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Exploring Public Acceptance of Connected and Autonomous Vehicles with a Focus on Cyber Security and Privacy Risks

Liu, Na (2021) Exploring Public Acceptance of Connected and Autonomous Vehicles with a Focus on Cyber Security and Privacy Risks. Doctoral thesis, University of Huddersfield.

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Abstract

Connected and Autonomous Vehicles (CAVs) constitute an automotive development carrying paradigm-shifting potential that may soon be embedded into a dynamically changing urban mobility landscape. The complex machine-led dynamics of CAVs make them more prone to data exploitation and vulnerable to cyber-attacks than any of their predecessors. This increases the risks of privacy breaches and cyber security violations for their users. Cyber security and privacy issues are of significant concern for automated mobility since they can adversely affect the public acceptance of CAVs, give them a bad reputation at this embryonic stage of their development, create barriers to their adoption and increased use, which ultimately complicates the business models of their future operations and ultimately their diffusion. Therefore, it is vital to identify and create an in-depth understanding of the cyber security and privacy issues associated with CAVs as it is something that will support a more systematic identification and contextualisation of the factors determining public acceptance of CAVs.

This empirical research aims to do exactly that by employing a sequential mixed method approach, with a qualitative phase looking in depth cyber security and privacy issues followed by a survey-based phase looking to model the factors underpinning CAV acceptance. For the qualitative research phase, 36 semi-structured elite interviews were organised with CAV experts that already anticipate problems and look for their solutions. Thematic analysis was used to identify and contextualise the factors that reflect and affect CAV acceptance in relation to the privacy and cyber security agendas. Six core themes emerged: awareness, user and vendor education, safety, responsibility, legislation, and trust. Each of these themes has diverse and distinctive dimensions and are discussed herein as sub-themes.

For the quantitative research phase, a theory-based extended technology acceptance model (TAM) model was developed and validated through an online survey of 1162 residents from the UK and China. The confirmative factor analysis-structural equation modelling (CFA-SEM) approach was used to analyse the collected data. Results suggested that perceived usefulness and perceived ease of use remain the most robust predictors that determine the using intention of CAVs. The exogenous variables, namely self-efficacy, facilitating conditions and perceived risks, were significant predictors of the intention to use CAVs. The perceived system characteristics such as the relative advantages of CAVs, the cyber security and privacy risks, and the perceived organisational factors like government, manufacturers, and service providers’ facilitation in personal data protection are proved to be crucial in the users’ attitude forming process.

Based on the overall findings, policy recommendations were provided to make CAVs more cyber secure and privacy friendly. These include prioritising cyber security and privacy issues in CAVs, utilising social media tools in promoting positive social influences and developing a novel human-machine interface that would enable easy and safe operations. The study also suggests that mitigating the cyber security and 5 privacy risks embedded in CAVs require inter-institutional cooperation, awareness campaigns and trials for trust-building purposes, mandatory educational training for manufacturers and perhaps more importantly for end-users, balanced and fair responsibility-sharing, two-way dynamic communication channels and a clear consensus on what constitutes threats and solutions. Additionally, recommendations for CAV market-entry and market penetration routes were given based on the multigroup analysis results.

Item Type: Thesis (Doctoral)
Subjects: T Technology > T Technology (General)
Schools: Huddersfield Business School
Depositing User: Annabel Danson-Darbyshire
Date Deposited: 20 Dec 2021 16:07
Last Modified: 06 Apr 2022 01:38
URI: http://eprints.hud.ac.uk/id/eprint/35637

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