The effect of management practices on aircraft incidents

Journal of Air Transport Management 84 (2020) 101784

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The effect of management practices on aircraft incidents €nmez *, Suat Uslu Kadir Do Eskis¸ehir Technical University, Air Traffic Control Department, Turkey

A R T I C L E I N F O

A B S T R A C T

Keywords: Aircraft incidents Proactive approach Human factors Management HFACS

Many models have been put forward in order to examine the human factors in aircraft accidents and incidents. Human Factors Analysis and Classification System (HFACS) which is the most widely used in literature is one of these models. HFACS is based on Reason’s Swiss Cheese Model. The biggest disadvantage of the Reason’s model is its post-accident applicability. Mostly HFACS aviation applications are usually based on accident data. This is a reagent (result-focused) approach. In this study, however, HFACS which is an improved version of Reason’s model, was applied to aircraft incidents that did not result in an accident. This is a proactive approach. Thus, with this approach, the biggest disadvantage of Reason’s model is turned into an advantage. In addition, a realistic application of this approach has been demonstrated in this study, focusing on aircraft incidents that took place between 2000 and 2018. The year 2000 forms a milestone in the manufacture of more technically advanced aircraft models which significantly reduced occurrence of technical errors in aircrafts, hence the choice of 2000 as base year. A total of 328 aircraft incident reports from the National Transportation Safety Board (NTSB) database were studied and among these reports cockpit crew related incidents were analyzed using HFACS. As a result of the analyzes, the root causes of incidents have been identified. In addition, unlike tradi­ tional HFACS analysis, the relationship between errors occurred at management levels of HFACS and the unsafe acts of the cockpit crew in aircraft incidents was statistically revealed.

1. Introduction During the analysis of an aircraft accident or incident, if the cause is a technical factor, it can be detected based on the data obtained from flight data recorder, voice recorder or from other sources with certain tests (fatigue test etc.). These causes can be entered into the accident or incident database in a well-defined way. For example; pilot tube mal­ function or engine failure. This facilitates the work of aviation author­ ities to ensure that post-accident database analyzes are carried out in a methodical way. However, if the accident or incident was caused by human factors, it will be more difficult to find out the exact cause, in this case, the analysis of the accident or incident will be more intuitive rather than data-based. Moreover, it would have been impossible to make advanced tests as in the case of mechanical failures. In addition, the expressions entered into the accident database will not be well defined. As a result, this will not provide useful, convenient, data for aviation authorities when performing subsequent data analysis (Shappell and Wiegmann, 2003). For all the above reasons, a comprehensive frame­ work is needed to analyze human factors. If all analyzes are carried out using an advanced framework, the expressions entered in the database

will be well defined and useful. The Human Factor Analysis and Clas­ sification System (HFACS) was developed by Shappell and Wiegmann to meet this need (Wiegmann and Shappell, 2001b). 1.1. Research problem and specificity of study According to the International Civil Aviation Organization (ICAO), the only way to prevent accidents is to analyze previous accidents and incidents (ICAO, 2010). If the aviation industry and other authorities want to reduce accident or incident rates in future, they should focus on human factors as a plausible cause. However, increasing the amount of money and the resources invested in investigating human factors will not be the solution, indeed the necessary resources are already being allocated in this regard. What needs to be done, however, is to make a comprehensive analysis of human factors that cause accidents or in­ cidents by systematically focusing on existing accident and incident data (Wiegmann and Shappell, 2001b). As a result, it is necessary to use a model that is efficient and widely used in literature in these analyzes. It has been emphasized that HFACS is the most efficient and useful model in accident analysis (Scarborough

* Corresponding author. E-mail address: [email protected] (K. D€ onmez). https://doi.org/10.1016/j.jairtraman.2020.101784 Received 25 October 2018; Received in revised form 10 February 2020; Accepted 11 February 2020 Available online 24 February 2020 0969-6997/© 2020 Elsevier Ltd. All rights reserved.

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Journal of Air Transport Management 84 (2020) 101784

et al., 2005). The applications of HFACS in aviation and other sectors are shown in Table 1; As we have stated in Table 1, HFACS aviation applications are usu­ ally based on accident data. This is a reagent (result-focused) approach. Today, however, the safety management system is defined as an activity that attempts to analyze and mitigate risks before accidents happen (SHGM, 2012). As can be understood from this description, safety in­ vestigations must be carried out with a proactive and predictive approach. HFACS is the improved form of Reason’s model, the greatest deficiency of which is its post-accident applicability. This disadvantage can be turned into an advantage by applying HFACS to aircraft incidents rather than accident. A study of HFACS applied to aircraft incidents will be more innovative in terms of approaches to safety. In addition, Airbus reported that the total commercial flight traffic between 2000 and 2015 was double the traffic in previous years. Also, it emphasized that the aircraft used after 2000 were highly technologically advanced planes that reduced the rate of accidents resulting from CFIT (Controlled Flight into Terrain) and LOC (Loss of Control) by 70–80% (Airbus, 2017). Therefore, between these dates, the problems caused by the aircraft were minimized and the importance of investigating the human factors between these dates has increased. When all the above-mentioned conditions are considered, it is necessary to carry out HFACS analysis for aircraft incidents which occurred after 2000 to achieve a more realistic approach. This will be important in order to realize the dangers that human beings experience together and to fulfill the necessities of today’s safety approaches. Thus, it can be said that the year 2000 is the milestone for accident in­ €nmez, 2018 provides a detailed analysis of human fac­ vestigations. Do tors in aircraft incidents which occurred 2000–2016. Our current study €nmez, 2018). is based on this master thesis study (Do The purpose of this study was determined as reveal the management effect in the incidents by describing the associations between HFACS causal factors from organizational influences to the condition of flight operators observed in the incidents, considering the above-mentioned shortcomings and requirements. Therefore, HFACS applied to aircraft incidents which occurred between 2000 and 2018.

and data analysis tool for the US Navy. Since its first design, HFACS has been applied to more than 1000 military aviation accidents. HFACS, increases the quality and quantity of the data collection process while increasing the efficiency of data-driven research strategies (Wiegmann and Shappell, 2001b). HFACS consists of 4 levels and 19 causal sub­ categories. Level 1 is the unsafe acts that cause accidents directly. Level 2 is the preconditions for the unsafe acts that make up the underlying causes of accidents. Level 3 is unsafe supervision, which contains most of the hidden errors. Level 4 is the organizational influences that are typically ignored by accident analysts. HFACS framework is shown in Fig. 1; Levels of HFACS and causal factors are described below. The first of these levels is unsafe acts which are most closely associated with incidents. 2.1.1. Unsafe acts The first level to analyze is the bottommost level – unsafe acts. Un­ safe acts are divided into two groups; errors and violations. While errors are defined as unsafe acts that occur within rules, violations are defined as deliberate ignorance of the rules (Shappell and Wiegmann, 2000). Errors are examined in three causal subcategories: � Decision errors � Skill based errors � Perceptual errors Decision errors are the most common error types. In this type of error, the planned process progresses as desired but the plan is made incor­ rectly at first. Contrary to decision errors, skill-based errors occur in sit­ uations that do not require any thought. For example; movements such as steering control or gear shifting when using a car are automated be­ haviors. Perceptual errors are as important as the other two error groups. This type of error occurs when perceptual input decreases or when un­ usual environmental factors (such as nighttime conditions, bad weather conditions etc.) decrease perception. Violations are examined in two causal subcategories: � Routine violations � Exceptional violations

2. Analytical framework 2.1. Human factor analysis and Classification System (HFACS)

Routine violations are habitual behaviors which usually allowed by the system or administrations. A typical example of this situation, which is also referred to as stretching the rules, is driving at a speed of 55 km on a road with a speed limit of 50 km. Moving over the speed limit 5 km may be a condition permitted by law. Exceptional violations can be described as situations that are in con­ flict with the authorities. The most typical example for these violations is driving at a speed of 120 km on a road with a speed limit of 50 km. Neither authority nor laws allow this. A police officer who sees this situation will certainly impose a penalty on the person (Wiegmann and Shappell, 2001a).

The origin of HFACS is based on Reason’s Swiss Cheese Model. HFACS was originally designed as an accident and incident investigation Table 1 Applications of HFACS. Scope Commercial aviation accidents General aviation accidents Military aviation accidents Maintenance-related accidents Helicopter accidents Air traffic control related accidents Unmanned aerial vehicle accident Railway accidents Maritime accidents Health and Medicine Mining accidents

Studies (Wiegmann and Shappell, 2001b), (Wiegmann and Shappell, 2001a), (Wiegmann and Shappell, 2001c), (Shappell et al., 2006), (Shappell et al., 2007), (W. Li et al., 2008), (ATSB, 2007), (Ting and Dai, 2011) (Shappell and Wiegmann, 2003), (Wiegmann et al., 2005), (Lenn� e et al., 2008), (Daramola, 2014) (Shappell and Wiegmann, 2004), (Li and Harris, 2005), (Li and Harris, 2006b), (Li and Harris, 2006a), (Olsen and Shorrock, 2010), (O Connor and Walker, 2011) (Thaden et al., 2007), (Rashid, 2010), (Rashid et al., 2010) Liu et al. (2013) (Pape et al., 2001), (Broach and Dollar, 2002), ( Scarborough et al., 2005) Yesilbas and Cotter (2014)

2.1.2. Preconditions for unsafe acts This category is the second level of HFACS. Focusing on only the unsafe acts is like focusing on body heat instead of the underlying causes of a patient with fever (Wiegmann and Shappell, 2001a). To analyze the preconditions for unsafe acts, researchers need to conduct further research. Preconditions for unsafe acts are examined in three classes within HFACS. Environmental factors are examined in two causal subcategories;

(Reinach and Viale, 2006), (Zhan et al., 2017), (Baysari et al., 2008), (Baysari et al., 2009) (Hinrichs et al., 2011), (Akyuz and Celik, 2014), (Celik and Cebi, 2009), (Bilbro, 2013) (Cintron, 2015), (Diller et al., 2014) Patterson and Shappell (2010)

� Physical environment � Technological environment. Physical environment, which significantly affects the performance of the cockpit crew, has numerous limitations on team performance. 2

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Journal of Air Transport Management 84 (2020) 101784

Fig. 1. HFACS framework (Shappell and Wiegmann, 2003).

Factors such as temperature, noise, vibration, external forces, light, etc. are examined in this class. The technological environment factor has entered aviation literature with the rapid development of technology in recent years. This factor includes the design, display characteristics and automation of any hardware and controls that may affect the perfor­ mance of the cockpit crew (Villela, 2011). Condition of operators includes three causal subcategories:

� Personal readiness Crew resource management category includes deficiencies in the crew’s cockpit and non-cockpit (with air traffic controllers or ground personnel etc.) communications. This category also includes situations where the crew cannot work together in harmony. Personal readiness category includes violations of rest periods, violations of alcohol re­ strictions, self-medication, dieting etc. (Wiegmann and Shappell, 2001a).

� Adverse mental states � Adverse physiological conditions � Physical and mental limitations

2.1.3. Unsafe supervision It is clear that the cockpit crew is responsible for their own actions. However, there are many examples in which managers’ errors and hidden errors trigger the unsafe acts of the crew. Hidden errors are mostly caused by the management level. Unsafe supervision includes four causal subcategories;

Adverse mental states include harmful attitudes that may adversely affect decisions such as a loss of situational awareness, mental fatigue, circadian rhythm disorder, excessive self-confidence, complacency or poor motivation. Adverse physiological conditions this category includes physiological factors that are important for aviation, such as spatial disorientation, poisoning, visual anomalies, insomnia, and medical or chemical abnormalities affecting performance (Wiegmann and Shappell, 2001a). Physical and mental limitations include the conditions that exceed the limit of the individual in the control of the aircraft. For example; people’s visual perception drops significantly at night. When perception decreases while driving a car in the dark at night, additional measures can be taken such as slowing down. In aviation, slowing down is not an option. In such cases, more attention to basic flight instruments will be a measure that increases safety. However, if necessary precautions are not taken, the results can be catastrophic as pilots will have difficulty seeing other aircrafts or obstacles (Shappell and Wiegmann, 2000). Personal factors are examined in two causal subcategories;

� � � �

Inadequate supervision Planned inappropriate operations Failed to correct problem Supervisory violations

Inadequate supervision category contains errors in the chain of administrative command affecting the attitudes and actions of the su­ pervisors. Managers should demonstrate appropriate and necessary at­ titudes towards the individual, such as providing adequate training, providing professional guidance, and conducting organizational lead­ ership. Planned inappropriate operations; Often, operational tempo or work calendars are planned which adversely affect an individual’s performance due to financial concern. Inappropriate crew matching, non-allocation of appropriate rest times for the crew, and failure to

� Crew resource management 3

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manage risk in specific tasks are examined under this causal factor. Failed to correct problem category includes such situations where, although the problems for the individual, the hardware or the relevant safety areas are known by the managers, the operations continue without corrective action. Supervisory violations category includes situ­ ations such as the deliberate violation of rules or regulations by man­ agers. For example; allowing personnel without a valid license or certificate to fly can be the beginning of a chain of events that will lead to disasters (Wiegmann and Shappell, 2001a).

3.2. Data coding and analysis The version of HFACS framework introduced in Shappell and Wiegmann (2003) was used in this study as a classification framework (see Fig. 1) (Shappell and Wiegmann, 2003). Among the 328 aircraft incident reports obtained from the NTSB database, coding has been applied to the 74 aircraft incident reports related to the cockpit crew. 3.2.1. Reliability analysis Coding was achieved by two experienced academicians according to the presence or absence of each HFACS category. If any causal factor was observed in any incident, it was encoded as “1” and if not observed as “0”. After the coding was completed, the independent ratings were compared. The inter-rater reliability was determined as a simple per­ centage. As a result, overall compliance between the coders was measured as 94.14%. In cases where disputes existed, consensus was achieved by bringing together relevant experts and results were included in the database. Compatibility between coders was measured by a simple percentage and kappa coefficients. Table 2 shows the results of the inter-rater reliability tests; The Kappa coefficient is a rate calculated from symmetric cross ta­ bles with a row ¼ column. The Kappa coefficient determines the interrater reliability coefficient between two observers who evaluate a situ­ ation or event at the same time. The Kappa coefficient varies from 1 to þ1. A value of 0 indicates inconsistency, and a value of þ1 indicates positive full compliance. If Kappa coefficient (κ);

2.1.4. Organizational influences Incorrect decisions by higher-level management can directly affect the practices at mid management level, or even the conditions or movements of the cockpit team. Unfortunately, organizational in­ fluences are often overlooked by even the best accident investigators and are not reported. Organizational influences are examined in three causal subcategories: � Resource management � Organizational climate � Organizational process Resource management category includes the management, allocation and maintenance of organizational resources. The resource management category also includes topics such as human resource management (elimination, training, staffing), budget management and hardware design. Organizational climate category is generally defined as how the organization behaves towards individuals. It can also be expressed as any kind of change in the organization that affects the performance of the individual. It includes formal accountability, order of the command chain, assignment of authorities and responsibilities, communication channels etc. Organizational process category covers topics such as the formal processes (operational tempo, time pressure, work calendars), methods (performance standards, objectives, instructions on methods) and oversight within the organization (organizational work, risk man­ agement, safety program implementation and preparation). Each of the deficiencies in management and the decisions by the upper levels can negatively affect the performance of the cockpit crew and system safety indirectly (Wiegmann and Shappell, 2001a).

� � � � � �

Table 2 Inter-rater reliability tests results. HFACS category

Cohen’s Kappa coefficient (κ)

Simple percentage compliance rate (%)

Level 4 – Organizational Influences

3. Method

Organizational process Organizational climate Resource management

3.1. Inclusion criteria

.877 .269 .684

94.5 93.2 90.5

Level 3 – Unsafe Supervision

Reports of 328 aircraft incidents in the USA between 2000 and 2018 were obtained from the NTSB database according to the following criteria; � � � � � �

-1 ¼ perfect disagreement 0 � κ � 0.20 ¼ there is no compatibility 0.20 � κ � 0.40 ¼ there is weak compatibility. 0.40 � κ � 0.60 ¼ there is moderate (adequate) compatibility. 0.60 � κ � 0.80 ¼ there is a very good (high) level of compatibility. € 0.80 � κ � 1.00 ¼ There is excellent compliance (Ozdamar, 2004).

Supervisory violations Failed to correct problem Planned inappropriate operations Inadequate supervision

Time interval: 2000–2018 Type of research: Aviation incidents Aircraft category: Airplane Operation: Air carriers (part-121) Flight type: Scheduled flights Report type: Final reports

.774 .734 .863

93.2 89.1 94.5

.949

98.6

Level 2 – Preconditions for Unsafe Acts Technological environment Physical environment Personal readiness Crew resource management Physical and mental limitations Adverse physiological conditions Adverse mental states

Part 121 is one of the Federal Aviation Administration (FAA) regu­ lations relative to airworthiness certification which includes operating requirements: Domestic, flag and supplemental operations. To reach detailed information about FAA regulations (FAA, 2020). Part 121 scheduled flights consists of commercial passenger flights of major airline companies, therefore it has been selected in order to better observe the influence of management and organizational factors. Also, the most accurate and complete information about the incident is ob­ tained from the final reports.

.867

94.5

.890 .490 .844

94.5 98.6 93,2

.904

95.9

.572

94.5

.827

93.2

.891 .852 .786 .889

94.5 93.2 93.2 94.5

Level 1- Unsafe Acts Violations Perception errors Skill based errors Decision errors

4

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Kılıç (2015) emphasized that the kappa value in the kappa test was influenced by the number of classes. Also, he stated that the smaller the number of categories (the best case being two categorical variables), the larger the kappa value that can be calculated (Kılıç, 2015). However, low observed frequencies can distort the kappa value ( Li et al., 2008). For example, two variables with 95% compliance by percentage may be inconsistent in the kappa test. For this reason, the compliance between the variables coded by the two coders is given both as the kappa test results and as a percentage in Table 2. When we look at Table 2, it is seen that the kappa coefficient is adequate, good or excellent in almost all categories except the organizational climate category, the kappa coef­ ficient was found to be 0.269, although the percent compliance was 93.2% in this category. This is because the frequency of the organiza­ tional climate class is very low (only 1). If the inter-rater reliability tests result between the coders is evaluated; the rates of compliance were quite high compared to literature. In their study, Li et al. (2008) emphasized that, for the rate of compliance between coders, between 63% and 95% is acceptable ( Li et al., 2008). Shappell and Wiegmann (2003) found 85% overall agreement between coders in their study and interpreted this as an excellent level (Shappell and Wiegmann, 2003). Li and Harris (2005) found compliance rates between coders of between 72% and 96%, and described this as acceptable ( Li and Harris, 2005).

Riffenburgh briefly summarizes the difference of these three tests as follows; Yates chi-square test which is also called Yates correction is a different form of chi square statistics calculated as [(∣observed valueexpected value∣-0.5)2/expected value]. The coefficient 0.5 is to adjust for the counts being restricted to integers. It was preferred tables with small cell counts. Fisher’s exact test provides a better solution to dealing with small cell counts. For larger cell counts, Yates’ correction alters the result negligibly and may be ignored (Riffenburgh, 2006). According to Beukelman and Brunner, Yates correction is used to compensate for deviations from the theoretical (smooth) probability distribution and the Fisher exact test is used when the expected frequency of one or more cells is less than 5. This test is commonly used in studies in which one or more events are rare (Beukelman and Brunner, 2016). Manual calcula­ tion of the fisher test is quite difficult. But it can be easily calculated by computer. 3.2.2.2. Phi (Φ) coefficient. It is the recommended correlation coeffi­ cient for 2 class 2*2 tables. The significance of the relationship between the two variables is given by a chi-square test and the information about the level of the relationship is given by the phi correlation coefficient (Kilmen, 2015). The Phi coefficient varies from 0 to þ1, a value of 0 indicating that two variables are independent and a value of þ1 in­ € dicates a complete association between the two variables (Ozdamar, 2004). Cohen (1988) emphasized that if the value of the Phi coefficient is equal to 0.1, it indicates a low level of relationship, if it equals 0.3 it indicates moderate and 0.5 indicates a high level of relationship (Cohen, 1988).

3.2.2. Relationship analysis HFACS framework allows for relationship analyses thanks to its structure. It is possible to examine how all levels of HFACS affect each other, from the top-level organizational influences to the unsafe acts, which is the lowest level. The database obtained from coding was transferred to the SPSS (Statistical Package for the Social Science) pro­ gram and the relationship between HFACS levels was determined by the Chi-square independence test, phi correlation coefficient and the odds ratio.

3.2.2.3. Odds ratio. The odds ratio is the risk statistic calculated in the € 2*2 cross table based on case control studies (Ozdamar, 2004). The odds value is the ratio of the likelihood of occurrence of an examined event to the likelihood of its non-occurrence. The ratio of odds values belonging to two different events is called the ‘odds ratio’. Since the odds ratio is the ratio of the probability of an event occurring to not occurring, it indicates how many times the Y variable is likely to be observed with the effect of the X variable (Girginer and Cankus¸, 2008).

3.2.2.1. Chi square independence test. Non-parametric tests are used for statistical analyzes if the data does not show a certain distribution fit and if it is a nominal or ordered scale. Whether the X and Y variables with 2 or more classes are dependent on each other is tested by a chi-square independence test. The chi square independence test is applied to cross tables in the form of 2*2 or r*c. The hypotheses tested in the in­ dependence test are “there is no association” or “there is an association” € (Ozdamar, 2004). Chi-square is calculated as: X

χ 2i j ¼

4. Results In this section, firstly, the descriptive analysis results of the examined incident reports are given. HFACS analysis results and relationship analysis results followed this part. As a result of examining 328 reports, the following Table 3 was obtained; As it seen in Table 3, from cockpit crew to managers are human factors. In total, 66.8% of incidents were caused by human factors. Since multiple factors can be observed in the incidents at the same time, it cannot be expected that the sum of the percentages is equal to 100%. With this ratio, human factors have been the primary cause of incidents, leaving environmental and equipment factors behind. The five most frequent aircraft types in the 328 aircraft incidents were; B737 (19.4%), Bombardier CL600 (8.9%), B757 (7.4%), A320 (6.7%), EMB145 (4.9%), other (52.4%). Note that different aircraft

EÞ2

ðO E

Where: O ¼ Observed value E ¼ Expected value χ 2 ¼ The cell Chi-square value P 2 χ ¼ Formula instruction to sum all the cell Chi square values χ 2 i j ¼ i-j is the correct notation to represent all the cells, from the first cell (i) to the last cell (j) (Mchugh, 2013).

Table 3 Main causes of incidents.

The chi-square independence analysis in the 2*2 cross tables is made with three different approaches according to the size of the theoretical values in the table cells; � Pearson chi-square test; if the theoretical values in the cells are all equal to or greater than 25. � Yates’ chi-square test; if any of the theoretical frequencies in the cells are between 5 and 25. � Fisher exact test; if any of the theoretical frequencies in the cells are € less than 5 (Ozdamar, 2004).

5

The factors that caused the incidents

Frequency (n)

Percent (%)

Cockpit crew Air traffic controller Ground crew Maintenance personnel Managers

74 54 34 76 90

22.6 16.5 10.4 23.2 27,4

Total human factors

219

66,8

Equipment, materials

183

55,8

Environmental impacts

79

24,1

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Journal of Air Transport Management 84 (2020) 101784

types are used more frequently than others, as such the frequency in these incidents does not mean that the aircraft type is problematic. A total of 177 people were injured slightly in the 328 aircraft in­ cidents investigated. 216 (65.8%) incidents occurred during the day and 83 (25.3%) incidents occurred during the night. For 29 (8.8%) incidents, the time of occurrence was not recorded on the NTSB. Cockpit crew related incidents were mostly observed at the landing phase of the flights with 32.4%. In addition, air traffic controller related incidents were mostly observed at the takeoff phase of the flights with 35%.

factor was followed by procedures, at 17%. The equipment/facility re­ sources factor was observed in 12% of aircraft incidents and it was the most frequently observed factor in resource management causal cate­ gory. Human resources factor followed this with 9%. 4.1.1. Relationship analysis results 18 HFACS causal categories were coded as present or absent (1-0) for each of the 74 aircraft incidents. Then the relationship between the categories was examined individually as 2 * 2 tables. As a result, sig­ nificance values and coefficients obtained from chi square independence test, phi correlation coefficient and odds ratio are given in Table 5; Analysis of the strength of association between categories in the higher and lower levels of HFACS framework was shown in Table 5. The level 4 organizational influences versus level 3 unsafe supervision found that there were seven pairs of significant associations. Also two pairs of categories have significant association between level 3 and level 2. Analysis of the strength of association between categories at level 2 preconditions for unsafe acts versus level 1 unsafe acts of operators showed three pairs of significant associations. In the level 4 categories resource management was significantly associated with three categories of unsafe supervision: inadequate su­ pervision (df ¼ 1, p � 0.001), failure to correct a known problem (df ¼ 1, p � 0.01) and supervisory violations (df ¼ 1, p � 0.05). Organizational process was significantly associated with four categories of unsafe su­ pervision: inadequate supervision (df ¼ 1, p � 0.001), planned inap­ propriate operations (x2 ¼ 34.248, df ¼ 1, p � 0.001), failure to correct a known problem (x2 ¼ 36.888, df ¼ 1, p � 0.001) and supervisory vio­ lations (x2 ¼ 15.024, df ¼ 1, p � 0.001). In the level-3 categories, supervisory violations were significantly associated with one category of level 2: technological environment (df ¼ 1, p � 0.01). Planned inappropriate operations was significantly associated with one category of level 2: crew resource management (x2 ¼ 5.834, df ¼ 1, p � 0.05). In the level-2 categories, technological environment was signifi­ cantly associated with one category of level 1: perceptual errors (x2 ¼ 6.161, df ¼ 1, p � 0.05). Physical environment was significantly asso­ ciated with one category of level 1: perceptual errors (x2 ¼ 25.416, df ¼ 1, p � 0.001). Physical/mental limitations were significantly associated with one category of level 1: perceptual errors (x2 ¼ 32.924, df ¼ 1, p � 0.001). When the correlation coefficients were examined, the statistically highest significant positive correlation was observed in level 4 and level 3 between the organizational process and failed to correct known problem (Φ ¼ 0.737, p � 0.001). In level 3 and level 2 there was a moderate correlation between couples with a statistically significant relationship between them. The statistically highest significant positive correlation was observed in level 2 and level 1 between physical/mental limitations and perceptual errors (Φ ¼ 0.698, p � 0.001). Odds ratios were interpreted only in the context of human factors. That is the likelihood of the occurrence of a human factor in the presence of another human factor was interpreted. In this analysis, the effect of management practices on incidents were tried to be revealed based on the associations between the causal factors in incidents. Odds ratio analysis can contribute to an easier understanding of the relationships between the causes obtained in analysis of Chi-square. With this analysis how many times the probability of occurrence of one cause increased in the presence of the other can easily be observed. Also, it can provide an insight into what kinds of interactions happen until incident takes place. When odds ratios were examined, it can be said that the occurrence of physical and mental limitations increases the probability of percep­ tual errors by about 40 times. The highest odds ratio in the table was determined between these two pairs. The odds ratios between the other couples can be similarly interpreted. But it can be logically said that the probability of perceptual errors will increase when there is a physical restraint. In this respect, the odds ratio is more important for the other

4.1. HFACS analysis results HFACS analysis was performed on 74 cockpit crew related aircraft incidents from 328 aircraft incidents. From the coding, 532 causal fac­ tors were identified. The results obtained from the analysis of the 4 levels of HFACS are shown in Fig. 2; 227 unsafe acts, 182 preconditions for unsafe acts, 71 unsafe su­ pervisions and 52 organizational influences factors were observed in cockpit crew-related aircraft incidents. The frequency of HFACS causal categories in HFACS levels is shown in Fig. 3; It can be seen from Fig. 3, unsafe acts consist of skill-based errors (90), decision errors (62), violations (47) and perceptual errors (28) respectively. This ranking observed that in unsafe acts in HFACS was similar to other studies in literature in terms of proportions accounted for; (Wiegmann and Shappell, 2001c), (Wiegmann and Shappell, 2001a), (Shappell et al., 2007), (Shappell et al., 2006), (Ting and Dai, 2011). In precondition for unsafe acts level, the highest ratio belongs to personal factors (79). The highest ratio in level 3 and level 4 belongs to failed to correct problem (22) and organizational process (35) causal category respectively. The percentages of the most common HFACS factors observed in the incidents are given in Table 4; As seen in Table 4, in level 1 for decision error, skill-based error, perceptual error and violations the most common factors were inad­ vertent use of flight controls (61%), poor decision (34%), visual illusion (28%) and failed to properly prepare for the flight (19%) respectively. (Note that percentages do not add up to 100% because each incident is typically associated with multiple causal factors across several causal categories). In level 2, for condition of operators, personnel factors and envi­ ronmental impacts causal categories the most common factors were failed to communicate/coordinate (54%), physical environment (41%), technological environment (28%) and visual limitation (26%) respectively. In level 3, for inadequate supervision, planned inappropriate oper­ ations, failed to correct problem and supervisory violations causal cat­ egories the most common factors were failed to provide correct data (18%), failed to identify an at-risk aviator (16%) and authorized un­ necessary hazard (16%) failed to provide oversight (9%) respectively. As it seen in Table 4, the most frequently observed factor in level 4 in organizational process causal category was the oversight with 27%. This

Fig. 2. HFACS levels’ rates. 6

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Journal of Air Transport Management 84 (2020) 101784

Fig. 3. HFACS causal categories’ rates.

management does not plan the work and crew matching properly. This pair is not directly embedded like the physical and mental limitations and perceptual errors pair. When the relationships between HFACS organizational and super­ vision levels and unsafe acts of operators were examined, Table 6 is obtained; There was one pair of significant associations in level-4 versus level1; organizational process and violations of operators (x2 ¼ 6.343, sd ¼ 1, p � 0.05). Also, there was one pair of significant associations in level-3 versus level-1; supervisory violations and violations of operators (x2 ¼ 4.205, df ¼ 1, p � 0.05). Intermediate and low positive correlations were found between these pairs, respectively. There was a statistically sig­ nificant relationship between supervisory violations and violations of operators, but the coefficient of this significance is close irrelevance as the correlation between them is very low. Therefore, it can be said that there may be other factors that affect the relationship between these two factors rather than a direct relationship. When odds ratios are examined, it can be said that the occurrence of supervisory violations or problem of organizational process increase the probability of a violation of opera­ tors by about 3–4 times.

Table 4 Most common causal factors. HFACS Levels

Most Common Causal Factors

Percentage (%)

Unsafe Acts

Improper procedure Inappropriate maneuver Poor decision Breakdown in visual scan Inadvertent use of flight controls Visual illusion Failed to adhere to brief Failed to properly prepare for the flight

24 11 34 14 61 28 12 19

Preconditions for Unsafe Acts

Distraction Visual limitation Failed to communicate/coordinate Failed to conduct adequate brief Failed to use all available resources Misinterpretation of traffic calls Physical environment Technological environment

12 26 54 15 15 12 41 28

Unsafe Supervision

Failed to provide oversight Failed to provide correct data Failed to identify an at-risk aviator Authorized unnecessary hazard

9 18 16 16

Human resources; selection, staffing/ manning, training Equipment/facility resources; poor design, purchasing of unsuitable equipment Procedures; Standards, documentation, clearly defined objectives, instructions Oversight; Risk management, safety programs

9

Organizational Influences

5. Discussion and conclusion The results of the analysis into the unsafe acts by cockpit crew that lead to incidents between the years 2000 and 2018 were ranked ac­ cording to the frequency of the type of act. In line with other findings in literature, skill-based errors were highest, followed by decision errors, violations and then perceptual errors. The most observed factor in skill-based errors is the inadvertent use of flight controls with 61%. DOD (Department of Defense) defined this factor as the excessive or inadequate control of the aircraft or systems causing an inappropriate response that is not in accordance with the rules by the individual (DOD, 2005b). No statistically significant rela­ tionship was found between the skill-based error and other causal cat­ egories of HFACS. The most frequently observed factor in decision errors is poor

12 17 27

pairs that are not logically embedded. Planned inappropriate operations and crew resource management pairs can be given as an example. The probability of encountering a problem in crew resource management in the presence of planned inappropriate operations is about 11 times higher. Disagreements can be observed between the crew if 7

K. D€ onmez and S. Uslu

Journal of Air Transport Management 84 (2020) 101784

Table 5 Relationship analysis results. HFACS Levels

Table 6 Relationship analysis results - management effect.

Chi Square Test

Phi (Φ)

Chi Square Value

Phi coefficient

p-Value

Odds Ratio PValue

HFACS Levels

Value

Level 4 – Level 3 Resource Management x Inadequate Supervision Resource Management x Failed to Correct a Known Problem Resource Management x Supervisory Violations Organizational Process x Inadequate Supervision Organizational Process x Planned Inappropriate Operations Organizational Process x Failed to Correct a Known Problem Organizational Process x Supervisory Violations

–

.000***

.538

.000***

20.300

–

.002**

.393

.001**

8.333

–

.030*

.279

.017*

4.821

–

.000***

.429

.000***

21.500

34.248

.000***

.712

.000***

–

36.888

.000***

.737

.000***

–

15.024

.000***

.487

.000***

28.667

–

.004**

.359

.002**

6.533

5.834

.016*

.315

.007**

11.118

6.161

.013*

.320

.006**

0.139

25.416

.000***

.615

.000***

20.900

32.924

.000***

.698

.000***

40.714

Level 4 – Level 1 Organizational Process x Violations of operators Level 3 – Level 1 Supervisory Violations x Violations of operators

Odds Ratio

Chi Square Value

pValue

Phi coefficient

pValue

pValue

6.343

.012*

.320

.006**

3.867

4.205

.040*

.274

.018*

4.800

accidents had occurred. Some pilots may instantly lose their ability to make decisions under factors such as fatigue and stress. In some, a more widespread and persistent lack of the ability of judgement was observed as a consequence of the social environment and excessive personalities. However, according to Krause, a pilot can re-learn or regain these abilities (decision making) regardless of how or why he lost his abilities. There are two basic principles of good judgment and decision making. These are the perception and ability to distinguish between right and wrong solutions. Examining the identification of perception and discrimination reveals many layers of mental ability leading to a good judgment. You have to perceive (be aware, observe, detect, understand) and distinguish between the right and wrong alternatives for the solu­ tion (recognize, see clearly, and understand the differences). There are four basic skills needed to develop correct perception; a vigilant sense of awareness, observation, detection and understanding. Even if the pilots are competent in terms of judging, some factors considerably affect their judgements. Cognitive, moral, emotional, physiological, social, per­ sonal, and attitude factors directly influence the judging process. Any of these factors can lead to a failed judgment. However, it is possible for pilots to know the existence of these negative factors and to learn how to change these conditions (Krause, 2003). The most frequently observed factor in violations is the failed to properly prepare for the flight factor. This factor is included in routine violations. One thing to note here is that this factor is not to be confused with the personal readiness causal factor under the heading of pre­ conditions for unsafe acts. The personal readiness factor includes preflight drug use, alcohol consumption, sleep patterns, etc. The failed to properly prepare for the flight factor includes situations such as incompletion or violation of the procedural controls immediately prior to the flight. The most frequently observed factor under the heading of perceptual errors was the visual illusion factor. Perceptual errors can be described as the difference between the world that the individual perceives and the real world. Misjudgment of distance, and the wrong decisions made after the visual illusion can be given as an example for this error group. The root of perceptual errors are sensory inputs (Berry, 2010). DOD described perception errors as a factor that caused human error, resulting in the misinterpretation of an object, threat or condition. These misinterpretations are seen as visual and auditory delusions, or cogni­ tive and attention deficits (DOD, 2005a).

Level 2 – Level 1 Technological environment x Perceptual Errors Physical Environment x Perceptual Errors Physical/Mental Limitations x Perceptual Errors

Phi (Φ)

*P � 0.05 **p � 0.01 ***p � 0.001. Note 1: Degrees of freedom ¼ 1 for the entire table. Note 2: All other comparisons were non-significant. Note 3: The Fisher Exact test was used when the expected value was below 5. This test only gives significance (P) value. Note 4: If one of the cells in the 2*2 tables is 0, the odds ratio cannot be calculated. The confidence interval for calculating the odds ratio is 95%.

Level 3 – Level 2 Supervisory Violations x Technological environment Planned Inappropriate Operations x Crew resource management

Chi Square Test

*P � 0.05 **p � 0.01 ***p � 0.001. Note 1: Degrees of freedom ¼ 1 for the entire table. Note 2: All other comparisons were non-significant. Note 3: The Fisher Exact test was used when the expected value was below 5. This test only gives significance (P) value. Note 4: If one of the cells in the 2*2 tables is 0, the odds ratio cannot be calculated. The confidence interval for calculating the odds ratio is 95%.

decision factor. DOD defined this factor as an individual not sufficiently or successfully assessing the risks associated with a particular action plan and the individual causes an unsafe act after making an inappro­ priate decision as a result of this incorrect assessment (DOD, 2005b). Krause (2003) emphasizes that good judgment and decision-making are mental abilities that each pilot can learn. In addition, Krause stated that there was irrefutable evidence from academic studies, into safety sur­ veys and accident reports, that showed that there had been deficiencies in the decision-making abilities of pilots during flights in which

5.1. The key factor ‘crew resource management’ When preconditions for unsafe acts were examined, the most 8

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Journal of Air Transport Management 84 (2020) 101784

common factor was the failed to communicate and coordinate which is under the category of CRM. This factor was observed in 54% of all in­ cidents and thus the CRM causal factor, with the other sub factors that it includes, can be identified as a key factor in incidents. According to Krause (2003), crew resource management is a com­ bination of a pilot’s judging and decision-making abilities, personal behavior and attitudes towards others within group dynamics. Inter­ personal attitudes in crew resource management will ultimately inter­ fere with a pilots’ technical skills and aviation knowledge. The failure of any component of crew resource management will adversely affect a crew’s flight performance and flight safety. It should not be forgotten that a team is as strong as the weakest member, but it is unacceptable for a cockpit crew to include any weakness. Krause (2003) described the widespread purpose of crew resource management training as improving the decision-making process by increasing the performance of the cockpit team in communication, teamwork and leadership. He described the following as key factors for quality communication in the context of crew resource management;

aviation incidents. Therefore, it can be said that CRM training is one area to focus on in order to reduce and preventing aviation incidents and accidents. The failed to communicate and coordinate factor was observed in more than half of all cockpit crew-related incidents. This emphasizes once again the importance of communication and coordi­ nation in aviation and, as it is under the CRM causal category, shows how vital good CRM is for the sector. 5.2. Management effect As a result of the relationship analysis carried out in the study, very important findings were obtained about the violations: organizational process and supervisory violations were found to be statistically signif­ icant in relation to the violations of operators. The analysis shows us that the probability of crew violations increases by 3–4 times when there are supervisory violations. In order to better understand the relationships between these factors it is necessary to examine their common defini­ tions. The organizational process title is composed of three main factors. These are; operations, procedures and oversight. The concept of oper­ ations here can be defined as the working conditions presented to workers by management. This factor includes subjects such as opera­ tional tempo, time pressure, production quotas, incentives, measure­ ment/appraisal, schedules and deficient planning. If any of these conditions are not suitable for employees’ safety is threatened. Pro­ cedures include official methods on how to do the job. These are; stan­ dards, documentation, clearly defined objectives, and instructions. The oversight factor is the continuous monitoring of resources, organiza­ tional process and organizational climate for a safe and productive working environment. The oversight factor includes risk management and security programs (DOD, 2005a). The deficiencies and errors in these factors can be related to the violations made by the cockpit crew. Supervisory violations include factors such as; authorized unnec­ essary hazard, failed to enforce rules and regulations, and authorized unqualified crew for flight (Shappell and Wiegmann, 2000). Supervisory violations are described as a deliberate violation of rules by managers. For example; The permission to allow an aircraft or personnel to fly without the necessary qualification and license. The deficiencies in the enforcement of rules and laws can also be examined under the heading of supervisory violations. Violations at management level can also cause the cockpit crew to violate the rules. Therefore, these chains of viola­ tions can lead to aircraft incidents and accidents. In the unsafe supervision level, the most common factor was failed to provide correct data, which was observed in 18% of cockpit crew related incidents. This factor is under the planned inappropriate operations causal category. This causal category was found to be statistically sig­ nificant in relation to the CRM causal factor in HFACS level 2. DOD defined the planned inappropriate operations factor as planning oper­ ational tempo and schedule so as to put the crew at unacceptable risk. Such planned inappropriate operations are often unavoidable in emer­ gency situations but unacceptable for routine operations. This category includes topics such as inappropriate crew matching. It is inevitable that problems will arise if two people with a great difference in talent are matched. In the same way, pairing two inexperienced pilots for a diffi­ cult operation will not be a sensible act. Planned inappropriate opera­ tions can be described as the incapacity of managers to assess the risks that would put the operation at risk and allow unnecessary hazards (DOD, 2005a). It is therefore inevitable that this factor is directly related to the CRM factor, which includes crew communication and coordina­ tion. In fact, according to the results of the analysis carried out in this study, the possibility of the occurrence of a problem in the crew resource management factor increases by about 11 times under the existence of planned inappropriate operations. The relationship between these two factors statistically proves that decisions or acts at the supervision level directly influences the performance of the cockpit crew. In the organizational influences level, the organizational process factor was found to be statistically related to all unsafe supervision

� Interrogation: A systematic investigation of information. � Defense: confidence in expressing situations and feelings. � Active listening: to actively contribute to the collection of informa­ tion and to accept or reject the ideas presented. � Conflict resolution: Deciding on the causes of the conflict and making the appropriate action plan correctly. � Criticism: To assess the overall situation correctly through personal performance and feedback (Krause, 2003). Coordination and communication has become a very important issue in aviation in recent years and there are now many studies on this subject. Some examples from literature are given below for a better understanding of the importance of the CRM factor. €nmez and Uslu (2016) investigated communication-related acci­ Do dents in aviation. These accidents were studied under three classes based on linguistic problems, expectations and cultural differences. As a result, they found that education, standardization, and management issues should be emphasized in order to eliminate communication problems €nmez and Uslu, 2016). (Do Krivonos (2007) stressed the importance of communication for aviation safety in his study. He also examined examples of communication-related accidents and highlighted the importance of lessons to be learned from these accidents. As a result, he emphasized that communication is the key factor for aviation safety and that effec­ tive coordination can only be achieved through effective communica­ tion. In this context, teaching effective coordination is essential for aviation safety training (Krivonos, 2007). Kaps et al. (1999) showed that CRM is a crucial issue for aviation committees and airline companies, but that there was not any literature research covering CRM studies at the time. As a result, they searched various databases according to certain criteria and collected summaries of important studies made between 1993 and 1998 in this study. These summaries were evaluated under four headings; the current status of CRM training and research, the evolution of CRM concepts, measuring methods, and the application of CRM (Kaps et al., 1999). Salas et al. (2001) examined and compared 58 CRM trainings pub­ lished up to that date. As a result, they emphasized that CRM training improves learning and develops desirable behavioral changes. However, they were not sure of the effects of CRM on the extreme layers of the organization (such as safety) (Salas et al., 2001). Salas et al. (2006) evaluated CRM training from 28 different areas (aviation, medicine, offshore oil production, maintenance, marine, and nuclear energy fields) with regard to the effects on learning and behavioral changes and reached different results according to each field. They also stated that it remained unclear as to how CRM affects factors extreme layers of the organization such as safety (Salas et al., 2006). As a result of our study, CRM was identified as a key factor in 9

K. D€ onmez and S. Uslu

Journal of Air Transport Management 84 (2020) 101784

factors of HFACS. The oversight factor, which is under the heading of organizational process, was the most frequent organizational influence observed in 27% of cockpit crew related incidents. Organizational process concept described previously in detail. As can be understood from the above definitions, deficiencies in these concepts at the level of organization and higher management affect lower man­ agement levels. Another factor that is related to lower levels of management under the heading of organizational impacts is resource management. Resource management can be examined in three sub-sections; human resources, budget management, hardware and resource budgeting. Human resources include qualification, assignment and training. Budget management includes issues such as the lack of funding and extreme cuts. Equipment/facility resources includes issues such as poor design and the purchasing of unsuitable equipment (DOD, 2005a). The resource management causal factor covering the above concepts was found to be significantly related to all factors under HFACS unsafe supervision causal category (except for planned inappropriate operations). As emphasized at the beginning of the study, the biggest deficiency of HFACS is its post-accident applicability. The applicability of HFACS to accidents has been proven in most of the studies given in the literature section. In this study, HFACS was applied to incidents which occurred between 2000 and 2018, not accident, to turn this into an advantage. Because the structure of the incident and accident reports published by NTSB are extremely similar, the applicability of the HFACS model to the incidents made it easier. In this study, the obtained kappa values which describes the compatibility between coders can provide evidence of the applicability of the model. As a result, the significant associations in human factors which were present in the occurrence of incidents were revealed. In addition, it was statistically shown how decisions and errors at the management level affect the unsafe acts of operators. Considering these associations ob­ tained from incidents, precautions can be taken before accidents happen. In fact, an accident is known to be just one of many incidents, resulting in disaster. Therefore, if accidents are desired to be prevented, a proactive approach will be demonstrated by conducting researches with focusing on incidents. This study may well serve as a guide for human factor-based incident researches. Further similar work in this area will provide evidence which could reduce future aviation incidents; consequently, further research is suggested using other data pools. It should not be forgotten that the struggle for the prevention of aviation accidents or incidents starts with the analysis of the previous event.

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