- Email: [email protected]
Structuring risk factors related to airline cabin safety
Journal of Air Transport Management 20 (2012) 54e56
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Structuring risk factors related to airline cabin safety Yueh-Ling Hsu*, Te-Chang Liu Department of Air Transportation, Kainan University, No. 1, Kainan Road, Luzhu, Taoyuan County 33857, Taiwan
a b s t r a c t Keywords: Aircraft cabin safety Risk management Aircraft safety
This study explores cabin safety by mapping it as the second-order factor into a theoretical framework based on the 5-M model. A questionnaire, together with structural equation modeling is used to verify the proposed model. The results suggest that cabin safety can be measured by a systemic factor set e media, man, machine, management and mission. A causal relationship between 5-M factors and 19 variables is also found; mission factor together with, hardware-design and cabin safety training, have the greatest effects on cabin safety. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
attitude and behavior. In this regard the International Civil Aviation Organization (2006) pinpoints company/management commitment, a positive safety culture, SOPs, checklists and briefings, hazard and incident reporting, training for cabin safety, and cabin safety standards. Our model of risk factors for cabin safety is founded in the International Air Transport Association Operational Safety Audit checklist in IOSA Standards Manual (International Air Transport Association, 2008b) relating to cabin safety risks and risk factors categorized using the “5-M model” (media, man, machine, management, mission). This provides a framework for analyzing systems and determining relationships between the elements that work together to achieve a goal (US Federal Aviation Administration, 2000). Fig. 1 shows the second-order factor analysis model structure. “Cabin safety” is the second-order factor (x) while media “man”, “machine”, “management”, and “mission” are first-order factors (h); the first-order factors are indicators of the second-order factor. The second-order factor model comprises of five first-order factors, which are measured by 19 observable variables or sub-factors.
To raise public confidence, as well as, survivability airlines take steps to deal with the risk of accident and injury in air travel. However, most analyses focuses on improving flight safety, i.e. the prevention of accidental occurrence, or a single risk related to cabin safety such as disruptive passengers. Here we identify risks related to cabin safety by using the 5-M model, fuzzy theory and structural equation modeling (SEM).
2. The conceptual background The range of threats to the aircraft and its occupants include in-flight turbulence, smoke or fire in the cabin, decompression, emergency landings, emergency evacuations and unruly passengers, passenger medical issues, and human factors issues. The International Air Transport Association (2008a) states that cabin safety operation include proactive data collection and ensuing preventive activities regarding cabin design and operation, equipment, procedures, crew training, human performance, and passenger management; thus implying that it covers a wide range of areas including crashworthiness, operations, human factors, psychology, bio-dynamics, physiology, ergonomics, and pedagogy (Transport Canada, 2009). Moreover, human factors extend beyond the man-machine and individual human error areas and migrate towards organization and culture (Maurino et al., 1995). A number of recent cabin incidents in the airline industry have not only highlighted the need for airline operators to pay more attention to cabin safety but also to improve the cabin environment, which affects crew’s safety
* Corresponding author. Tel.: þ886 (0) 3 3412500; fax: þ886 (0) 3 3016912. E-mail address: [email protected] (Y.-L. Hsu). 0969-6997/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2011.12.009
3. Methodology To test the model, a survey was conducted with the first part designed to obtain information on the gender, age, job position, number of years with the company, years employed in aviation, and education level of respondents. Because it is difficult to reasonably convey in questionnaires situations that are overly complex or difficult to define, the notion of linguistic variable is used (Zadeh, 1975). Respondents were asked to specify the range from one to 100 corresponding to linguistic terms ranging from “very high impact” to “very low impact”. The evaluation criterion evaluated by respondents was therefore measured as fuzzy number with triangular membership function.
Y.-L. Hsu, T.-C. Liu / Journal of Air Transport Management 20 (2012) 54e56
55
Fig. 1. The second-order factor analysis model structure.
The survey was then distributed to all cabin crew and personnel in the safety department of a domestic Taiwanese airline between January and March 2008. Two hundred questionnaires were distributed and 184 usable samples were obtained after deleting incomplete responses. Seventy-nine percent of respondents were
female; 46% were aged between 30 and 39 and 42% were between 20 and 29 with 39% being at their company for six to ten years, and 35% for under five years. Fifty-six percent of respondents had at least a bachelor’s degree.
Table 1 Modeling results.
Table 2 Model fit.
Construct Media
Man
Machine
Management
Mission
Item MD1 MD2 MD3 MN1 MN2 MN3 MN4 MC1 MC2 MC3 MC4 MG1 MG2 MG3 MG4 MG5 MS1 MS2 MS3
Item loading
Factor reliability
Factor loading
Average variance extracted
0.67 0.42 0.91 0.85 0.7 0.75 0.67 0.79 0.89 0.73 0.79 0.73 0.8 0.81 0.91 0.77 0.73 0.76 0.67
0.85
0.82
0.5
0.89
0.9
0.6
0.93
0.95
0.6
0.95
0.91
0.7
0.85
0.98
0.5
Fit index
c2 P-level df c2/df GFI SRMR RMSEA AGFI NFI RFI IFI TLI (NNFI) CFI PGFI PNFI PCFI
Acceptable level
First trial
Second trial
>0.05 .. <3 >0.9 <0.05 <0.08 >0.90 >0.90 >0.90 >0.90 >0.90 >0.90 >0.50 >0.50 >0.50
400.95 0 147 2.728* 0.81 0.053 0.097 0.75 0.84 0.81 0.89 0.88 0.89 0.62* 0.72* 0.76*
349.7 0 145 2.41* 0.83 0.04* 0.08* 0.81 0.90* 0.85 0.92* 0.90* 0.92* 0.63* 0.73* 0.78*
Note: * acceptable level is met. After the first trial, modification index were provided following AMOS 7.0. “Commitment” is correlated to “hazard/incident reporting”, and “software-SOP/checklist” is correlated with “emergency evacuation regulation”. Thus, a second trial was conducted.
56
Y.-L. Hsu, T.-C. Liu / Journal of Air Transport Management 20 (2012) 54e56
Table 3 Standardized path coefficients and total effects among constructs. Construct
Path
Second-order factor
Cabin safety
First-order factors
Media Man Machine Management Mission
) ) ) ) )
cabin cabin cabin cabin cabin
Observable variables (sub-factors)
Climate Aircraft Passenger CRM Workload Selection Error Hardware requirement Hardware Design Software-SOP Software-safety briefing Org commitment Safety culture Incident reporting Safety training Safety standard Emergency evacuation Flight operation rules Company operation goal
) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )
Media Media Media Man Man Man Man Machine Machine Machine Machine Management Management Management Management Management Mission Mission Mission
4. Results After performing defuzzification of the data, factor analysis was used to measure the convergent validity of the constructs. Table 1 summarizes the results for item loadings, internal reliability and convergent validity of the constructs. All of the indicators show that each construct exhibits a high degree of reliability with an acceptable average variance extracted (AVE) greater than 0.5. SEM was performed by using a correlation matrix across 19 sub-factors, which include 63 measurement variables (Table 2) After two trials, fit indices reveal that the structural model provides a good fit. Table 3 shows the standardized path coefficients have positive effects of between 0.4 and 0.97, and all associations are highly significant. In summary, the second-order factor structural model confirms that cabin safety is measured by a systemic factor set e Media, Man, Machine, Management, and Mission. The causal relationship between 5-M factors and 19 observed variables (sub-factors) are also verified.
safety safety safety safety safety
Standardized loading
Total effect on “cabin safety”
0.82 0.89 0.95 0.91 0.97
0.82 0.89 0.95 0.91 0.97
0.66 0.42 0.90 0.85 0.70 0.75 0.67 0.79 0.89 0.73 0.79 0.73 0.80 0.81 0.92 0.77 0.73 0.76 0.67
0.54 0.34 0.74 0.76 0.62 0.67 0.60 0.75 0.85 0.69 0.75 0.66 0.73 0.74 0.84 0.70 0.71 0.74 0.65
features, will require careful thought if high safety standards are to be maintained. We find that organizational commitment is correlated with hazard/incident reporting. Management commitment is often deemed as the most important attribute in safety culture, with a good safety culture will foster hazard reporting. Therefore, management commitment is influential in safety performance (Hsu, 2008). Also, software- SOP/checklist is correlated with emergency evacuation regulation, which further confirms importance of regulation. Airlines have to comply with regulators’ safety rules to conduct SOPs/checklist, especially in the event of an emergency evacuation. The authorities conduct audits and inspections on a regular basis in order to ensure airlines’ compliance. Acknowledgments This research is supported by the National Science Council of Taiwan, ROC, under Grant no. NSC96-2221-E-424-002.
5. Conclusions References The modeling suggest that in terms of the effect of 5-M factors on cabin safety, mission has the greatest impact, followed by machine, management, man and media (Table 3). This differs from, for example the position of the US Federal Aviation Administration (2000), in which human factors is often seen as the main source of majority of risks. Mission and machine factors also involve the concept of regulatory compliance, reflecting that aviation is a highly regulated business. As for the effect of observable variables upon cabin safety, Hardware-design has the greatest effect, followed by cabin safety training which is in line with Muir and Thomas (2004), who pointed out that the challenge in developing future very large transport aircraft, such as the A380, is to ensure that with a passenger load of over 400, together with a cabin interior which will include innovative
Hsu, Y.L., 2008. From Reactive to Proactive: using safety survey to assess effectiveness of airline SMS. Journal of Aeronautics, Astronautics and Aviation 40, 41e48. International Air Transport Association, 2008a. Cabin Safety, Retrieved Jan 2, 2008, website: http://www.iata.org/cabin_safety.htm. International Air Transport Association, 2008b. IOSA Standards Manual, second ed. Geneva. International Civil Aviation Organization, 2006. Safety Management Manual, Doc 9859 AN/460. ICAO, Montreal. Maurino, D.E., Reason, R., Johnston, N., Lee, R.B., 1995. Beyond Aviation Human Factors. Aldershot Ashgate Publishing. Muir, H., Thomas, L., 2004. Passenger safety and very large transportation aircraft. Aircraft Engineering and Aerospace Technology 76, 479e486. Transport Canada, 2009. Cabin Safety Program. http://www.tc.gc.ca/en/menu.htm. US Federal Aviation Administration, 2000. FAA System Safety Handbook. FAA, Washington DC. Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning, part 2. Information Science 8, 301e357.
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Structuring risk factors related to airline cabin safety Yueh-Ling Hsu*, Te-Chang Liu Department of Air Transportation, Kainan University, No. 1, Kainan Road, Luzhu, Taoyuan County 33857, Taiwan
a b s t r a c t Keywords: Aircraft cabin safety Risk management Aircraft safety
This study explores cabin safety by mapping it as the second-order factor into a theoretical framework based on the 5-M model. A questionnaire, together with structural equation modeling is used to verify the proposed model. The results suggest that cabin safety can be measured by a systemic factor set e media, man, machine, management and mission. A causal relationship between 5-M factors and 19 variables is also found; mission factor together with, hardware-design and cabin safety training, have the greatest effects on cabin safety. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
attitude and behavior. In this regard the International Civil Aviation Organization (2006) pinpoints company/management commitment, a positive safety culture, SOPs, checklists and briefings, hazard and incident reporting, training for cabin safety, and cabin safety standards. Our model of risk factors for cabin safety is founded in the International Air Transport Association Operational Safety Audit checklist in IOSA Standards Manual (International Air Transport Association, 2008b) relating to cabin safety risks and risk factors categorized using the “5-M model” (media, man, machine, management, mission). This provides a framework for analyzing systems and determining relationships between the elements that work together to achieve a goal (US Federal Aviation Administration, 2000). Fig. 1 shows the second-order factor analysis model structure. “Cabin safety” is the second-order factor (x) while media “man”, “machine”, “management”, and “mission” are first-order factors (h); the first-order factors are indicators of the second-order factor. The second-order factor model comprises of five first-order factors, which are measured by 19 observable variables or sub-factors.
To raise public confidence, as well as, survivability airlines take steps to deal with the risk of accident and injury in air travel. However, most analyses focuses on improving flight safety, i.e. the prevention of accidental occurrence, or a single risk related to cabin safety such as disruptive passengers. Here we identify risks related to cabin safety by using the 5-M model, fuzzy theory and structural equation modeling (SEM).
2. The conceptual background The range of threats to the aircraft and its occupants include in-flight turbulence, smoke or fire in the cabin, decompression, emergency landings, emergency evacuations and unruly passengers, passenger medical issues, and human factors issues. The International Air Transport Association (2008a) states that cabin safety operation include proactive data collection and ensuing preventive activities regarding cabin design and operation, equipment, procedures, crew training, human performance, and passenger management; thus implying that it covers a wide range of areas including crashworthiness, operations, human factors, psychology, bio-dynamics, physiology, ergonomics, and pedagogy (Transport Canada, 2009). Moreover, human factors extend beyond the man-machine and individual human error areas and migrate towards organization and culture (Maurino et al., 1995). A number of recent cabin incidents in the airline industry have not only highlighted the need for airline operators to pay more attention to cabin safety but also to improve the cabin environment, which affects crew’s safety
* Corresponding author. Tel.: þ886 (0) 3 3412500; fax: þ886 (0) 3 3016912. E-mail address: [email protected] (Y.-L. Hsu). 0969-6997/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2011.12.009
3. Methodology To test the model, a survey was conducted with the first part designed to obtain information on the gender, age, job position, number of years with the company, years employed in aviation, and education level of respondents. Because it is difficult to reasonably convey in questionnaires situations that are overly complex or difficult to define, the notion of linguistic variable is used (Zadeh, 1975). Respondents were asked to specify the range from one to 100 corresponding to linguistic terms ranging from “very high impact” to “very low impact”. The evaluation criterion evaluated by respondents was therefore measured as fuzzy number with triangular membership function.
Y.-L. Hsu, T.-C. Liu / Journal of Air Transport Management 20 (2012) 54e56
55
Fig. 1. The second-order factor analysis model structure.
The survey was then distributed to all cabin crew and personnel in the safety department of a domestic Taiwanese airline between January and March 2008. Two hundred questionnaires were distributed and 184 usable samples were obtained after deleting incomplete responses. Seventy-nine percent of respondents were
female; 46% were aged between 30 and 39 and 42% were between 20 and 29 with 39% being at their company for six to ten years, and 35% for under five years. Fifty-six percent of respondents had at least a bachelor’s degree.
Table 1 Modeling results.
Table 2 Model fit.
Construct Media
Man
Machine
Management
Mission
Item MD1 MD2 MD3 MN1 MN2 MN3 MN4 MC1 MC2 MC3 MC4 MG1 MG2 MG3 MG4 MG5 MS1 MS2 MS3
Item loading
Factor reliability
Factor loading
Average variance extracted
0.67 0.42 0.91 0.85 0.7 0.75 0.67 0.79 0.89 0.73 0.79 0.73 0.8 0.81 0.91 0.77 0.73 0.76 0.67
0.85
0.82
0.5
0.89
0.9
0.6
0.93
0.95
0.6
0.95
0.91
0.7
0.85
0.98
0.5
Fit index
c2 P-level df c2/df GFI SRMR RMSEA AGFI NFI RFI IFI TLI (NNFI) CFI PGFI PNFI PCFI
Acceptable level
First trial
Second trial
>0.05 .. <3 >0.9 <0.05 <0.08 >0.90 >0.90 >0.90 >0.90 >0.90 >0.90 >0.50 >0.50 >0.50
400.95 0 147 2.728* 0.81 0.053 0.097 0.75 0.84 0.81 0.89 0.88 0.89 0.62* 0.72* 0.76*
349.7 0 145 2.41* 0.83 0.04* 0.08* 0.81 0.90* 0.85 0.92* 0.90* 0.92* 0.63* 0.73* 0.78*
Note: * acceptable level is met. After the first trial, modification index were provided following AMOS 7.0. “Commitment” is correlated to “hazard/incident reporting”, and “software-SOP/checklist” is correlated with “emergency evacuation regulation”. Thus, a second trial was conducted.
56
Y.-L. Hsu, T.-C. Liu / Journal of Air Transport Management 20 (2012) 54e56
Table 3 Standardized path coefficients and total effects among constructs. Construct
Path
Second-order factor
Cabin safety
First-order factors
Media Man Machine Management Mission
) ) ) ) )
cabin cabin cabin cabin cabin
Observable variables (sub-factors)
Climate Aircraft Passenger CRM Workload Selection Error Hardware requirement Hardware Design Software-SOP Software-safety briefing Org commitment Safety culture Incident reporting Safety training Safety standard Emergency evacuation Flight operation rules Company operation goal
) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )
Media Media Media Man Man Man Man Machine Machine Machine Machine Management Management Management Management Management Mission Mission Mission
4. Results After performing defuzzification of the data, factor analysis was used to measure the convergent validity of the constructs. Table 1 summarizes the results for item loadings, internal reliability and convergent validity of the constructs. All of the indicators show that each construct exhibits a high degree of reliability with an acceptable average variance extracted (AVE) greater than 0.5. SEM was performed by using a correlation matrix across 19 sub-factors, which include 63 measurement variables (Table 2) After two trials, fit indices reveal that the structural model provides a good fit. Table 3 shows the standardized path coefficients have positive effects of between 0.4 and 0.97, and all associations are highly significant. In summary, the second-order factor structural model confirms that cabin safety is measured by a systemic factor set e Media, Man, Machine, Management, and Mission. The causal relationship between 5-M factors and 19 observed variables (sub-factors) are also verified.
safety safety safety safety safety
Standardized loading
Total effect on “cabin safety”
0.82 0.89 0.95 0.91 0.97
0.82 0.89 0.95 0.91 0.97
0.66 0.42 0.90 0.85 0.70 0.75 0.67 0.79 0.89 0.73 0.79 0.73 0.80 0.81 0.92 0.77 0.73 0.76 0.67
0.54 0.34 0.74 0.76 0.62 0.67 0.60 0.75 0.85 0.69 0.75 0.66 0.73 0.74 0.84 0.70 0.71 0.74 0.65
features, will require careful thought if high safety standards are to be maintained. We find that organizational commitment is correlated with hazard/incident reporting. Management commitment is often deemed as the most important attribute in safety culture, with a good safety culture will foster hazard reporting. Therefore, management commitment is influential in safety performance (Hsu, 2008). Also, software- SOP/checklist is correlated with emergency evacuation regulation, which further confirms importance of regulation. Airlines have to comply with regulators’ safety rules to conduct SOPs/checklist, especially in the event of an emergency evacuation. The authorities conduct audits and inspections on a regular basis in order to ensure airlines’ compliance. Acknowledgments This research is supported by the National Science Council of Taiwan, ROC, under Grant no. NSC96-2221-E-424-002.
5. Conclusions References The modeling suggest that in terms of the effect of 5-M factors on cabin safety, mission has the greatest impact, followed by machine, management, man and media (Table 3). This differs from, for example the position of the US Federal Aviation Administration (2000), in which human factors is often seen as the main source of majority of risks. Mission and machine factors also involve the concept of regulatory compliance, reflecting that aviation is a highly regulated business. As for the effect of observable variables upon cabin safety, Hardware-design has the greatest effect, followed by cabin safety training which is in line with Muir and Thomas (2004), who pointed out that the challenge in developing future very large transport aircraft, such as the A380, is to ensure that with a passenger load of over 400, together with a cabin interior which will include innovative
Hsu, Y.L., 2008. From Reactive to Proactive: using safety survey to assess effectiveness of airline SMS. Journal of Aeronautics, Astronautics and Aviation 40, 41e48. International Air Transport Association, 2008a. Cabin Safety, Retrieved Jan 2, 2008, website: http://www.iata.org/cabin_safety.htm. International Air Transport Association, 2008b. IOSA Standards Manual, second ed. Geneva. International Civil Aviation Organization, 2006. Safety Management Manual, Doc 9859 AN/460. ICAO, Montreal. Maurino, D.E., Reason, R., Johnston, N., Lee, R.B., 1995. Beyond Aviation Human Factors. Aldershot Ashgate Publishing. Muir, H., Thomas, L., 2004. Passenger safety and very large transportation aircraft. Aircraft Engineering and Aerospace Technology 76, 479e486. Transport Canada, 2009. Cabin Safety Program. http://www.tc.gc.ca/en/menu.htm. US Federal Aviation Administration, 2000. FAA System Safety Handbook. FAA, Washington DC. Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning, part 2. Information Science 8, 301e357.