Segmentation of The Italian E-Mobility Market. An Application of The Theory of Planned Behaviour
File(s)
Date Issued
September 11, 2024
Abstract
The pressing need to reduce worldwide greenhouse gas (GHG) emissions is pushing governments to plan the phase-out of internal combustion engine vehicles (ICEVs) in the coming years, encouraging consumers to switch to zero emission vehicles. In 2023, the EU parliament voted to ban on the sale of CO2 emitting vehicles by 2035. Electric vehicles (EVs), which have zero tailpipe emissions, are deemed to contribute to the GHG emission reduction in the transport sector that accounted for 24% of the EU’s total CO2 emissions (Faria et al., 2019; Europe Commission, 2023; European Parliament, 2023). Despite the support from governments, the EV uptake is still low in some EU countries such as Italy (ACEA, 2023). In this context, the analysis of the e-mobility consumer market is fundamental. The literature has largely investigated EV purchase intention highlighting that demographics, beliefs, knowledge, moral and social norms, perceptions, and behavioural factors underlie the adoption (Ivanova and Moreira, 2023). Psychographics, socio-demographics, and behavioural factors were also used as bases for the market segmentation. However, there are few studies concerning the e-mobility market segmentation despite that profiling consumers would help car manufacturers and policy makers to implement more suitable and differentiated measures to endorse the EV uptake. The classification of EV consumers is still underexplored (Mohamed et al., 2016; Morton et al., 2017; Jaiswal et al., 2022). Given the scarcity of segmentation works and the low EV uptake in Italy, this work aims to segment the Italian e-mobility market. The segmentation will allow for the identification of groups of consumers that can be targeted by tailored marketing and governmental actions. The acknowledged complexity of the EV consumption suggests adopting the Theory of Planned Behaviour by Ajzen (1985), which is the most widely used model in the literature and whose predictiveness is extensively proven (Mohamed et al., 2016; Yegin and Kram, 2022), to identify the psychographics used as segmentation bases.