Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process

Abstract

Objective

This work establishes a methodological framework for sources of pilot distraction and interruptions in a structured model that can be used as a tool for cockpit design/procedure assessment.

Background

Pilots must complete complex tasks, and distractions can impair performance and lead to errors that can cause aircraft accidents. Although various cockpit distractors are examined individually, there is no integrated approach.

Method

Distraction and interruption sources were identified through a literature review and confirmed/extended by interviews with airline pilots. Associated weights were determined through pairwise comparisons, yielding a hierarchical model using the Analytic Hierarchy Process.

Results

26 sources of pilot distraction and interruptions were quantified and categorized into four categories: communication, head-down time, responding to abnormal conditions & unexpected situations, and searching for traffic.

Conclusion

A taxonomic structure for assessment is achieved with the top 5 sources identified as communications, technical interruptions, experience in type, environmental factors, operational irregularities, and airspace high terrain, accounting for 63.07%.

Application

The structured system is a flexible assessment scale that provides a taxonomic framework for airline risk management, supports future research, and cockpit design efforts.

Keywords

pilot distraction pilot interruption accident human error analytic hierarchy process (AHP)

Introduction

Global commercial aviation carried over 4.2 billion passengers in 2023, with an annual growth rate of 12.9 % terms of departures. Moreover, despite the recent decline in accident rates, 66 accidents resulted in over 100 fatalities in 2023 (ICAO, 2024), underlining safety concerns. Besides loss of life and psychological harm, aircraft accidents cause reputational damage and have a negative economic impact (Perez-Granja et al., 2024; Stamolampros, 2022). Consequently, all aviation stakeholders invest significant time and resources in enhancing safety.

The root cause of most aircraft accidents involves human factors (Kilic & Ucler, 2019), human errors (Wiegmann & Shappell, 2001), and pilot interactions (Zhang & Mahadevan, 2021). Approximately half of aviation incidents and 80% of aircraft crashes are caused by pilot error (Li et al., 2001). Similarly, the National Transportation Safety Board attributes 80.9% of general aviation accidents to pilot error (NTSB, 2024).

Pilots must complete complex activities concurrently, which demand action and a high level of cognition, subject to perturbations (Loukopoulos et al., 2016). Nevertheless, ‘humans are vulnerable to interruptions and distractions’ (FSF, 2014, p. 12), which tax working memory. Memory errors and distractions can lead to aircraft accidents (Kilic & Ucler, 2019; Nowinski et al., 2003). Human errors are often subject to distractions (Chen et al., 2019), which are operationally defined in aviation as any event, stimulus, or task that diverts attention from primary flight duties (Loukopoulos et al., 2001; Yadav et al., 2022; Yıldız, 2024), leading to pilot performance reduction (Chen et al., 2019; Yadav et al., 2022) due to loss of focus and reduced awareness (Lee et al., 2020; Wang, Houghton, & Majumdar, 2024; White & O’Hare, 2021). Even primary duties can negatively affect attention depending on timing and relevance to the immediate flight phase. Consequently, tasks inherent to flight, such as ATC communication, can transition into a distraction. Distractions are mainly irrelevant or non-essential stimuli (Dismukes et al., 1998; Zickerick et al., 2020). Although distractions are characterized by interruptions (Loukopoulos et al., 2001), they don’t require immediate action (Zhou et al., 2024). On the contrary, interruptions interfere with general stimuli and require immediate attention. They require encoding new goals that primary task goals are decayed (Altmann et al., 2014). Both distractions and interruptions overload the flight crew (Bandeira et al., 2018) and increase crash risk in transportation, but ‘the relationship between visual, manual, and cognitive sources’ is unresolved (Strayer & Cooper, 2015). Usability evaluations targeting human factors are done for flight deck certification (Yeh et al., 2016), but they remain corrective (Salas et al., 2010).

Design is a causal or important contributing factor in accidents (Kinnersley & Roelen, 2007). High workload situations can reduce situational awareness (SA) and lead to misinterpretations, particularly when poor design is present (Yeh et al., 2016). The interrelation between distractions and cockpit design was a key motivation for the development of new-generation glass cockpits (Wiener, 1989). Although it is known that modern automated cockpits are reducing workload, the associated high cognitive workload and team-based flight operations require investigation (Salas et al., 2010).

This problem is still contemporary. New technologies, such as augmented reality (AR), on the one hand, enhance cockpit design and reduce interruptions; on the other hand, they can create new distractions that require further investigation (Li et al., 2022). Given that a representative analysis requires a systematic approach (Bandeira et al., 2018), the literature lacks a structured model for organizing and weighing distractors and interruptions.

Information about pilot distraction and interruption is scattered and disorganized, lacking an integrated approach within a common structure. Although various cockpit distractions and interruptions are examined individually, there is no integrated methodological approach that also focuses on pilots and their perceptions. The practitioners and grey literature appear to have a better organizational structure, but to the best of the authors’ knowledge, there is no existing work that specifically examines the perception of airline pilots. Nevertheless, the potential impairment of distraction sources on attention and focus is key (Sheridan, 2004), which can be best assessed by the pilot as the user. Consequently, further insight from the practitioner’s perspective, including the pilot’s, is required to complement and organize these sources. Moreover, scientific research struggles to keep pace with the rapid technological advances in aviation, which involve new means of cockpit interaction, while the development of new technologies and procedures fails to account for distraction and interruption. There are many documents focussing on distractions and interruptions, and it is impractical to consolidate them by traditional methods into a single model. As a result, essential sources of pilot distraction and interruption must be quantified and consolidated in a structured model, which urges the following research questions (RQ):

RQ1: What are the sources of pilot distraction and interruption?

RQ2: How can these sources be quantified and aggregated in a model?

RQ3: What is the associated ranking of distraction sources in the cockpit?

The structure of the paper is as follows: First, the scientific and grey literature are examined to identify sources using a traditional review and an AI-supported methodology. Then, these sources are checked, complemented, and placed in a hierarchy, and their weights are determined using the Analytic Hierarchy Process (AHP), as explained in the method section. After that, the results and limitations are discussed, concluding with contributions and potential future research.

Methods

Review

This work is necessitated by the need to quantify the sources of pilots’ distraction and interruptions. Consequently, a literature review was made first using the PRISMA method (Liberati et al., 2009). Therefore, the title was first identified as ‘pilot distractions and interruptions’, and the Scopus database was searched using the search terms ‘pilot and distraction’ and ‘pilot and interruption’ individually, which yielded 830 and 1,036 documents, respectively. After limiting the search for publications in English to include the keyword ‘aviation’, 27 and 9 documents remained. Documents that did not mention specific sources of distraction or interruption for regular airline flights were excluded, leaving 12 publications. Potential sources of distraction or interruption were identified in these documents.

In addition, a Google Scholar search using the same keywords was conducted to complement the analysis of related factors and to provide practitioners’ perspectives. It was observed that over 300,000 scientific papers, reports, rules, regulations, and other various downloadable materials were available. When excluding all non-English publications and limiting the scope to civil aviation and cited documents with identified sources, 24 suitable publications were identified from the grey & practitioner literature among the first 200 ranked sources. Moreover, considering the ranking algorithm of Google Scholar is a black box without precise control, the prompt ‘airline pilot distractions and interruptions - identifying sources’ was processed using the artificial intelligence (AI) tool of SciSpace (n.d.) on the databases of SciSpace, PubMed, Google Scholar, and arXiv, resulting in 644 records for the period 2000-2025. After duplicate removal, this number was reduced to 445 publications. Then, only peer-reviewed papers were examined, and a semantic AI prompt was applied to exclude irrelevant papers. Then, 5% of the excluded papers were also checked manually on a random basis. During this AI search process, the automated computation of relevance scores (Liu, 2009; Robertson & Zaragoza, 2009) was based on semantic similarity (Reimers & Gurevych, 2019) in SciSpace (n.d.) by utilizing the Relevance score (R) between 0 and 1, for paper p and query q as

R (p, q) = s i m (q, p) + β Q (p) + γ C (p) w i t h {\begin{cases} α = 0.7 (s i m i l a r i t y w e i g h t) \\ β = 0.2 (q u a l i t y w e i g h t) \\ γ = 0.1 (c o n t e x t w e i g h t) \end{cases}

There, the similarity formula sim used the cosine similarity approach (Lahitani et al., 2016), automated in the AI tool, where

• title, abstract, and keywords are looked at with the weights of 0.45, 0.45, and 0.1, respectively, for the query of ‘Airline pilot distractions and interruptions - identifying sources’;

• the simplified quality adjustment factor Q utilized the weighted sum of peer-review indicator PR (1 if peer-reviewed, 0.7 otherwise) and the methodology quality M (1.0 if empirical with data, 0.9 if systematic review, 0.8 if experimental or simulation, 0.7 if case study, 0.6 if theoretical/conceptual, 0.5 if opinion or commentary) with weights of 0.7 and 0.2, respectively;

• the content scoring for C(p) was made as the sum of related keywords over context keywords of the search (SciSpace, n.d.).

Thus, the algorithm enabled the assessment of all relevant literature using metrics for similarity, quality, and content. The search did not include any filters or ranking based on citation count. It relied solely on the keyword count and the method applied. This prevented potential bias toward more-cited work and ensured the reproducibility of the review results. Consequently, the number of publications remaining was 199, among which highly relevant papers with a relevance score R ≥ 0.7 were selected, resulting in a population of 30 papers. From this population, 10 papers were already identified in the traditional literature review described above, bringing in 20 new papers to the research (see Figure 1).

Figure 1.

Review diagram, also utilizing the AI-supported semantic elimination and ranking

All in all, literature without any specific identification of pilot distraction or interruption sources was not looked at, and literature not related to civil aviation was excluded. The information collected was used to identify distraction sources, which the authors grouped in an initial hierarchy using affinity diagrams. There, ontologies were respected, allowing different wordings for similar factors to be unified. The results were summarized in a hierarchy to serve as an initial starting point for semi-structured interviews.

Interviews

Semi-structured interviews were conducted with an expert group comprising 9 airline pilots, including 4 first officers (F/Os) and 5 captains, who fly narrow-body aircraft in European airspace, with an average of 14 years of flight experience. The expert group was from pilots who frequently conduct short- and medium-haul flights. All members of the expert group were informed about the research objectives and methodology, and participation was voluntary, with no consequences for not participating or not completing the interviews. Informed consent was obtained from each participant, and this research complied with the American Psychological Association Code of Ethics (APA, 2010). It was also approved by the Institutional Review Board at Ozyegin University. During these interviews, the experts were asked for the (i) clarity of the distraction/interruption sources identified, (ii) completeness and suitability of these sources, that is, whether they want to add or modify them, (iii) appropriateness of the hierarchy for the categories and these sources, and (iv) their opinion and other comments where they were able to comment in a free context. The authors used the results of these interviews to consolidate and organize the sources for the final hierarchical model.

AHP

The same expert group made pairwise comparisons of the factors embedded in this final hierarchical model, yielding AHP weights to enable prioritization and assessment. AHP is a structured multi-criteria decision-making approach (Saaty, 1990, 2003) applied to assessment and selection (Muller et al., 2025) and safety management extensively (Aminbakhsh et al., 2013; Chan et al., 2004), and it is a suitable method for civil aviation (Chai et al., 2024; Chen et al., 2017; Chen & Li, 2016; Kilic & Ucler, 2019).

Since the AHP method is an established and widely used tool, in-depth explanations of it are not the primary focus of this paper. Nevertheless, in short, AHP utilizes pairwise verbal comparisons of $n$ Criteria, stored in the simplest form on a linear scale from 1 to 9 (see Appendix A6) for equal to extreme importance, yielding a positive reciprocal $(n \times n)$ matrix with $A = a_{i j}$ with $i, j = 1, \dots, n$ , which normalized eigenvector $\vec{w}$ delivers associated weights of these criteria (Saaty, 1990). The symmetrical half of $A$ is populated with inverse values for $a_{j i} = 1 / a_{i j}$ (Ucler, 2017), and it must be near consistent. Consequently, the Consistency Ratio (CR) must be lower than 0.1 (Kilic & Ucler, 2019). CR is determined using the Consistency Index (CI) and the Random Index (RI) as $C R = C I / R I$ with $C I = (λ_{\max} - n) / (n - 1)$ where $λ_{\max}$ is the maximum principle eigenvalue of the comparison matrix for given $A \vec{w} = λ \vec{w}$ . RI can be determined from the literature (see Appendix A7) or by the linear fit formula of Alonso and Lamata (2006) as $C R = (λ_{\max} - n) / (2.7699 n - 4.3513 - n)$ . In practice, these pairwise comparisons can be iterated until the consistency reaches a desired level. There are many free Excel templates or software packages available for AHP applications. The authors used the 2022.07.08 version of the Excel template provided by Goepel (n.d.), a validated tool (Goepel, 2013, 2018) that utilizes a linear fit formula for RI.

Here, iterations were conducted with each participant to ensure consistency across evaluations. Then, the geometric means of all individual assessments were taken to populate the final comparison matrices, giving equal weight to each judgement. There, small asymmetries of the matrix are acceptable, and the consensus S of averaged results is computed based on Shannon Entropy (Goepel, 2013). These calculations were performed for each level of the hierarchy to determine the weights of the criteria and sub-criteria. Since AHP relies on subjective assessments, a criteria weight sensitivity analysis can be conducted to assess the robustness of the model, check for potential ranking fluctuations, and gain confidence (Dweiri et al., 2016; Maletic et al., 2014), which can be done perturbing weights of the most influential and/or close rank competitor criterion, for example, by 10%, 20%, and 30%, and normalization of the weights of other criteria yielding in a new weights vector and a new ranking. Then, ranking stability and reversals are examined, where stable rankings are referred to as robust (Ishizaka & Labib, 2009).

Results

Review Results

The high workload of a commercial pilot, along with distractions and interruptions, constitutes error sources (Wiener, 1989), as clearly quantified in the literature review. The literature identifies several interacting sources in airline operations, particularly occurring during take-off, turnaround, and landing phases (Liu et al., 2023; Stephens et al., 2017; Talone et al., 2015). These phases involve complex avionics and concurrent operational duties that demand high concentration and attention, where failure modes due to channelized attention, startle, diverted attention, and cognitive bias can lead to accidents (Stephens, 2017).

Human errors are an inevitable by-product of complex systems (Reason, 1990). The Human Factors Analysis and Classification System (HFACS) provides a framework for examining human errors in aircraft accidents, tracing them back to four hierarchical levels: unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences (Kelly & Efthymiou, 2019). Distractions and interruptions are causal factors in HFACS (Kelly & Efthymiou, 2019; Muecklich et al., 2023) and often manifest as skill-based errors, but they can also serve as preconditions for acts causing accidents, thereby reducing pilots’ SA and contributing to accidents (Bandeira et al., 2018; Chen et al., 2019).

SA in dynamic decision-making is based on the perception of elements in the current situation, their comprehension, and the projection of future status, where, among others, the interface, complexity, and stress & workload influence SA (Endsley, 1995). It is a state of knowledge and a product of perception, comprehension, projection, attention allocation, memory, and environmental factors (Endslay, 2015). Any item interacting with these elements can inhibit SA. In particular, interruptions can become hazardous because the pilot’s cognitive workload increases, leading to lagging performance across perceptual modalities and discrete tasks (Martins, 2016). Indeed, task interruptions are considered a significant source of distraction (Kelly & Efthymiou, 2019; Loukopoulos et al., 2001; Martins, 2016; Yadav et al., 2022). Moreover, distractions and interruptions can lead to cognitive limitations, negatively impacting flight safety (Scannella et al., 2018). When sudden events draw attention away and lead to inappropriate sampling of the environment, SA may become disturbed (Endsley, 1995), which is a form of distraction. Together with an increase in the mental workload, this negatively affects information processing (see Figure 2) by interacting with its elements, which are perceptual processes, decision-making, and response processes.

Figure 2.

Information processing, as described by Martins (2016)

An example of the perception a commercial pilot might have is the airport location and current altitude information, whereas comprehension involves assessing the impact of a malfunction; this involves projecting the trajectory (Endslay, 2015), which enables the correct response. Any item that diverts attention from these elements, increases mental workload, or interferes with the assessment can be considered a distraction. Scientific scholars categorize distractions in aviation into four main areas: communications, head-down time, searching for traffic, and responding to abnormal situations (Dismukes et al., 1998; Sparks, 2008; Xie et al., 2024a).

Dealing with environmental factors also interrupts other tasks and distracts the pilot (Iani & Wickens, 2007; Xu & Zhang, 2022). Uncertainties in meteorology and aircraft settings, the swapping of duties to divide responsibility among pilots, paperwork, and audio task interruptions (Rodrigues et al., 2025), such as incoming communications from ground support teams and the cabin crew, are significant sources of pilot distraction (Bennet, 2019; Gontar et al., 2017; Loukopoulos et al., 2001; Wiener, 1989). Even non-safety-related announcements to passengers divert attention and result in distraction (Barnes & Monan, 1990).

Workload results in attentional impairments (Scannella et al., 2018), and it correlates positively with distraction and interruption (Chen et al., 2019; Dismukes et al., 1998; Evertsen et al., 2025), which is increased by head-down work in programming flight management systems (Dismukes et al., 1998). Complex flight instrument displays further increase the pilot’s workload and result in distraction (Yadav et al., 2022).

Pilots can miss critical events due to the time spent on head-down tasks, such as in-vehicle controls, displays, onboard information systems, mobile phones, reviewing charts, communication, and programming navigation instruments (Wilkins, 2018). There, displays have a vitally important role because complex visuals, extensive use of pop-up information, colours, blinking or flashing, delays between voices and related visual alerts, extensively loud alerts, usage of different tones, and extensive amount of information on complex data cards can cause distraction (Yeh et al., 2016) so that only the required information is to be given at the ‘right time in the right format’ (Estes et al., 2018).

When interruptions occur in the same sensory channel as the in-process work, disruptions occur (Sarter, 2013). Then, external bright light sources, such as laser illumination, also cause distraction (Dietrich, 2017; Nakagawara et al., 2008; Williamson et al., 2023; Yimga, 2023). Research on information delivery involves the simultaneous use of advanced display technologies and automated synthetic oral instructions for traffic information and Air Traffic Control (ATC) communications, which are found to reduce head-down time (Iani & Wickens, 2007; Wickens et al., 2017). Nevertheless, auditory load impairs SA (Xie, Li, et al., 2024); thus, it is essential to consider that excessive ATC interruptions can become disruptive and reduce flight deck performance (Latorella, 1996).

From the practitioner’s perspective, the United Kingdom (UK) Civil Aviation Authority attributes distractions and interruptions to passengers, including dropping the aircraft, technical issues, searching for traffic, or personal issues (CAA, 2023). The Flight Safety Foundation categorizes interruptions and distractions under the headings of communication, head-down work, and responding to abnormal conditions or unexpected situations (FSF, 2000), such as malfunctions (O’Brien & Bull Schaefer, 2020; Xu & Zhang, 2022). These must be managed aggressively to prevent flightpath deviations and automation monitoring shortcomings. Additionally, time pressure, further exacerbated by distractions from ground and cabin crews, makes pilots vulnerable (FSF, 2014). A clean cockpit and pilots locked in the cockpit are essential to prevent distraction (Caron, 2019; Jelacic, 2023). Moreover, the grey literature also quantifies sources as traffic, passengers, backseat pilots, unfamiliar aircraft, unfamiliar airspace, non-essential electronics (personal), unnecessary radio congestion, cluttered avionics (too much info), foreign object damage (FOD), and open doors and windows (Boldmethod, 2024).

The authors consolidated the literature in a list of 20 distinct sources of pilot distraction/interruption (see Table 1). Descriptions of situations were grouped based on their affinities. Certain sources repeatedly cited by name across many publications were included in this list. While some papers only refer to general terms, such as noise (Bao et al., 2019), others are more specific, mentioning such as auditory interfaces in critical events that interrupt the pilot (Ferraro et al., 2017). Given the paper’s motivation to deliver a tool capable of evaluating cockpit design and operational procedures, these sources were classified and categorized by their root causes. For example, considering that the noise is based on auditory alerts (Stadler, 2014), this finding was categorized under responding to operational irregularities. It was also observed that there is a strong consensus on the negative impact of communication, with various communication paths designated as individual sources.

Table 1.

Pilot distraction/interruption sources from the literature

Nr.	Source	Reference
1	Accessing onboard information systems	Wilkins (2018)
2	ATC communication	Hodgetts et al. (2005); Iani & Wickens (2007); Wickens et al. (2017); Xie, Li, et al., 2024; Latorella (1996); Latorella (1998); Talone et al. (2015); Carugati et al. (2015); Watkins et al. (2018); Stader (2014); Rodrigues et al. (2025); Wolff et al. (2021); Yıldız (2024); Zarlengo (2025); Rankova (2023)
3	Backseat pilot communication	Boldmethod (2024)
4	Captain to F/O communication	Wiener (1989); Loukopoulos et al. (2001); Gontar et al. (2017); Rodrigues et al. (2025); Zarlengo (2025)
5	Cluttered avionics	Yadav et al. (2022); Boldmethod (2024)
6	Coordinating with the ground team	Gontar et al. (2017); FSF, 2014; Bennet (2019); Stader (2014); Loukopoulos et al. (2001)
7	Dealing with bright light, laser	Nakagawara et al. (2008); Dietrich (2017); Williamson et al. (2023); Yimga (2023)
8	Dealing with environmental factors	Iani & Wickens, 2007; Rodriguez Paras et al., 2021; Xu & Zhang (2022); Estes et al. (2018); Boettcher (2014); Zarlengo (2025)
9	Dealing with technical interruptions	O’Brien & Bull Schaefer (2020); Xu & Zhang (2022); Bennet (2019); Dehais et al. (2012); Stader (2014); Boettcher (2014); Serber (2013); Zarlengo (2025)
10	Handling FOD	Boldmethod (2024)
11	Operating in extensive traffic	CAA, 2023; Boldmethod (2024)
12	Programming navigation instruments	Wilkins (2018); Dismukes et al. (1998); Latorella (1998); Carugati et al. (2015); Stader (2014); Miller & Holley (2017)
13	Responding to cabin crew	Xi et al. (2024b); Wiener (1989); Loukopoulos et al. (2001); Gontar et al. (2017); FSF, 2014; Stader (2014); Bennet (2019); Rodrigues et al. (2025)
14	Responding to operational irregularities	CAA, 2023; Xu & Zhang (2022); Bennet (2019); Bao et al. (2019); Ferraro et al. (2017); Stader (2014); Estes et al. (2018); Yıldız (2024); Rankova (2023)
15	Reviewing charts	Wilkins (2018); Watkins et al. (2018)
16	Understanding traffic patterns	Wickens et al. (2017); Dill & Young (2015)
17	Unfamiliar aircraft	Boldmethod (2024)
18	Usage of non-essential electronics	Boyne (2013), Wilkins (2018); Watkins et al. (2018); Wentzel & Deaton (2018); Boldmethod (2024); Zarlengo (2025)
19	Using in-vehicle controls	Wilkins (2018); Yadav et al. (2022); Yeh et al. (2016); Estes et al. (2018)
20	Working in unfamiliar airspace	Boldmethod (2024)

After that, the sources were grouped and organized by the authors under (a) communication, (b) head-down time spent, (c) responding to abnormal conditions & unexpected situations, and (d) searching for traffic and landmarks for visual flight rules according to the general classification of Dismukes et al. (1998), Sparks (2008), and Xie et al. (2024a). This enabled the construction of a hierarchy, in which the identified sources were assigned to the associated categories as follows:

(a) The communication category included ATC communication, backseat pilot communication, captain to F/O communication, coordinating with ground teams, and responding to the cabin crew.

(b) The head-down time sources were quantified as accessing onboard information systems, programming navigation instruments, reviewing charts, understanding traffic patterns by instrument flight rules (IFR), usage of non-essential electronics, and using in-vehicle controls.

(c) Cluttered avionics, dealing with bright light, laser, dealing with environmental factors, dealing with technical interruptions, and handling FOD were grouped under responding to abnormal conditions and unexpected situations.

(d) Sources for distraction under the category of searching for traffic & landmarks for the case of the visual flight rules (VFR) were determined as operating in extensive traffic, responding to operational irregularities, and working in unfamiliar airspace.

Interview Results

Following that, interviews with experts, that is, airline pilots, were conducted to test, refine, and finalize this hierarchy, ultimately delivering an inclusive model. Consequently, the following changes were made to the source categories:

(a) The communication category was complemented by adding communication with other aircraft and communication with the company.

(b) Head-down time, in-vehicle controls were exemplified by the overhead panel, the command and display unit (CDU), and the mode control panel (MCP). There, the reading of displays and monitoring tasks were also added.

(c) Operational irregularities such as delays, slots, baggage, passenger reduced mobility (PRM), and curfew were added under the category of responding to abnormal conditions and unexpected situations. There, dealing with environmental factors is further explained by adding ‘meteorological conditions’. Moreover, less experience in type, that is, aircraft type, is added.

(d) In searching for traffic & landmarks, the source operational irregularities were not well understood, so it was converted into airspace with high terrain, and limited sight/low visibility operation.

This yielded a classification with 4 categories and 26 associated sources, as seen in Table 2.

Table 2.

Classification of pilot distraction/interruption sources

Level 1	(a)	(b)	(c)	(d)
Level 1	Communication	Head-down time	Responding to abnormal conditions & unexpected situations	Searching for traffic & landmarks (VFR)
Level 2	Captain to F/O Backseat pilot Cabin crew ATC Other aircraft Ground team Company	Understanding traffic patterns (IFR) Reading displays Non-essential electronics (mobile phones, etc.) Reviewing charts Onboard information systems In-vehicle controls (overhead panel, CDU, MCP) Programming navigation instruments Monitoring tasks	Technical interruptions Less experience in type Environmental factors (Meteorological conditions) Bright light, laser Cluttered avionics (too much info) FOD Operational irregularities (delays, slot, baggage, passenger reduced Mobility (PRM), curfew)	Extensive traffic Airspace high terrain Limited sight/Low visibility operation Unfamiliar airspace

AHP Results

Then, the same expert group, consisting of pilots, is interviewed again for pairwise comparisons of the elements of the hierarchy set by the authors, based on the literature and the results of the first-round interviews. There, Saaty’s linear scale (see Table 3) is used for pairwise comparisons to determine the weights of categories and subordinate sources using the AHP method; the results are explained below.

Table 3.

Linear Pairwise Comparison Scale used, based on Saaty (1990)

Rating	Scale	Reciprocal
1	Equally important	1
3	Moderately important	1/3
5	Strongly important	1/5
7	Very strongly important	1/7
9	Extremely important	1/9

The AHP assessments began with the Level 1 categories. Individual comparison matrices of each expert were averaged, giving equal weight to each judgement. Hence, aggregated group assessments were used, yielding a high consensus (S) of 81.6% across the categories, delivering associated weights of 16%, 9%, 59.2%, and 15.8%, for communication, head-down time, responding to abnormal conditions/unexpected situations, and searching for traffic & landmarks (VFR), respectively (see Table 4).

Table 4.

Aggregated comparison matrix and weights of the categories

		a	b	c	d	Weight
Communication	a	1	11/5	1/3	11/5	16.0%
Head-down time	b	5/6	1	1/7	2/5	9.0%
Responding to abnormal conditions/Unexpected situations	c	3	73/7	1	43/8	59.2%
Searching for traffic & landmarks (VFR)	d	5/6	24/9	2/9	1	15.8%

^*S = 81.6%; CR = 3.5%.

The most influential criterion was determined as (c), with such a high dominance that the criteria weight sensitivity analysis for (a) is descriptive (see Figure 3), where changes were examined for ±30%. The only rank instability is observed between (b) and (d), but considering that it does not alter the decision structure, the results were considered robust.

Figure 3.

Criteria weight sensitivity plot for the perturbation of (a) with Δ = ±30%

After that, communication-related sources were examined within group (a) on Level 2. While the aggregated group assessment indicated a high consistency ratio of 2.9%, indicating ATC and the captain to F/O communications as the most distracting items, with weights of 25.1% and 24.4%, respectively, Similarly, the communications with ground teams, the company, the cabin crew, the backseat pilot, and other aircraft were weighed at 12.4%, 11.5%, 10.2%, 8.6%, and 7.9%, respectively (see Table 5).

Table 5.

Aggregated comparison matrix and weights of sources subordinated to (a) communication

		a.1	a.2	a.3	a.4	a.5	a.6	a.7	Weight
Captain to F/O	a.1	1	31/2	2	1	31/9	21/3	13/4	24.4%
Backseat pilot	a.2	2/7	1	2/3	1/2	11/4	3/5	2/3	8.6%
With cabin crew	a.3	1/2	13/7	1	2/5	11/5	4/5	7/9	10.2%
With ATC	a.4	1	2	23/5	1	3	21/2	21/2	25.1%
With other aircraft	a.5	1/3	4/5	5/6	1/3	1	2/3	2/3	7.9%
With ground teams	a.6	3/7	12/3	11/4	2/5	14/9	1	13/7	12.4%
With the company	a.7	4/7	13/7	12/7	2/5	11/2	5/7	1	11.5%

^*S = 62.6%; CR = 2.9%.

Since (a.4) is dominant, the sensitivity analysis was made by perturbing it (see Figure 4). There was no global rank instability, but only a change in (a.1) due to normalization, indicating that the decision structure is robust.

Figure 4.

Criteria weight sensitivity plot for the perturbation of (a.4) with Δ = ±30%

Nevertheless, the consensus level for communication remained moderate at 62.6%. This was mainly assumed to result from some of the F/O’s assessments, particularly Expert 6, who did not emphasize the captain to F/O communication as a significant distractor. This raised the question of the potential moderating effect of formative background, total experience, and civil aviation experience because Expert 6 is the youngest F/O with the lowest CR. Unfortunately, the sample was not large enough for a statistical analysis, and the research design did not include any information on the pilots’ formative background, that is, their education. Thus, given that the sample was unsuitable for such an analysis, but the consolidated consistency was provided, Expert 6 was not excluded, and future research was planned to investigate such asymmetries at a later stage.

Then, the sources under head-down time were examined within group (b) on Level 2. Although the averaged results indicated an excellent consistency ratio of 1.1%, there was moderate consensus regarding the distractors during the head-down time. While non-essential electronics, such as mobile phones, and monitoring tasks were identified as distractors with lower importance weights of 4% and 8.8%, respectively, the remaining items were similarly weighted, ranging from 12.1% to 15.9%. It was noted that there were no significant differences in weight between the F/O and captain groups, with variations occurring on an individual basis. Consequently, the aggregated values are assumed to represent the sample well (see Table 6).

Table 6.

Aggregated comparison matrix and weights of sources subordinated to (b) head-down time

		b.1	b.2	b.3	b.4	b.5	b.6	b.7	b.8	Weight
Understanding traffic patterns (IFR)	b.1	1	1	1	1	35/8	13/8	11/2	12/3	15.9%
Onboard information systems	b.2	1	1	2/3	2/3	32/5	3/5	11/6	15/7	12.5%
Reading displays	b.3	1	11/2	1	11/7	35/9	5/6	11/2	2	16.3%
In-vehicle controls	b.4	1	14/9	7/8	1	23/4	11/7	11/3	11/2	14.8%
Non-essential electronics	b.5	2/7	2/7	2/7	3/8	1	1/4	1/4	1/3	4.0%
Programming navigation instruments	b.6	5/7	12/3	11/5	7/8	32/3	1	11/4	18/9	15.7%
Reviewing charts	b.7	2/3	6/7	2/3	3/4	33/4	4/5	1	17/9	12.1%
Monitoring tasks	b.8	3/5	4/7	1/2	2/3	31/5	1/2	5/9	1	8.8%

^*S = 60.0%; CR = 1.1%.

Considering that (b.3) has the highest weight, the sensitivity analysis was made by perturbing it, which indicates a robust assessment due to the largely insensitive lower-weight criteria and the only alterations due to the normalization (see Fig. 5).

Figure 5.

Criteria weight sensitivity plot for the perturbation of (b.3) with Δ = ±30%

After that, the sources under abnormal conditions & unexpected situations were examined within group (c) at Level 2, yielding a high CR of 1.3% with a moderate consensus of 63.8%. This was mainly due to Expert 9, who emphasized the bright laser as the most important source despite other members of the expert group having different opinions. Nevertheless, since the group CR is at a high level, this response was not excluded from the analysis. Consequently, technical interruptions, less experience in type, and environmental factors were ranked as the top three distractors with 26.7%, 23.7%, and 15.2%, respectively. While operational irregularities were also weighed significantly at 13.9%, cluttered avionics, FOD, bright light, and laser were ranked lower at 8.4%, 7.4%, and 4.8%, respectively. Compared to other items, lasers were ranked low in this category, contradicting the literature. Nevertheless, since this category has a higher weight than the others in the higher order, this distractor was expected to be significant in the overall ranking (see Table 7).

Table 7.

Aggregated comparison matrix and weights of sources subordinated to responding to (c) abnormal conditions & unexpected situations

		c.1	c.2	c.3	c.4	c.5	c.6	c.7	Weight
Technical interruptions	c.1	1	35/7	11/6	31/7	11/2	35/6	26/7	26.7%
Cluttered avionics	c.2	1/4	1	4/9	11/5	1/2	22/5	2/5	8.4%
Less experience in type	c.3	6/7	22/7	1	31/7	2	34/5	21/5	23.7%
FOD	c.4	1/3	5/6	1/3	1	1/2	11/2	5/9	7.4%
Environmental factors	c.5	2/3	2	1/2	2	1	34/9	11/5	15.2%
Bright light, laser	c.6	1/4	3/7	1/4	2/3	2/7	1	2/7	4.8%
Operational irregularities	c.7	1/3	21/2	1/2	14/5	5/6	32/3	1	13.9%

S = 74.6%; CR = 1.3%.

Considering that (c.1) has the highest weight, the sensitivity analysis was made by perturbing it, which indicates a robust assessment due to the largely insensitive lower-weight criteria and the only alterations due to the normalization (see Figure 6).

Figure 6.

Criteria weight sensitivity plot for the perturbation of (c.1) with Δ = ±30%

Finally, the evaluation of the last group of sources, which focused on (d) searching for traffic & landmarks (VFR), yielded a CR of only 0.3% at a moderate consensus level of 64.2%. There, airspace high terrain was the most significant source of distraction, accounting for 36.2% of the total, followed by limited sight/low visibility operations, which accounted for 28.2%. Extensive traffic and unfamiliar airspace were weighed at 19.4% and 16.2%, respectively (see Table 8).

Table 8.

Aggregated comparison matrix and weights of sources subordinated to searching for traffic & landmarks (VFR)

		d.1	d.2	d.3	d.4	Weight
Extensive traffic	d.1	1	5/9	11/3	2/3	19.4%
Airspace high terrain	d.2	14/5	1	2	12/7	36.2%
Unfamiliar airspace	d.3	3/4	1/2	1	2/3	16.2%
Limited sight/low visibility operation	d.4	11/2	7/9	15/9	1	28.2%

S = 64.2%; CR = 0.3%.

The sensitivity analysis of perturbing the most significant criterion (d.2) also confirmed robustness, with the only rank reversal occurring due to normalization (see Figure 7).

Figure 7.

Criteria weight sensitivity plot for the perturbation of (d.2) with Δ = ±30%

All in all, the weighted hierarchy of sources of pilot distractions is presumed to be as shown in Figure 8, where each source is subordinated below a category.

Figure 8.

Sources of pilot distractions and interruptions with their weights as set in the hierarchy

Discussion

This work was presented as a methodology paper intended to provide a framework for global research by utilizing two contemporary techniques: AI for the literature review and AHP for aggregating distraction/interruption constructs into a single usable hierarchy.

Review Discussion

The literature review included the Pilots’ opinions only in a limited scope. The quantifications, likes/dislikes, and potential sources were identified in the literature through interviews with pilots (Bennett, 2019; Wiener, 1989), surveys (Bandeira et al., 2018), and analysis of Aviation Safety Reporting System (ASRS) reports (Dismukes et al., 1998; Nowinski et al., 2003). However, there is no comparison of these items to prioritize them. There were case-by-case assessments of the effects of various distraction sources, such as laser exposure (Nakagawara et al., 2008); however, to the best of the authors’ knowledge, no work prioritizes these sources. There is scholarly work on the root causes of accidents presented in percentages (Bandeira et al., 2018; FSF, 2014; ICAO, 2024; Kelly & Efthymiou, 2017; Nakagawara et al., 2008), which enables the testing of findings, but they remain incomplete in building a systematic model that incorporates the pilots’ perceptions. Consequently, a list of sources was compiled through review, then evaluated and refined through interviews. This was then converted to a hierarchy, which successfully answers the first two research questions, RQ1 and RQ2.

During the creation of the list of distraction/interruption sources, an integrated list was compiled, including both distractions and interruptions, as the targeted model was a general one that supports the assessment of cockpit design/procedures. In scholarly work as well as in practice, their distinction is evident. However, considering that distraction is a unique case of an interruption (Fletcher et al., 2018) and even primary duties at an inappropriate timing can transition into distractions, the interviewed pilots were not asked to focus on the nature of the distinction, whether it resulted from an interruption by a component of the flying task or from a distraction. Consequently, this model does not differentiate between cognitive mechanisms underlying distractions and interruptions; rather, it treats them as functionally equivalent in their ability to divert attention. In this research, it is more important to isolate potential sources that provide a framework and thus a tool for assessment, which aligns with the early origins of the NASA research by Wiener (1989). Here, the idea is to explore the weak points in operational procedures and design elements so that the associated distraction/interruption sources can be elaborated in detail, involving methods including but not limited to HFACS, Fault Tree Analysis (Lu et al., 2006), and root cause analysis (Molan & Molan, 2021).

Interview Discussion

The experts noted that most workload and distractions occur during the taxi, take-off, approach, and landing phases, despite the use of modern airliner cockpits. This comment aligns with the existing literature (Lee & Liu, 2003; Liu et al., 2023; Wang, Zhang, et al., 2024). Considering the workload is lowest during cruise (Liu et al., 2023), the critical time for both short- and long-haul is the same, and thus analogy can be assumed for long-haul as well, where the only difference is the cruise at high altitude; however, future research plans to interview long-haul pilots as well.

The top 10 sources of distraction in the cockpit were determined as technical interruptions, less experience in type, environmental factors (meteorological conditions), operational irregularities (delays, slot, baggage, passenger reduced mobility, curfew), airspace high terrain, cluttered avionics (too much info), limited sight/low visibility operation, FOD, communication with the ATC, and communication of captain to F/O. This group had a coverage of 74.5%, while it is worth noting that the top 5 sources of distractions and interruptions accounted for 52.8% of all items.

AHP Discussion

Pairwise comparisons provided weights (W) and thus ranked the items in the set. To determine the overall importance, that is, overall weights (OW), the weights were multiplied by the category weights (CW). This delivered a global ranking that adequately addresses RQ3, the final research question, and also provided a structured model for assessing cockpit designs and operational procedures (see Table 9).

Table 9.

Factors of importance of sources of pilot distractions and their ranking

Rank	Designation	Source	Weight (W)	Category weight (CW)	Overall weight (OW)^a
1	c.1	Technical interruptions	0.267	0.5920	0.1581
2	c.3	Less experience in type	0.237	0.5920	0.1403
3	c.5	Environmental factors (meteorological conditions)	0.152	0.5920	0.0900
4	c.7	Operational irregularities (delays, slot, baggage, passenger reduced mobility (PRM), curfew)	0.139	0.5920	0.0823
5	d.2	Airspace high terrain	0.362	0.1583	0.0573
6	c.2	Cluttered avionics (too much info)	0.084	0.5920	0.0497
7	d.4	Limited sight/Low visibility operation	0.282	0.1583	0.0446
8	c.4	FOD	0.074	0.5920	0.0438
9	a.4	With ATC	0.251	0.1599	0.0401
10	a.1	Captain to F/O	0.244	0.1599	0.0390
11	d.1	Extensive traffic	0.194	0.1583	0.0307
12	c.6	Bright light, laser	0.048	0.5920	0.0284
13	d.3	Unfamiliar airspace	0.162	0.1583	0.0256
14	a.6	With ground teams	0.124	0.1599	0.0198
15	a.7	With the company	0.115	0.1599	0.0184
16	a.3	With cabin crew	0.102	0.1599	0.0163
17	b.3	Reading displays	0.163	0.0898	0.0146
18	b.1	Understanding traffic patterns (IFR)	0.159	0.0898	0.0143
19	b.6	Programming navigation instruments	0.157	0.0898	0.0141
20	a.2	Backseat pilot	0.086	0.1599	0.0138
21	b.4	In-vehicle controls	0.148	0.0898	0.0133
22	a.5	With other aircraft	0.079	0.1599	0.0126
23	b.2	Onboard information systems	0.125	0.0898	0.0112
24	b.7	Reviewing charts	0.121	0.0898	0.0109
25	b.8	Monitoring tasks	0.088	0.0898	0.0079
26	b.5	Non-essential electronics	0.040	0.0898	0.0036

^aOW = W × CW.

With a weight of 15.8%, technical interruptions were identified as the most critical source of distractions. However, communication has been identified in the literature as a significant distractor and primary interrupter (Bennet, 2019; Estes et al., 2018). At first glance, this may seem contradictory, but considering that the communication in this work is divided into seven distinct types, whose cumulative general weight is 16.01%, this is entirely in line with the literature, where Gontar et al. (2017) found a similar weight of 17.2% each for technical interruptions as well as for communication. Hence, communication in general is similarly weighted as technical interruptions; thus, future cockpit design and procedure design must focus on minimizing interaction. Moreover, communication is used as both a classification and a unifying source for its subheadings, and is designated as the primary source, while technical interruptions are designated as the secondary source, accounting for 15.8% of distractions and interruptions. Communications related to heading changes ordered by the ATC during traffic congestion can be both inherent and critical, but they can also become excessive and disruptive when they force unnecessary focus changes, which illustrates the complex nature of interactions and the construct ambiguity. Hence, the model is set with a communication category to enable an assessment; pilots perceive it as a duty and a perturbation, depending on when it occurs.

Another important point to note is that less experience with the type was identified here as the third important source, with a weight of 14.03%. This is interesting because experienced pilots with over 14,000 hours of flight time were also found to have reduced attention due to overconfidence (Kelly & Efthymiou, 2017). This shouldn’t be evaluated as a contradiction but rather handled as a nonlinear relationship; the source name can be updated to ‘experience’ in type to enhance generality. Consequently, while limited experience increases cognitive workload and reduces attentional reserves, very high levels of experience might lead to reduced vigilance. This requires that procedural design for crew resource management (CRM) must focus on workload management for pilots with low experience and complacency awareness for highly experienced pilots. This relies on the utilization of the working memory and long-term working memory (Xi et al., 2024). Airlines can also, during recruiting and training, focus on working memory capacity for less experienced pilots, while emphasizing long-term working memory for experienced pilots to enhance safety.

Approximately 94% of controlled flight into terrain (CFIT) accidents are attributed to meteorological conditions that restrict vision (Kelly & Efthymiou, 2017). Generally, environmental factors such as adverse weather (Li et al., 2001) and noise (Bandeira et al., 2018; Yeh et al., 2016) increase the likelihood of pilot error. The pilots interviewed in this research also ranked environmental factors as a critical source, ranking third with an overall weight of 9%, which aligns with the existing literature.

Operational Irregularities (delays, slots, baggage, passenger reduced mobility (PRM), and curfew) were also identified as a major source, accounting for 8.23%. Loukopoulos et al. categorize these in (2001) and (2016) as intrusions or perturbations and claim that there is a linearity conflict in flight manuals portraying the cockpit work without real-world perturbations. Considering that these operational irregularities can arise from inadequate information sharing and poor management, this finding is also consistent with Bandeira et al. (2018), who attributed inadequate information sharing to 12%. Moreover, given that replanning accounts for 81% of CFIT accidents in associated HFACS models (Kelly & Efthymiou, 2017), this finding is further supported.

Although results are unique for the population, and a recalibration is due to differing cockpit, regional, and airline protocol procedures, the top 5 sources were determined to account for communications, technical interruptions, experience in type, environmental factors, and operational irregularities, which collectively account for more than half, with a cumulative weight of 63.07% (see Figure 9).

Figure 9.

The top five sources of pilot distractions and interruptions and their weights

These results were also successfully evaluated using a criteria-weight analysis for robustness, which included perturbations. There were no global rank reversals, and only gradual and interpretable changes occurred, indicating the stability of the priority structure (Dweiri et al., 2016; Maletic et al., 2014).

It is recommended that policies and practices for distractions be implemented in aviation (FSF, 2014). This research not only focuses on the behavioural reaction of pilots to distractions and interruptions from the perspective of the pilots themselves, but also on the potential assessment of cockpit and procedure design. The list of sources determines, in practice, all required fields of action. Even if focussing only on the first five sources, most distractions can be addressed. As a result, this prioritized list can serve as a guideline for assessing and further developing current procedures and practices, focussing on the severity of potential distractions and the existing mitigation potential. It enables the inclusion of pilots’ voices in the conceptualization of operational procedures related to safety management. Moreover, poor design can lead to errors, particularly in high workload situations (Yeh et al., 2016). The hierarchy set herewith provides a taxonomic structure suitable for assessing cockpit and procedure design, where alternatives can be scored within each dimension, i.e., source of distraction, which are then multiplied by the OWs, yielding a sum score for comparison. This enables the comparison of alternatives using a structured, reproducible methodology.

Limitations

Construct ambiguity is a limitation. Although the AHP model can aggregate sources for assessment, the interrelationships among interruptions and distractions remain a complex human-factors challenge. Given that a distraction is a unique case of an interruption (Fletcher et al., 2018), distinguishing between them based on pilot perceptions is not easy. Nevertheless, the difference between inherent flight tasks and distractions is not only a scholarly one and should be approached with extraordinary care. The proposed model aims to evaluate cockpit designs and operational procedures with respect to distractions and interruptions. The purpose justifies the integrated model developed, but the results should be used within this limited scope. The primary contribution of this work is not the current ranking outcomes themselves, but the reproducible framework for eliciting, structuring, and prioritizing pilot-perceived distraction sources across broader regions, airlines, and flight paths.

Another limitation is the small, homogeneous sample of pilots flying narrow-body aircraft in European airspace. This is a statistical limitation that prevents the assessment of potential moderating variables such as educational background and total experience. Furthermore, demographics were not examined, which is also a future research area. Then, the sample of pilots did not include long-range airline pilots, which is a further limitation. Although most distractions and interruptions occur during the approach, landing, and take-off, as in long-range flights on wide-body aircraft, findings should not be generalized to all airline operations without caution. Similarly, the weights should not be interpreted as transferable across fleets, regions, or pilot populations without recalibration. Future research is needed to further test the model with long-range airline pilots before using it for assessment purposes in wide-body aircraft.

Finally, although the AI algorithm for the literature review prevented bias toward more-cited work by evaluating all related papers, the ranking of papers based on similarity, quality, and content relied on the explicit inclusion of certain keywords and did not account for variants. Furthermore, AI tools are sensitive to the prompt used and previous prompts in the session. Although a clean session is used, Python coding is planned for future research to absolutely prevent potential prompt sensitivity. Consequently, some opacity can be assumed regarding the exclusion criteria for papers, which is a limitation. Nevertheless, the papers used were also checked manually by the authors and confirmed as appropriate; however, due to the vast number of documents, not all excluded papers were checked manually for inappropriateness. Instead, spot checks were conducted on 5% of the eliminated papers, totalling 12, in which no evidence was found against the exclusion of any of them. Thus, further research is suggested to focus on the impact of ontologies and related exclusion logic to increase the robustness of the methodology presented here.

Conclusions

In the airline industry, safety is the most crucial factor for the continued operation of commercial business. Any fault or incident can have a negative impact on both human beings and the company. Because of this, the airline community keeps safety above all other aspects. As mentioned above, situational awareness is a key element of safety. Any interruption or distraction during the critical phases of flight can degrade the overall performance and can lead to an incident or accident. As stated in the literature review, considerable work has been done on the issue; however, neither flight crew perceptions nor a structured model to assess design and policies were part of it.

Consequently, this work has been motivated by the lack of a structured approach to summarizing cockpit distractions from the perspective of airline pilots. The literature review also employed a purposive application of AI tools, thereby providing a methodology. This resulted in quantifying various sources; however, they were not compared with one another. Therefore, prioritization is a key contribution of this work, achieved through interviews with airline pilots and the AHP method. Consequently, related sources were classified, and the top 5 sources identified were communications, technical interruptions, experience in type, environmental factors, and operational irregularities. This delivered a structured model suitable for representative analysis, which recognizes the dual nature of flight tasks as essential duties and potential distractors.

Another important academic contribution of this work is that it is the first scholarly study to focus on the pilots’ perceptions. The model was freed from ontologies and incorporated vital elements based on literature and practical experience, delivering a priority structure and a reproducible methodology. Hence, it delivered an inclusive model for safety management, which can serve as a basis for assessing human-factor-related distractions. As a result, the work provided a hierarchical model with weights, serving as a basis for evaluating and comparing various alternatives to assess cockpit designs or operational procedures, which also has managerial implications.

Additionally, this research could lead to revisions of Standard Operating Procedures to support the flight crew in preventing undesirable events. These findings may draw the attention of the flight crew to manage their limited resources optimally and provide them with an early warning of potential hazardous situations. By understanding the flight crew’s perspective when developing new procedures, regulators can use this input to create safer, more realistic outcomes. From the practitioner’s perspective, this prioritization also provided the means to assemble a preventive action plan for aircraft operations.

Although this work was conducted with only a sample of nine airline pilots, the consistency ratios of the aggregated comparison matrices were satisfactory. Consequently, applicability is limited to similar operational contexts. Moreover, it has been shown that perceptions of the flight experience can be biased. The investigated airline pilot group was conducting only short- and medium-haul flights. Although the approach and landing phases in long-haul flights are generally the same, and there are obviously common sources of distraction and interruption, future studies are planned to examine variations in long-haul operations. The planned future research also includes an investigation of the change in perception of distractors, moderated by age, flight experience, and educational background.

Key Points

• Distractions and interruptions in the cockpit are critical precursors to human error and potential accidents.

• A methodological and reproducible hierarchical framework is delivered here, quantifying pilot perceptions.

• The top 5 sources are determined as communications, technical interruptions, experience in type, environmental factors, and operational irregularities, covering 63%.

• This framework can be used for airline safety assessment and cockpit design evaluation, respecting the dual nature of flight tasks where inherent tasks can transition to disruptions.

Supplemental Material

Supplemental Material - Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process

Supplemental Material for Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process by Caglar Ucler, Mehmet Onur Balkan in Human Factors.

Footnotes

ORCID iDs

Caglar Ucler

Mehmet Onur Balkan

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Caglar Ucler, Assoc. Prof. Dr. Ozyegin University, Faculty of Aviation & Aeronautical Sciences. Degree: (1) PhD in engineering management (2009), Marmara University, Istanbul, Turkey. (2) Dipl.-Ing. in aerospace engineering (2000), TU München, Munich, Germany.

Mehmet Onur Balkan, Asst. Prof. Dr. & airline pilot. Ozyegin University. Faculty of Aviation & Aeronautical Sciences. Degree: (1) PhD in management and organisational behaviour (2010), Inonu University, Malatya, Turkey. (2) MSc in management and organisational behaviour (2010), Inonu University, Malatya, Turkey. (3) BS in systems engineering (1999), Turkish Military Academy, Ankara, Turkey.

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