aviation: Success stories of women pilots and captains. Paper presented at the Academic and Business Research Institute International Conference. Nashville, TN. Shao, B. S., Guindani, M., & Boyd, D. D. (2014). Causes of fatal accidents for instrument-certified and non-certified private pilots. Accident Analysis and Prevention, 72, 370–375. doi: 10.1016/j.aap.2014.07.013 Strachan, I. (2015, January). Learning from mistakes. Aerospace.

32–33.

Vail, G. J., & Ekman, L. G. (1986). Pilot error accidents: Male vs female. Applied Ergonomics, 17(4), 297–303. doi: 10.1016/ 0003-6870(86)90133-X Received June 21, 2015 Revision received October 5, 2015 Accepted November 8, 2015 Published online May 3, 2016

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R. O. Walton & P. M. Politano, Characteristics of Aircraft Accidents

Robert Walton Embry-Riddle Aeronautical University – University Kurfürstenstraße 56 10785 Berlin Germany E-mail waltonr@erau.edu

Bob Walton (PhD) is executive director of European operations for Embry-Riddle Aeronautical University – Worldwide (Berlin, Germany). He holds a PhD in business administration, a master of aeronautical sciences, and an MBA. He has published in numerous academic journals, authored books on air cargo operations, and presented his work at multiple professional conferences.

Aviation Psychology and Applied Human Factors (2016), 6(1), 39–44

Mike Politano (PhD, MPS, ABPP) is a professor of psychology at The Citadel (Charleston, SC). He holds an undergraduate degree from Duke University, a master’s degree and PhD from Indiana University in school psychology, a postdoctoral degree from Indiana University and the Medical College of Virginia in clinical child psychology.

Ó 2016 Hogrefe Publishing

Emotional Processing Scale The Emotional Processing Scale (EPS) is a 25-item questionnaire designed to identify emotional processing styles and potential deficits.

Developed over the course of 12 years by an experienced team, the EPS is for use by clinicians working in mental health, psychological therapy and health psychology, as well as researchers interested in the emotional life of healthy individuals and other populations.

fies Identi onal emoti yles ing st s s e c pro ial otent and p ts defici

The EPS can be used to: • Identify and quantify healthy and unhealthy styles of emotional processing.

• Assess the contribution of poor emotional processing to physical, psychosomatic and psychological disorders.

• Provide a non-diagnostic framework to assess patients for research or therapy.

• Measure changes in emotions during therapy/counselling.

• Assist therapists in incorporating an emotional component into their formulations of psychological therapy.

Want to see more? Watch our EPS video: www.hogrefe.co.uk/eps.html

"An invaluable tool... an essential contribution to the modern and holistic understanding of the mind-body paradigm."

Dr Birgit Gurr, Consultant Clinical Neuropsychologist, Dorset HealthCare University NHS Foundation Trust

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Hogrefe House Albion Place Oxford, OX1 1QZ +44 (0)1865 797920 clinical@hogrefe.co.uk www.hogrefe.co.uk

How to meet people’s increasing need to develop and manage their own lives and careers “A must read for all career development professionals and students alike. The Handbook of the Life Design is a new and essential resource for those working to improve career services in line with today’s challenges and conditions.”

Sara Santilli, PhD, writing in Career Convergence, February 2015

Laura Nota / Jérôme Rossier (Editors)

Handbook of Life Design

From Practice to Theory and from Theory to Practice 2015, vi + 298 pp., hardcover US $54.00 / € 38.95 ISBN 978-0-88937-447-8 Also available as eBook Our lives and careers are becoming ever more unpredictable. The “lifedesign paradigm” described in detail in this ground-breaking handbook helps counselors and others meet people’s increasing need to develop and manage their own lives and careers.

Life-design interventions, suited to a wide variety of cultural settings, help

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individuals become actors in their own lives and careers by activating, stimulating, and developing their personal resources. This handbook first addresses life-design theory, then shows how to apply life designing to different age groups and with more at-risk people, and looks at how to train life-design counselors.

News and Announcements The Best Paper Award Aviation Psychology and Applied Human Factors (APAHF) ISSN-Print 2192-0923 ISSN-Online 2192-0931 Aviation Psychology and Applied Human Factors (APAHF) is the peer-reviewed scientific journal of the European Association for Aviation Psychology (EAAP), in cooperation with the Australian Aviation Psychology Association (AAvPA) and published by Hogrefe Publishing, Göttingen, Germany.

The journal APAHF invites innovative, original, high quality papers from researchers and practitioners addressing all psychological/human factors aspects of the aerospace domain. Aviation Psychology and Applied Human Factors is abstracted/indexed in PsycINFO, PSYNDEX, and Academic Index.

The new annual APAHF Best Paper Award, introduced in 2015, recognizes excellence in aviation psychology and human factors research of a scientific paper, research note, or practitioner paper submitted to Aviation Psychology and Applied Human Factors which was accepted for publication in the respective year. The winner will be selected by the Editor-in-Chief and the Associate Editors of APAHF. The award will be formally conferred at a special session at the bi-annual EAAP Conference. The winning paper will be published in the APAHF journal and the first author will

Ó 2016 Hogrefe Publishing

be invited to present the paper in a Keynote Speech at the EAAP Conference.

Eligibility Any researcher is eligible to submit relevant work. Membership in EAAP or AAvPA is not required. Each manuscript accepted for publication in APAHF in 2016 will be considered for the Best Paper Award 2016. Submissions must cover original, unpublished research and comply with the manuscript submission instructions. Manuscripts should be submitted via the journal e-mail address: journal@eaap.net.

Ioana Koglbauer Editor-in-Chief, Aviation Psychology and Applied Human Factors Contact: journal@eaap.net André Droog EAAP President Contact: president@eaap.net

Aviation Psychology and Applied Human Factors (2016), 6(1), 45 DOI: 10.1027/2192-0923/a000097

News and Announcements Meeting Report How Safe is Your Change? Safety and Validation Workshop in Budapest Katalin Nanaí CRDS, Budapest, Hungary

In December last year, the Centre of Research, Development and Simulation of HungaroControl (CRDS, Figure 1) organized a 2-day Safety and Validation Workshop in order to examine the links between validating procedures before their implementation and maintaining the number one priority in ATM-Safety.

The aim of the thought-provoking event was also to provide a common platform for representatives to discuss recent tendencies regarding Safety – partners from Slovakia, Romania furthermore Bosnia and Herzegovina exchanged insights as for the role of simulation and validation in their change management process (see Figure 2).

maintenance of safe ATM operations. The takeaway message of the roundtables was clear: a professional – reliable and valid – human performance evaluation is therefore indispensable to understand if the tested system or procedure is safe and ready to implement. The evaluation also requires the appropriate experts’ – Human Factors specialists and subject matter experts – common contribution throughout an ATM life cycle.

Adding even more value to the event, guests were also provided with a hands-on experience from the recently initiated Controller-Pilot Data-Link Communications

“The solution of safety case-related problems arising from the increasing intensity and complexity of air traffic is considered as one of the primary tasks of national and international aviation safety professionals. An improved validation process has to be based on formalisation of safety by a modelling process and has to able to define and measure risk level quantitavely rather than on the qualitative risk assessment opinions.” explained Mihály Kurucz, Head of Safety & Quality Department of HungaroControl.

Katalin Nánai from CRDS then presented various methods to evaluate Human Factors performance when introducing a new, freshly designed procedure – prior to its implementation. It has been settled that Human Factors (e.g., ATCO situational awareness, workload or the ability to adapt to new procedures) might act as a contributor but if left unattended, also as a possible hinder in the

Figure 1. CRDS logo.

Aviation Psychology and Applied Human Factors (2016), 6(1), 46–47 DOI: 10.1027/2192-0923/a000094

Figure 2. Participants from Slovakia, Romania, and Bosnia and Herzegovina ANSPs together with CRDS expert team from HungaroControl.

Ó 2016 Hogrefe Publishing

News and Announcements

(CPDLC) operations at HungaroControl’s Test and Training Facility (TTF). The program also included an introduction to HungaroControl’s remote TWR (r-TWR) concept by Gyula Hangyál, ATM Director of HungaroControl.

To learn more about the Centre of Research, Development and Simulation, please visit http://www.crds.eu.

Katalin Nanaí CRDS Igló u. 33-35 1185 Budapest Hungary Tel. +36 1 2934679 E-mail Katalin.nanai@hungarocontrol.hu

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Katalin Nánai is a Human Factors Analyst at the Centre of Research, Development and Simulation at HungaroControl, Budapest. She is responsible for identifying, considering and studying Human interactions, issues and benefits during an Air Traffic Management project throughout its lifecycle. Some of her major interests are: Human performance analysis in a simulated environment, recruitment, fatigue risk management, HF workshops, researches and accident investigations.

Aviation Psychology and Applied Human Factors (2016), 6(1), 46–47

News and Announcements Meeting Report The 3rd European STAMP Workshop, Amsterdam, The Netherlands Nektarios Karanikas and Robert J. de Boer Amsterdam University of Applied Sciences/Aviation Academy, Amsterdam, The Netherlands

Traditional system safety approaches are being challenged by the introduction of new technology and the increasing complexity of the systems we build and operate. System Theoretic Accident Model and Processes (STAMP) is a new systems thinking approach that was introduced in Nancy Levesonâ&#x20AC;&#x2122;s book Engineering a Safer World (MIT Press, 2012). While relatively new, the STAMP approach and its respective tools and methods are already being used in many industry sectors such as space, aviation, automotive, software, security medical, defence, and nuclear. In more than 150 published articles, it has been demonstrated that the STAMP family of tools have the potential to identify more hazards and causal factors than traditional tools (e.g., FTA, HAZOP, FMEA).

The Aviation Academy of the Amsterdam University of Applied Sciences, in collaboration with the MIT Partnership for a Systems Approach to Safety, organized the 3rd European STAMP Workshop, which took place October 4â&#x20AC;&#x201C;6, 2015, and followed similar successful meetings in Germany â&#x20AC;&#x201C; in Braunschweig (2013) and Stuttgart (2014). The workshop featured keynote speeches by Sidney Dekker (Griffith University, Brisbane, Australia) and John Thomas (MIT), as well as a program covering recent developments in the application of STAMP models and associated tools and relevant research. The workshop was attended by about 90 delegates from academia, industry and various authorities.

In addition to participants from European countries, there were speakers and participants from the USA, Canada, and Japan (Figure 1).

Scientific Program On Sunday, October 4, three consecutive tutorials introduced various STAMP tools and methods in a friendly Aviation Psychology and Applied Human Factors (2016), 6(1), 48â&#x20AC;&#x201C;50 DOI: 10.1027/2192-0923/a000098

and informal atmosphere. This included practice sessions demonstrating their use both on paper and via a software tool. These highly interactive tutorials were aimed at participants who wanted to familiarize themselves further with the theory and implementation of STAMP. The tutorials were delivered by instructors with substantial professional experience and a research background in STAMP. John Thomas â&#x20AC;&#x201C; standing in for Nancy Leveson who was unfortunately unable to travel â&#x20AC;&#x201C; opened the 1st day of the workshop with a comprehensive update on the latest developments in STAMP and presented MITâ&#x20AC;&#x2122;s research in the occupational safety & health field. Sidney Dekker delivered a thought-provoking speech that provided a trigger for participants to reconsider their views on safety as compliance to procedures and bureaucratic accountability. In addition to the two renowned keynote speakers, the workshop included 22 presentations from academia and industry.

Here, a wide range of research results and case examples were communicated.

The variety of topics and the quality of presentations kept the interest of attendants high during the two full workshop days. In addition to applications of STAMP in various domains, several speakers presented their own approaches and adaptations in the use of the methodology to serve the needs of the industry. Below we mention some indicative application cases and research results that were presented and led to further discussions: Measurement of a systemâ&#x20AC;&#x2122;s awareness provision capability, which was demonstrated with the Ă&#x153;berlingen aviation accident as a case example.

The use of STAMP in the investigation of Dutch railway accidents.

Development and certification of nuclear software based on STAMP. Assessment of the design of single-seat interception aircraft training based on the STAMP method. Ă&#x201C; 2016 Hogrefe Publishing

News and Announcements

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The need for cooperation of STAMP researchers and practitioners in the frame of European funded projects.

The requirement for more research and applications of STAMP to cyber-security.

The need to consider human factors in STAMP studies more consistently and transparently.

Which models of human and software behaviors might be used as references in the investigation of accidents?

Increased complexity in the case of multi-controllers in the STAMP control structure diagram.

Figure 1. Participants from numerous countries around the world met at the 3rd European STAMP workshop.

The need to supplement the design of safety constraints with an understanding of social dimensions of the working environment, as a result of a 2-year case study in aircraft ground handling services.

Identification of operational hazards of unmanned aerial vehicles, which is becoming a growing safety concern of modern societies.

The development of a safety management systems evaluation tool based on STAMP. STAMP-based control and monitoring of maritime operations.

Verification of software and tools that support the application of STAMP. In addition to the presentations, two plenary discussions provided the participants with an opportunity to address questions and to share their perspectives on organizational transformation and traditional probabilistic models.

Those sessions revealed some interesting points connected with the future of STAMP: What are the criteria for assessing the completeness, consistency, and correctness of STAMP results? To what depth must a STAMP analysis reach and how can the validity of the control structures be evaluated?

Ă&#x201C; 2016 Hogrefe Publishing

The feedback received during and after the workshop was very encouraging and indicative of the high quality of the event. The participants valued the selection of topics and speakers, the scheduling and time management, the convenient location and the pleasant venue, and the great networking opportunities during the breaks and the conference dinner in the centre of Amsterdam.

Following the successful 3rd European STAMP workshop, the program committee selected 11 of the contributions for inclusion in a special issue of Procedia Engineering. These open access papers are available at: http://www.sciencedirect. com/science/journal/18777058/128

STAMP Workshop 2016 The next European STAMP workshop will be held in Switzerland in September 2016, hosted by the Zurich University of Applied Sciences. We strongly believe that human factors and ergonomics scientists and practitioners can share knowledge and experience with the STAMP community to mutual benefit, and invite you to attend this workshop.

Nektarios Karanikas Amsterdam University of Applied Sciences Weesperzijde 190 P.C. 1090 DZ Amsterdam The Netherlands Tel. +31 6 2115-6287 E-mail n.karanikas@hva.nl Robert de Boer Amsterdam University of Applied Sciences Weesperzijde 190 P.C. 1090 DZ Amsterdam The Netherlands Tel. +31 6 2115-6269 E-mail rj.de.boer@hva.nl

Aviation Psychology and Applied Human Factors (2016), 6(1), 48â&#x20AC;&#x201C;50

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News and Announcements

Nektarios Karanikas (MSc, DProf) is Associate Professor of Safety and Human Factors in the Aviation Academy of Amsterdam University of Applied Sciences. His research interests include assessment of safety management performance, development of safety performance metrics, and evaluation of human performance through technology.

Aviation Psychology and Applied Human Factors (2016), 6(1), 48â&#x20AC;&#x201C;50

Robert de Boer (MSc, PhD) is Professor of Aviation Engineering in the Aviation Academy of Amsterdam University of Applied Sciences. His research interest focuses on human performance and process improvements in complex environments.

Ă&#x201C; 2016 Hogrefe Publishing

News and Announcements Meetings and Congresses Advanced Human Factors in Aviation Safety Training Course, Dubai, UAE May 15–19, 2016 Contact: Brent Hayward, Dédale Asia-Pacific, Sydney, Australia. E-mail bhayward@dedale.net, Web http:// www.eaap.net/read/2982/advanced-human-factors-in-aviation-safety.html The course is kindly hosted by Emirates Airlines. Previous attendance at an initial EAAP HF in Flight Safety course is a prerequisite. For detailed information, please contact Brent Hayward.

Initial Human Factors in Flight Safety Training Course, Barcelona, Spain May 23–27, 2016 Contact: Brent Hayward, Dédale Asia-Pacific, Sydney, Australia. E-mail bhayward@dedale.net, Web http:// www.eaap.net/read/2981/initial-human-factors-in-flightsafety.html The experienced team of Rob Lee, Kristina Pollack and Brent Hayward will present this applied training course.

The course is kindly hosted by Vueling Airlines. For detailed information, a brochure is available for download at the above URL. A/NZSASI 2016 Australasian Seminar, Brisbane, Australia June 3–5, 2016 Contact: Paul Mayes, Australian Society of Air Safety Investigators, E-mail asasiexecutive@gmail.com, Web http:// www.asasi.org/ The Australian Society of Air Safety Investigators (ASASI) was formed in 1978 after an inaugural meeting held in Melbourne, Victoria. ASASI was formed to better serve and represent the views of Air Safety Investigators in Australia. Since then ASASI has grown to a membership of 150 plus and now hosts a biennial conference in conjunction with the New Zealand Society of Air Safety Investigators (NZSASI). ASASI is affiliated with ISASI. 6th International Conference on Traffic and Transport Psychology, Brisbane, Australia August 2–5, 2016 Contact: QUT Conferences, Event Manager – Alanna Hankey, E-mail icttp2016@qut.edu.au, Web http:// icttp2016.com

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With a theme of “Taking Traffic and Transport Psychology to the World”, this conference will feature a strong program of keynote speakers, oral and poster presentations, workshops and symposia.

The Conference will be a global forum at which all those involved in traffic and transport psychology, human factors, cognition and behaviours, road safety research, policy, education, enforcement and injury prevention, can meet with researchers, academics, and professionals to discuss and present on the latest work being undertaken in these areas.

HCI-Aero 2016 – International Conference on Human– Computer Interaction in Aerospace, Paris, France September 14–16, 2016 Contact: HCI-Aero 2016 Organizing Committee, Delilah Caballero, Florida Institute of Technology, Melbourne, FL, USA, E-mail dcaballe@fit.edu, Web http://research.fit. edu/hci-aero/HCI-Aero2016 The HCI-Aero (Human-Computer Interaction in Aerospace) conference series have been found by Dr Guy Boy in cooperation with ACM-SIGCHI, the International Ergonomics Association and the Air and Space Academy.

HCI-Aero conferences followed the Human-Machine Interaction and Artificial Intelligence in Aerospace conference series. HCI-Aero conferences have become a major reference in the field of HCI in Aerospace (full papers are indexed in the ACM Digital Library).

HFES 2016 International Annual Meeting, Washington DC, USA September 19–23, 2016 Contact: HFES/Annual Meeting, Santa Monica, CA, USA, E-mail lois@hfes.org, Web https://www.hfes.org//Web/ HFESMeetings/meetings.html HFES Annual Meetings are important events for the Society's members and others who are interested in the latest developments in the field. The Society's mission is to promote the discovery and exchange of knowledge concerning the characteristics of human beings that are applicable to the design of systems and devices of all kinds.

32nd EAAP Conference, Cascais, Portugal September 26–30, 2016 Contact: European Association for Aviation Psychology (EAAP), E-mail secretarygeneral@eaap.net, Web http:// conference.eaap.net

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EAAP organizes biannual conferences to encourage EAAP members and guests to share their latest findings, data and experience in academia, research and applied aviation psychology. The theme of the 32nd Conference will be: “Thinking, High and Low: Cognition and Decision Making in Aviation”.

For details please visit the above website.

2nd International Human Factors Conference by Lufthansa Flight Training October 20–21, 2016 Contact: Lufthansa Flight Training, Martin Egerth, E-mail human-factors-conference@lft.dlh.de, Web http://www. human-factors-conference.com Following the great success and positive feedback of the 1st Lufthansa Flight Training Conference in 2015, the organizers are looking forward to the next Human Factors Conference.

For program and further details please visit the above website.

HFES 2017 International Annual Meeting, Austin, TX, USA October 9–13, 2017 Contact: Human Factors and Ergonomics Society, Web https://www.hfes.org//Web/HFESMeetings/upcoming.html

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International Symposium on Aviation Psychology, USA May 2017 Contact: Wright State University, Dayton, OH, USA, Web https://isap.wright.edu/front The International Symposium on Aviation Psychology is convened for the purposes of: presenting the latest research on human performance problems and opportunities within aviation systems; envisioning design solutions that best utilize human capabilities for creating safe and efficient aviation systems; and bringing together scientists, research sponsors, and operators in an effort to bridge the gap between research and application.

Although the symposium is aerospace oriented, we welcome anyone with basic or applied interests in any domain to the extent that generalizations from or to the aviation domain are relevant.

HFES 2018 International Annual Meeting, Philadelphia, PA, USA September 30–October 5, 2018 Contact: Human Factors and Ergonomics Society, Web https://www.hfes.org//Web/HFESMeetings/upcoming.html

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News and Announcements Aviation Human Factors Related Industry News1

Airline Safety Record Improved in 2015

For further information, please see: http://www.avweb.com/ avwebflash/news/Report-Germanwings-Pilot-Practiced-On-PriorFlight-224038-1.html, http://www.avweb.com/avwebflash/news/ Egypt-Refuses-to-Concede-Terrorism-in-Russian-Crash-225365-1. html, and http://www.avweb.com/avwebflash/news/TriganaATR42-Crashes-In-Papua-Indonesia-224701-1.html.

In 2015, 560 people were killed in 16 commercial aviation accidents worldwide, the Aviation Safety Network reported this week. Overall, it was the lowest number of fatal crashes ever, and it was the fifth-safest year for fatalities, according to ASN. The five worst crashes all had “at least a contributing cause of human,” according to an analysis by Jacdec, a German research firm. In March, a pilot deliberately crashed an Airbus A320 in the French Alps, killing all 150 on board, and a terrorist bomb is the suspected cause in the crash of a Russian jet carrying 224 people in October. The other worst commercial aircraft crashes in 2015 were an ATR-42 in Indonesia, killing 54; an ATR-72 in Taiwan with a death toll of 43; and the loss of an Antonov An-12 in the Sudan, killing 25. The year showed dramatic improvement from 970 deaths in the “disastrous previous year,” when two widebody jets crashed with the loss of all on board, said Jacdec.

Over the long term, analysis shows a shift away from technical causes and toward the human factor, Jacdec said.

Given the estimated worldwide air traffic of 34 million airline flights in 2015, the accident rate was 1 fatal accident per 4,857 million flights, according to ASN. “Since 1997 the average number of airliner accidents has shown a steady and persistent decline,” said ASN, “for a great deal thanks to the continuing safety-driven efforts by international aviation organizations such as ICAO, IATA, Flight Safety Foundation, and the aviation industry.”

BRAVO GOLF AVIATION announces a NEW HUMAN FACTORS REFRESHER COURSE. Robert “Bob” Gould, President of Bravo Golf Aviation is now offering a 1-day, 8-hour Human Factors refresher course.

“Aviation Maintenance Human Factors Refresher” includes a review of human performance, then takes the technician into specific areas of aircraft maintenance such as situational awareness, technical communication, complacency, distractions, failure to follow procedures, normalization of deviance in the workplace, fatigue, and fatigue risk management for the individual technician. The course closes with a brief review of the Federal Air Rules applicable to technicians and how these rules affect human performance.

This course is taught on-site at a customer’s location, and is FAA accepted for 8 hours of IA renewal training. This course may also be divided into two 4-hour segments. Bob Gould has over 47 years of aviation experience, is a certificated A&P, commercial pilot, and a retired Naval Aviation Maintenance Officer. In addition to teaching his 2-day “Practical Aviation Risk Management” course (NBAA PDP approved and FAA IA accepted 8 hours), he is an accredited IBAC IS-BAO auditor and instructs part-time at the University of Southern California’s Aviation Safety and Security Program.

Reprinted with permission from AVweb. Original story appeared January 4, 2016 at http://www.avweb.com/avwebflash/news/ Airline-Safety-Record-Improved-In-2015-225476-1.html

For further information, contact Bob Gould at (413) 3203977, email him at bravogolf-aviation@earthlink.net, or visit his website at www.bravogolfaviation.com.

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New Human Factors Refresher Course

Parts of this section are compiled from “Aviation Human Factors Industry News” and reproduced with permission of Roger Hughes.

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Aviation Psychology and Applied Human Factors (2016), 6(1), 53–55 DOI: 10.1027/2192-0923/a000095

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Birds, Fuel Systems Cited As Helo Hazards Two recent news reports have cited bird strikes and fuel-fed fires as safety concerns for helicopter operators. According to an Associated Press story published over the weekend, operators reported 204 helicopter bird strikes in 2013, a 68% increase from 2009.

While some of that is due to increased reporting by pilots, the AP says there has also been an increase in the U.S. of large birds, like Canada geese and turkey vultures, that can do significant damage to an aircraft.

“We’re getting more severe damage, more frequent cases of birds penetrating the windshield, and the risk of pilot incapacitation that could cause fatalities for everybody there,” said Gary Roach, an FAA helicopter safety engineer, at a recent FAA meeting. Roach and his colleagues have urged the FAA to establish an industry committee to examine the helicopter/ bird-strike issue, the AP said. Another recent report, by NBC News affiliate KUSA in Denver, examined the incidence of fuel-fed fires in the crashes of medevac helicopters. According to KUSA, the fuel systems in many older helicopters are not well-protected in crashes, and while the FAA requires stronger systems in helicopters certified since 1994, it hasn’t required aircraft that were certified under the older rules to change.

According to the NTSB, more than 4,700 of the 5,600 helicopters manufactured since 1994 don’t have fuel systems that would meet the 1994 FAA standards, since they were copies of helicopters that were certified earlier than 1994.

The NTSB issued a safety recommendation in July that urged the FAA to mandate crash-resistant fuel systems for all new helicopters, regardless of the date of certification. “Between 1994 and 2013, the NTSB has investigated at least 135 accidents in the United States involving certificated helicopters of various models that resulted in a postcrash fire. Those accidents resulted in 221 fatalities and 37 serious injuries. Only three of the accident helicopters that experienced post-crash fire had crash-resistant fuel systems and crashworthy fuel tanks,” the NTSB wrote (PDF). The KUSA report said it could find only one post-crash fire report involving military helicopters, which have long had crash-resistant fuel systems. “We’ve seen it in the military,” NTSB chairman Christopher Hart told KUSA. “We want to see similar progressive action taken in civilian helicopters.”

Airbus officials responded to KUSA that the true impact of postcrash fires on survivability is “not well understood.”

Overall, helicopter crash statistics have been improving.

According to the International Helicopter Safety Team, Aviation Psychology and Applied Human Factors (2016), 6(1), 53–55

News and Announcements

during the first 6 months of 2015, total accidents in the U.S. were down 28% compared to the same period last year; compared to 2006, the number of accidents has been cut nearly in half.

For further information, see the two reports at the following links: http://bigstory.ap.org/article/68afacf4b18041b7ab0a24e36 68626be/dangeroushelicopter-bird-strikes-rise-faa-warns, http:// www.ntsb.gov/safety/safety-recs/recletters/A-15-012.pdf.

NASA Talk Examines How Math Located Air France 447 Wreckage On June 1, 2009, Air France Flight 447 with 228 passengers and crew disappeared over the Atlantic while on a flight from Rio de Janeiro to Paris. About 2 years after the loss of the aircraft and four intensive searches, a group of statisticians was able to predict almost the exact location of the wreckage. Searchers found it within a week. On Tuesday, February 2, at NASA’s Langley Research Center in Hampton, Virginia, J. Van Gurley presented “Bayesian Search for Air France 447: The Math that Found a Needle in a Haystack” at 2 p.m. in the Pearl Young Theater. Gurley is with Metron, Incorporated, whose Advanced Mathematics Applications Division is credited with producing the analysis that found the aircraft.

Bayesian statistics is a set of mathematical rules for using new data to continuously update an existing knowledge base.

A well-developed method for planning searches for missing aircraft, ships lost at sea, or people missing on land, Bayesian search theory combines all the available information about the location of a search object. This is important in one-of-a-kind searches where there is little or no statistical data to rely upon.

The theory has been applied successfully to searches for the missing nuclear submarine USS Scorpion and the 1,857 shipwreck of the SS Central America. It is used routinely by the U.S. Coast Guard to find people and ships missing at sea.

Gurley’s talk presents the basic elements of the theory and how it was used to locate the wreck of Air France flight 447 after two years of unsuccessful search. He will finish with a discussion of the current search for Malaysian Air flight 370 in the Indian Ocean, describing what is known and how the Bayesian approach could be used to guide search efforts.

As a senior manager at Metron in the District of Columbia, Gurley leads a number of research and development efforts in predictive analytics, data fusion, and mission planning for the Defense Advanced Research Projects Agency, Office Ó 2016 Hogrefe Publishing

News and Announcements

of Naval Research, and Federal Aviation Administration.

Prior to joining Metron, he completed a 26-year career in the United States Navy rising to the rank of captain while serving as a submarine warfare officer, and naval meteorology and oceanography specialist. His education includes a Bachelor of Science in physics from the University of Florida,

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and a Master of Science and engineering degrees in ocean engineering awarded jointly by the Massachusetts Institute of Technology and Woods Hole Oceanographic Institute.

For more information about NASA Langley’s Colloquium and Sigma Series Lectures, visit: http://colloqsigma.larc.nasa.gov.

Aviation Psychology and Applied Human Factors (2016), 6(1), 53–55

Instructions to Authors Aviation Psychology and Applied Human Factors (APAHF) publishes innovative, original, high-quality applied research covering all aspects of the aerospace domain. In order to make the journal accessible to both practitioners and scientific researchers, the contents are broadly divided into original scientific research articles and papers for practitioners.

The fully peer-reviewed articles cover a variety of methodological approaches, ranging from experimental surveys to ethnographic and observational research, from those psychological and human factors disciplines relevant to the field, including social psychology, cognitive psychology, and ergonomics. High-quality critical review articles and meta-analyses cover particular topics of current scientific interest. APAHF in Practice consists of shorter, less technical, but still fully peer-reviewed articles covering a wide range of topics, such as comments on incidents and accidents, innovative applications of aviation psychology, and reviews of best practices in industry.

Aviation Psychology and Applied Human Factors publishes the following types of articles: Original Articles, Research Notes, APAHF in Practice, Book Reviews, News and Announcements.

Manuscript Submission: Manuscripts for any section must be sent to the journal’s e-mail address journal@eaap.net and addressed to the Editor in Chief, Ioana Koglbauer (PhD). Should you have any technical queries please contact Michaela Schwarz at admin@eaap.net. Detailed instructions to authors are provided at http://www.hogrefe.com/periodicals/journal-of-individualdifferences/advice-for-authors/ Copyright Agreement: By submitting an article, the author confirms and guarantees on behalf of him-/herself and any coauthors that the manuscript has not been submitted or published elsewhere, and that he or she holds all copyright in and titles to the submitted contribution, including any figures, photographs, line drawings, plans, maps, sketches, tables, and electronic supplementary material, and that the article and its contents do not infringe in any way on the rights of third parties.

ESM will be published online as received from the author(s) without any conversion, testing, or reformatting. They will not be checked for typographical errors or functionality. The author indemnifies and holds harmless the publisher from any thirdparty claims.

The author agrees, upon acceptance of the article for publication, to transfer to the publisher the exclusive right to reproduce and distribute the article and its contents, both physically and in

nonphysical, electronic, or other form, in the journal to which it has been submitted and in other independent publications, with no limitations on the number of copies or on the form or the extent of distribution. These rights are transferred for the duration of copyright as defined by international law. Furthermore, the author transfers to the publisher the following exclusive rights to the article and its contents: 1. The rights to produce advance copies, reprints, or offprints of the article, in full or in part, to undertake or allow translations into other languages, to distribute other forms or modified versions of the article, and to produce and distribute summaries or abstracts.

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Hogrefe OpenMind: Information about the open access publishing program Hogrefe OpenMind, including the article processing fee and the Creative Commons license under which the article will then be published, are given at http://www.hogrefe.com/ openmind.

Online Rights for Journal Articles: Guidelines on authors’ rights to archive electronic versions of their manuscripts online are given in the Advice for Authors on the journal’s web page at www. hogrefe.com.

April 2016

How to be more persuasive and successful in negotiations “Presented in a concise and even entertaining style, this book succeeds in demonstrating how to negotiate successfully and fairly at the same time. A clear recommendation.”

Heinz Schuler, PhD, Hohenheim University, Stuttgart, Germany

Marco Behrmann

Negotiation and Persuasion

The Science and Art of Winning Cooperative Partners 2016, viii + 128 pp.

US $34.80 / € 24.95 ISBN 978-0-88937-467-6 Also available as eBook Scientific research shows that the most successful negotiators analyze the situation thoroughly, self-monitor wisely, are keenly aware of interpersonal processes during the negotiation – and, crucially, enter negotiations with a fair and cooperative attitude. This book is a clear and compact guide on how to succeed by means of such goal-oriented negotiation and cooperative persuasion. Readers learn models to understand and describe what takes place during negotiations, while numerous figures, charts, and checklists clearly summarize effective

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strategies for analyzing context, processes, competencies, and the impact of our own behavior. Real-life case examples vividly illustrate the specific measures individuals and teams can take to systematically improve their powers of persuasion and bargaining strength. The book also describes a modern approach to raising negotiation competencies as part of personnel development, making it suitable for use in training courses as well as for anyone who wants to be a more persuasive and successful negotiator.

New, insightful theory and research concerning reactance processes Topics covered include • Reactance theory in association with guilt appeals • Tests to study the relationship between fear and psychological reactance • The influence of threat to group identity and its associated values and norms on reactance • Benefit of reactance research in health psychology campaigns • Construction and empirical validation of an instrument for measuring state reactance (Salzburger State Reactance Scale) • Motivation intensity theory and its implications for how reactance motives should convert into effortful goal pursuit

Sandra Sittenthaler / Eva Jonas / Eva Traut-Mattausch / Jeff Greenberg (Editors)

New Directions in Reactance Research (Series: Zeitschrift für Psychologie – Vol. 223) 2015, iv + 76 pp., large format US $49.00 / € 34.95 ISBN 978-0-88937-479-9 Psychological reactance theory, formulated by Jack Brehm in 1966, is one of the most popular social psychological theories explaining how people respond to threats to their free behaviors and has attracted attention in both basic and applied research in areas such as health, marketing, politics, and education.

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A review article published 40 years later by Miron and Brehm pointed out several research gaps. That article inspired the editors to develop this carefully compiled collection presenting recent research and developments in reactance theory that both offer new knowledge and illuminate issues still in need of resolution.

SEPTEMBER 14-16

HCIAERO 2016

International Conference on Human-Computer Interaction in Aerospace

DGAC Conference Center, Paris, France

http://research.Ã&#x17E;t.edu/hci-aero/HCI-Aero2016

EAAP32 32nd International Conference of the European Association for Aviation Psychology

Thinking, High and Low Cognition and Decision Making in Aviation Mental Health panel, Collaborative Decision Making workshop

26 - 30 September 2016 Hotel Quinta da Marinha, Cascais, Portugal

conference.eaap.net