Accepting Technology in Aviation Safety Risk Management: an extension of the technology acceptance model

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Published Nov 11, 2024
Washington Mhangami Stephen King David Barry

Abstract

Aviation safety is paramount, and advancements in technology play a pivotal role in mitigating risks and enhancing operational efficiency. The Technology Acceptance Model (TAM) has been widely utilised to understand the adoption of various technologies across industries. However, its application within the context of aviation risk assessment requires nuanced considerations due to the unique operational environment and stringent safety requirements. This paper critically reviews existing literature on TAM and its adaptations in aviation risk assessment, identifying limitations and gaps. Drawing from interdisciplinary insights in psychology, human factors, and aviation safety, this paper proposes an enhanced framework for TAM tailored specifically to the aviation industry. The proposed model integrates key constructs such as perceived usefulness of technology in this area, trust in technology, system complexity, and organizational factors to provide a comprehensive understanding of technology acceptance within aviation risk assessment practices. By enhancing the TAM framework, this paper aims to offer valuable insights for researchers, practitioners, and regulators involved in aviation safety management and technology integration efforts.

How to Cite

Mhangami, W., King, S., & Barry, D. (2024). Accepting Technology in Aviation Safety Risk Management: an extension of the technology acceptance model. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.3922
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References
Ajzen, I., (1985). From intentions to actions: A theory of planned behaviour. In: J. Kuhl and J. Beckmann, eds. Action control: From cognition to behaviour. Berlin: Springer, pp.11-39.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice-Hall.
Apostolakis, G.E., 2004. How useful is quantitative risk assessment? Risk Analysis, 24(3), pp.515-520. Available at: https://doi.org/10.1111/j.0272-4332.2004.00455.x [Accessed 25 May 2024].

Aven, T. and Ylönen, M., 2018. A risk interpretation of sociotechnical safety perspectives. Reliability Engineering & System Safety, 175, pp.13-18. Available at: https://doi.org/10.1016/j.ress.2018.03.004.

Barki, H. and Hartwick, J. (1994) 'Measuring User Participation, User Involvement, and User Attitude', MIS Quarterly, 18(1), p. 59. doi: https://doi.org/10.2307/249610.

Bauranov, A. and Rakas, J. (2024) 'Bayesian network model of aviation safety: Impact of new communication technologies on mid-air collisions', Reliability Engineering & System Safety, 243, p. 109905. doi: https://doi.org/10.1016/j.ress.2023.109905.
Chang, C.-T., Su, C.-R., & Hajiyev, J. (2017). Examining the students' behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for E-learning approach. Computers & Education, 111, 128–143.

Chen, K. and Chan, A.H.S., 2011. The ageing population of China and a review of gerontechnology. Gerontechnology, 10(2), pp.63-71.

Clothier, R.A., Greer, D.A., Greer, D.G. and Mehta, A.M., 2015. Risk perception and the public acceptance of drones. Risk Analysis, 35(6), pp.1167-1183

Cohen, N. and Arieli, T., 2011. Field research in conflict environments: Methodological challenges and snowball sampling. Journal of peace research, 48(4), pp.423-435.

Woodley, X. & Lockard, M. (2016). Womanism and snowball sampling: engaging marginalized populations in holistic research, 21, 2, pp 321-329

Davies, M.B., 2007. Doing a successful research project. Basingstoke: Palgrave Macmillan.
Dobbie, M.F. and Brown, R.R., 2014. A framework for understanding risk perception, explored from the perspective of the water practitioner. Risk Analysis, 34(2), pp.294-308. doi:10.1111/risa.12100.

Fenton, N., & Neil, M. (2018). Risk Assessment and Decision Analysis with Bayesian Networks (2nd ed.). CRC Press.
Fussell, S. and Truong, D. (2020) 'Preliminary Results of a Study Investigating Aviation Student’s Intentions to Use Virtual Reality for Flight Training', International Journal of Aviation, Aeronautics, and Aerospace. doi: https://doi.org/10.15394/ijaaa.2020.1504.

Goher K. M., Mansouri N., Fadlallah SO (2017). Assessment of personal Care and medical robots from older adults’ perspective. Robotics and Biomimetics, 4(1), 5–7. https://doi.org/10.1186/s40638-017-0061-7

Granić, A. and Marangunić, N. (2019) 'Technology Acceptance Model in Educational Context: A Systematic Literature Review', British Journal of Educational Technology, 50(5), pp. 2572–2593. doi: https://doi.org/10.1111/bjet.12864.

Guest, W., Wild, F., Vovk, A., Lefrere, P., Klemke, R., Fominykh, M., & Kuula, T. (2018). A technology acceptance model for augmented reality and wearable technologies. Journal of Universal Computer Science, 24(2), 192–219.

Hair, J.F., Jr., Gabriel, M.L.D.S., da Silva, D. and Braga Junior, S. (2019) 'Development and validation of attitudes measurement scales: Fundamental and practical aspects', RAUSP Management Journal, 54, pp. 490–507.

He, J. and King, W. R. (2008) 'The Role of User Participation in Information Systems Development: Implications from a Meta-Analysis', Journal of Management Information Systems, 25(1), pp. 301–331. doi: https://doi.org/10.2753/MIS0742-1222250111.

Help University, Malaysia, and Lai, P. C. (2017) 'The Literature Review of Technology Adoption Models and Theories for the Novelty Technology', Journal of Information Systems and Technology Management, 14(1), pp. 21–38. doi: https://doi.org/10.4301/S1807-17752017000100002.

Froman, R. D. (2001). Elements to consider in planning the use of factor analysis. Southern Online Journal of Nursing Research, 2. Available online at www.snrs.org

Hubbard, D. W. (2020). The Failure of Risk Management: Why It’s Broken and How to Fix It (2nd ed.). Wiley.
Huysamen, G. K. (2006a). Coefficient alpha: Unnecessarily ambiguous; unduly ubiquitous. South African Journal of Industrial Psychology, 32, 34–40.

Kabir, S. and Papadopoulos, Y. (2019) 'Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review', Safety Science, 115, pp. 154-175. doi: https://doi.org/10.1016/j.ssci.2019.02.009.

Khakzad, N., Khan, F., & Amyotte, P. (2013). Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Safety and Environmental Protection, 91(1-2), 46-53. https://doi.org/10.1016/j.psep.2012.01.005

Kieras, D. and Polson, P. G. (1985) 'An Approach to the Formal Analysis of User Complexity', International Journal of Man-Machine Studies, 22(4), pp. 365–394. doi: https://doi.org/10.1016/S0020-7373(85)80045-6.
Kumar, R.V., 2024. Cronbach’s Alpha: Genesis, Issues and Alternatives. Management, 1, p.17.

Li, X., Lai, P.L., Yang, C.C. and Yuen, K.F., 2021. Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97, p.102139.

Liu, A. C., & Chou, T. Y. (2020). An integrated technology acceptance model to approach the behavioral intention of smart home appliance. International Journal of Organizational Innovation, 13(2), 95–118.

Malakis, S., Kontogiannis, T. and Smoker, A. (2023), “A pragmatic approach to the limitations of safety management systems in aviation”, Safety Science, Vol. 166, 106215, doi: 10.1016/j.ssci.2023.106215.

Molnar, A., 2019. SMARTRIQS: A simple method allowing real-time respondent interaction in Qualtrics surveys. Journal of Behavioral and Experimental Finance, 22, pp.161-169.

Munoz-Leiva, F., Climent-Climent, S. and Liébana-Cabanillas, F. (2017), “Determinants of intention to use the mobile banking apps: an extension of the classic TAM model”, Spanish Journal of Marketing-ESIC, Vol. 21 No. 1, pp. 25-38, doi: 10.1016/j.sjme.2016.12.001.
Myers, P. (2016). SMS Derived vs. Public Perceived Risk in Aviation Technology Acceptance (Literature Review). International Journal of Aviation, Aeronautics, and Aerospace. https://doi.org/10.15394/ijaaa.2016.1141
Naderifar, M., Goli, H. and Ghaljaie, F., 2017. Snowball sampling: A purposeful method of sampling in qualitative research. Strides in development of medical education, 14(3).
Parker, C., Scott, S. and Geddes, A., 2019. Snowball sampling. SAGE research methods foundations.
Parsons, F.E., Mota, S.J. and Quan, Y., 2015. Managing Qualtrics Survey Distributions and Response Data with SAS®.
Polit, D.F. and Beck, C.T., 2004. Nursing research: Principles and methods. Lippincott Williams & Wilkins.
Sijtsma, K., 2009. On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74, pp.107-120.
Syarifudin, G., Abbas, B.S. and Heriyati, P., 2018, August. TAM approach on E-commerce of aircraft ticket sales on consumer purchase intention. In 2018 6th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-6). IEEE.
Talley, S.M., 2020. Public acceptance of AI Technology in self-flying aircraft. Journal of Aviation/Aerospace Education & Research, 29(1), pp.49-64.
Tavakol, M. and Dennick, R., 2011. Making sense of Cronbach's alpha. International journal of medical education, 2, p.53.
Tinsley, H. E., & Tinsley, D. J. (1987). Uses of factor analysis in counseling psychology research. Journal of Counseling Psychology, 34, 414–424.
Vaske, J.J., Beaman, J. and Sponarski, C.C., 2017. Rethinking internal consistency in Cronbach's alpha. Leisure sciences, 39(2), pp.163-173.
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D., 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), pp.425-478.
Venkatesh, V., and Davis, F.D., 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), pp.186-204.
W.-H. Pan, Y.-W. Feng, C. Lu and X.-F. Xue, "Adoption of a Bayesian network for the operational reliability analysis of aircraft systems", IOP Conference Series: Materials Science and Engineering, pp. 022055, 2021.
Wang, X., Ong, S.K., & Nee, A.Y.C. (2016). A comprehensive survey of augmented reality assembly research. Advances in Manufacturing, 4, 1-22. doi: 10.1007/s40436-015-0131-4.
Wang, Y., Anne, A. and Ropp, T., 2016. Applying the technology acceptance model to understand aviation students’ perceptions toward augmented reality maintenance training instruction. International Journal of Aviation, Aeronautics, and Aerospace, 3(4), p.3.
Wu, M.-Y., Chou, H.-P., Weng, Y.-C., & Huang, Y.-H. (2008) 'A Study of Web 2.0 Website Usage Behavior Using TAM 2', in 2008 IEEE Asia-Pacific Services Computing Conference, Yilan, Taiwan, pp. 1477-1482. doi: 10.1109/APSCC.2008.92
Zurheide, F., Hermann, F. and Lamesberger, T. (2021) pyBNBowTie: Python library for Bow-Tie Analysis based on Bayesian Networks. GitHub. Available at: https://github.com/zurheide/pybnbowtie [Accessed 22 May 2024].

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