1
A Preliminary Study on Technology Acceptance in Airline Safety
Risk Management: Extending the Technology Acceptance Model
Washington Mhangami
1
, Stephen King
2
, and David Barry
3
1,2,3
3
Cranfield University, Bedfordshire, MK43 OAL, United Kingdom
washington.mhangami@cranfield.ac.uk
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 applies the TAM model to aviation
safety risk assessment methods. A review of the literature on
TAM and its adaptations in aviation risk assessment is carried
out. Drawing from interdisciplinary insights in psychology,
human factors, and aviation safety, this paper proposes
constructs that may enhance the TAM framework to improve
its applicability to the aviation industry. This study explores
key areas including individual versus organisational
acceptance of technology, procurement and operational costs,
trust in technology, system complexity and security issues.
These factors are examined to provide a comprehensive
understanding of technology acceptance within aviation risk
assessment practices. By proposing an expansion of the TAM
framework, this paper aims to offer valuable insights for
researchers, practitioners, and regulators involved in aviation
safety management and technology integration efforts.
1. INTRODUCTION
Safety risk management (SRM) is fundamental within the
framework of a robust safety management system, crucial in
preventing accidents and incidents caused by hazards or
safety deficiencies. Core activities include operational
system delineation, hazard analysis, safety risk evaluation,
and implementing preliminary mitigation
measures.According to Aven and Ylonen (2018),
contemporary literature increasingly adopts a socio-technical
perspective.
According to Aven and Ylonen (2018), contemporary
literature increasingly adopts a socio-technical perspective.
This viewpoint emphasises the intricate safety dynamics
within complex systems, often overlooked by conventional
risk assessment approaches and it also highlights the
importance of technology consideration.
In the aviation industry, a persistent challenge lies in
accurately determining the probability of safety occurrences
and representing risks through tools like risk matrices.
Existing methodologies, including bowties, face criticism for
their static nature, as highlighted by Cox (2008). Malakis,
Kontogiannis and Smoker (2023) argue that the dynamic
nature of risk, coupled with ‘information-based uncertainty,'
undermines safety assessments. Current risk assessment
methods often overlook the fundamental aspects of
uncertainty and variability (Vose,2008). Quantitative risk
assessment methods, advocated by Apostolakis (2004) and
Saluda and Idris (2012), offer a clearer depiction of risks but
are underutilised in aviation.
While quantitative risk assessment methods hold significant
potential benefits, they encounter barriers to widespread
adoption, notably data scarcity and limited technological
comprehension. Fenton and Neil (2019) strongly refute the
notion that data scarcity is a justifiable impediment. In their
seminal work, "Risk Assessment and Decision Analysis with
Bayesian Networks (BNs)," they assert that data limitations
should not serve as a pretext for exclusively employing
qualitative analysis. They argue that BNs exemplify the
capability to adeptly address such challenges. Hubbard
(2020) emphasises the importance of probabilistic models in
risk analysis, highlighting a resistance to leveraging data
within safety risk management circles.
In the realm of aviation safety risk assessment, a plethora of
quantitative methods, including Fault Tree Analysis, BNs,
Monte Carlo Simulation, Failure Modes and Effect Analysis
(FMEA), and Event Tree Analysis are available. Among
these, BNs stand out as a promising solution to data scarcity,
