Realizing Trust Dynamics and Governance for Humanizing Driverless Technology
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Driverless Technology (DT) in the shape of Autonomous Vehicles (AVs) will emerge as a powerful catalyst transforming the future cascade of mobility, infrastructure, and social wellbeing. Due to the proliferation of AVs, NZ is likely to seek a panacea against challenges of ageing, congestion and road trauma. Public trust is quintessential in harnessing these technologies. Trust is a key mediator in users’ acceptance and promoting human-machine interaction (HMI). Present day research lacked examining this phenomenon and is mainly focused on optimistic technological orientations of AVs. User acceptance theories and models were originated to address the requirements of Information Technology and most of these were neither qualitatively nor quantitatively tested in real driving situations for autonomous and semi-autonomous vehicles. Trust in HMI is mainly considered as a by-product of interpersonal human trust without considering the institutional perspectives. This warrants investigations into the user acceptance needs to unfold trust dynamics, the applicability of existing theories, and linkages of interpersonal and institutional trust for driverless systems. The study examined the trust impacting technology and governance factors in real-world autonomous driving environments. The study explored how dynamic trust evolves through experience and interaction with a system and technology from initially learned trust. In a mixed methods Multiphase Design research settings comprising four studies, the study developed an AD Trust Acceptance Model, HMI AD Event Relationship Identification Framework (HMI-ADERIF), and an integrated trust and governance model for adoption in NZ. The recommended AD trust Model has provided a way for the researchers to fundamentally re-assess and augment the various trust models and theories present in the literature today. HMI-ADERIF provides a guide for manufacturers to make it easier to understand the chain of events and how the relationship with trust affecting factors occurs during a single or continuous AD scenario. The study has also developed a unique system dynamics (SD) model to accrue perceived societal and technological benefits. The research provides profound insights into the likely AVs diffusion timelines and a roadmap for the next 100 years till 2121. Study – 1 is based on a qualitative AV user study in live traffic conditions with BMW X5 xDrive40i SUV. Study – 1 is embedded in the main quantitative Study – 2 (SEM) in a concurrent embedded correlational design setting in Phase - 1. Study – 1 observed the users’ interpersonal and institutional trust towards finalizing key autonomous driving (AD) corroborating factors. Study – 2 deployed an exploratory survey and tested seven hypotheses using Structural Equation Modelling (SEM) and Confirmatory Factor Analysis (CFA) with IBM SPSS and AMOS version 26. Study – 2 validated the integrated trust and governance model, and confirmed the role of ‘trust’ as a mediator between interpersonal and institutional trust. Study – 3 employed the convergent parallel design with Study - 4 concurrently in Phase- 2, and developed a quantitative SD model for NZ using System Dynamics (SD) modelling technique. Study – 3 provided policy insights for articulating transport investment decisions for shaping up driverless eco-system. Study – 4 used 13 experts’ interviews for useful interpretations, realization of research outcomes and validation of trust and governance framework.