Resource Integration in the Electric Vehicle Ecosystem from A User Perspective in Australia.

Abstract

Electric vehicles (EVs) are considered a promising global greenhouse gas reduction option and a necessary component of a low-carbon mobility future. Due to the rapid rise of digitalisation and big data, the Internet of Things, electric batteries, advanced communication and sensing capabilities and innovative vehicle development, major changes in transportation behaviour will occur in the near future. EVs with a unique range of services and connectivity technologies offer opportunities for better utilisation of resources; sustainable, safe, clean and efficient mobility; and thus a higher quality of life, especially in cities and urban areas. EV adoption has the possibility of being increased to up 24% of new vehicles sold globally in 2030, compared to 3% in 2020 (EPM Advosory Council, 2021). However, realisation of EVs with these range of services and connectivity technologies requires unlimited personal information collection and exchange with or without users permission, such as user identity, vehicle identification number and driving behaviour, thereby compromising users’ privacy. EV users commonly deactivate these services or may abandon EVs due to privacy concerns and service provider distrust. Traditionally, EVs have been perceived as a product rather than a mobility service. There is recognition of the need to investigate EVs and their ecosystems from a service-dominant (S-D) logic perspective to explore resources such as private information and trust and understand resource integration as an important aspect of S-D logic. This thesis presents a detailed and comprehensive investigation of resource integration, understood through S-D logic, in an EV ecosystem from a user perspective Resource integration is critical for vehicle ecosystems (Schulz, Böhm, Gewald, & Krcmar, 2019). Few prior studies have investigated resources such as private data (Mikusz & Herter, 2016; Schulz et al., 2019) and value propositions such as smart navigation technology (Mikusz & Herter, 2016) from a S-D logic meta-theoretical perspective. There is little research on the privacy calculus (Cichy, Salge, & Kohli, 2014; Ju & Mou, 2018) as a micro-theoretical issue or on the antecedents–privacy concerns–outcomes (APCO) model (Buck & Reith, 2020) as a mid-range theoretical perspective. This is confirmed by an industry study conducted by Deloitte, which found that 47% of respondents rejected using telematics services due to privacy concerns and 27% would only agree to use the services if the prices were accurate and a high discount was given (Friedman & Canaan, 2014). Existing research is inadequate for EV industries, which integrate essential resources and social coordination. Thus, EV industries face significant uncertainty in terms of service provision and privacy protection, and insufficiently understand and support value co-creation. Further, the low adoption of EVs across countries burden them. Australia lags behind other developed countries in EV adoption; compare, for example, Australia’s EV uptake of 0.78% versus that of Norway’s 74.8% in 2021 (Electric Vehicle Council, 2021). Therefore, the necessary resources and social coordination need to be identified and the required resource integration needs to be quantified. S-D logic as a meta theory has become an appealing concept for improving insight into the roles played by resources, such as personal data and trust, and institutions and institutional arrangements, such as concerns, perception and bias, in influencing different individual behaviours in value co-creation. The enhanced APCO model (as a mid-range theory) has discussed the effects of antecedents/resources and various levels of cognitive responses/institutions and institutional arrangements on the risk–benefit analysis that users conduct in their choice of a specific behavioural reaction. This aids value co-creation, for example, by identifying the circumstances under which customers will release private information or/and use EVs. This study integrates S-D logic and an enhanced APCO model. A single case study and an embedded exploratory sequential mixed methods approach was used to answer three research questions: 1) what resources and values need to be recognised in an EV ecosystem?, 2) what factors influence resource integration in an EV ecosystem?, and 3) how have these resources been integrated to co-create value in an EV ecosystem? Exploratory sequential mixed methods were adopted, and this involved collecting qualitative data through semi-structured questions to answer the research questions, with the answers subsequently validated using the results from a developed survey instrument. This study developed a conceptual model based on the enhanced APCO model that yielded five themes: 1) antecedents, which studies the operant resources; 2) high- level cognitive responses, which represents institutions and institutional arrangements; 3) low-level cognitive responses, which represents institutions; 4) privacy concerns or the privacy calculus conducted by users, which shows how resources are integrated in the EV ecosystem; and 5) outcomes, or users’ behavioural reactions to continuing to disclose their private data while using EVs, with this perceived as value co-creation. This study provides comprehensive understanding into resource integration in an EV ecosystem, including on resources, institutions and institutional arrangements, value co-creation and how these resources are integrated and coordinated. The enhanced APCO model facilitates understanding of the interaction of cognitive responses as parts of institutions and institutional arrangements. Most importantly, the theory of privacy calculus gives insight into how users conduct risk–benefit analysis, and our analysis reveals how values are co-created in terms of behavioural reactions. These insights facilitate understanding of users’ interactions in a complex and dynamic EV ecosystem. EV technologies have been rapidly developed but slowly adopted, and this thesis helps to address the current dearth of research in this area and provides real-world value to EV manufacturers and service providers in the form of theoretically informed insights into users’ behaviour while adopting EVs and using related services and technologies.