*3.1. Accident Analysis with MxFACS*

Insley's MxFACS was selected to analyse the accident reports. The taxonomy consists of a three-level hierarchy:


The MxFACS taxonomy makes use of the Bowtie Risk Assessment Model to identify risks, causal and contributory factors. The three levels are derived from the "top event" element, "consequence" element and "threats" element, respectively, as shown in Figure 2. The maintenance error is taken as an equivalent of the "hazard" in each accident. It aids in identifying the action, the outcome and the context which are three of four basic elements of error [16].

The data contained in the Accident Investigation Bureau (AIB) publications were in PDF. Therefore, each report published in the last decade was downloaded. The documents were thoroughly reviewed in order to identify the maintenance-related accidents. After identification, each accident report was analysed and coded by using the MxFACS taxonomy structure.

**Figure 2.** Bowtie Risk Assessment Model, CGE Risk Management Solutions (2017).

### *3.2. Incident Analysis with Hieminga's Maintenance Incident Taxonomy*

Hieminga's taxonomy was selected to analyse the reports which were sourced from the Safety Deficiency Incidence Analysis (SDIA) mandatory occurrence report database. The taxonomy consists of a two-level hierarchy:


Although it gives the reporter the opportunity to select a descriptor that is as precise as possible due to its broad spectrum [17], it is a very broad coding system and could confuse maintenance personnel. This is because one incident could be coded into more than one first and second level category. This taxonomy utilises familiar words for classification which makes it easier for maintenance personnel to report incidents appropriately.

The data contained in the SDIA dataset were thoroughly analysed. All maintenance-related incidents were identified and classified in accordance with Hieminga's template.

### *3.3. Collection of Data: Accident Investigation Reports Available to the Public*

The process of collecting maintenance-related accident data involved downloading all accident reports available to the public. All commercial aircraft category and general aviation category accident reports published in the last decade, i.e., 2009–2019, were identified and downloaded.

There was a total of 70 accidents published in Portable Document Format (PDF). In order to identify the maintenance-related accidents, each document was analysed by the lead author. The next step was to identify the maintenance-related accidents and compile them in a dataset. All accident investigation reports that occurred in Nigeria were sourced from Nigeria's Accident Investigation Bureau's (AIB) publication database.

The AIB's database contains official documented reports of all previous civil accidents in Nigeria. It is the only aviation parastatal in Nigeria with the principal responsibility of performing independent investigations into aviation accidents and making safety recommendations to the relevant agencies [38] and this satisfies the standards and recommended practices defined in ICAO Annex 13.

### *3.4. Collection of Data: Mandatory Occurrence Reports (MORs)*

The MORs were sourced from the Nigerian Civil Aviation Authority (NCAA). In particular, it was received from the Safety Deficiency Incidence Analysis (SDIA) unit under the Directorate of Airworthiness Standards (DAWS).

The NCAA was established in 1999 to comply with ICAO's requirements. ICAO required every member state to set up an organisation with the responsibility of ensuring compliance with air navigation rules [6]. NCAA is responsible for the safety oversight of the aviation industry in Nigeria.

All civil aviation events that meet the criteria for reportable occurrences defined in the NCARs are reported to the NCAA through an MOR form. These incidents must be reported within 72 h of its occurrence [3]. The incidents are then analysed, uploaded to SDIA's Google Drive database and monitored till closure.

A total of 2530 incidents from 2006 to 2019 were stored in the SDIA MOR database. The data were de-identified before analysis for the purpose of confidentiality. All the incidents were analysed to discern maintenance-related incidents. They were compiled in a dataset and reviewed to ensure that only maintenance-related events were considered.

### *3.5. Collection of Data: Subject Matter Experts (SMEs)*

Feedback, clarification and recommendation were sought from three di fferent groups of SME. This was to acquire relevant information for recommending next steps of the data analysis. Another reason was to create awareness of the process of developing customised taxonomy for maintenance-related accidents and incidents in Nigeria. The three di fferent groups of SMEs were;


The data were collected by sending links of the three di fferent surveys to three di fferent groups. This was distributed via online questionnaires (shown in Appendix D). These were administered by the Qualtrics software.

The first questionnaire contained four open-ended questions. This was responded to by NCAA SME participants. Their experience of collecting and analysing the MORs in Nigeria made them adequately qualified to contribute to this study. The questions covered the following topics:


The second questionnaire contained three open-ended questions. This was responded to by AIB SME participants. Their wealth of experience in carrying out accident investigations, developing the reports and publishing them made them adequately qualified to contribute to this study. The questions covered the following topics:


The third questionnaire contained three open-ended and five multiple-choice questions. It was distributed to AMEs practising in Nigeria. A total of 25 responses were received. The question covered topics related to:


All the questionnaires were sent along with a PowerPoint presentation containing the aims, objectives and methodology. The first two surveys were accompanied by an Excel worksheet containing the taxonomies used and sample data to enable them to code the data using the taxonomies. The data output of this study was also presented to the first two SME groups.

These questionnaires were also sent out due to the limited data available on Nigeria aviation industry. An inductive approach was used to gather the information for this qualitative aspect [39].
