Understanding Electric Vehicle Acceptance in Indonesia using Structural Equation Modeling: A Conceptual Model

∗To reduce Green House Gas (GHG) emission as a commitment of Indonesia in the Paris Agreement 2015, Indonesia government is enhancing using of green and sustainable energy replacing fossil energy resources. One of government's actual commitments is written in Government Regulatory no. 55/2019 regarding the acceleration programs of battery-based Electric Vehicles (EVs) which is believed to emit low emissions to the atmosphere. This study promotes an extended version of the Theory of Planned Behavior to analyze the impacts of personal beliefs on individual adoption intention towards EVs and structural equation modeling (SEM) method to quantify its acceptance level in Indonesia. The limitations of this research are a) study analyzes Indonesian acceptance level towards Plug-in Hybrid (PHEV) and Full Battery (BEV) based Electric Car; b) study was conducted in the situation of the pandemic of Covid-19 that might influence psychology of respondents. A questionnaire survey was conducted online from December 2020 to February 2021 to Indonesian people. A total of 154 valid survey responses were obtained. Based on the empirical results, we concluded that environmental concern (ENC) has a significant positive effect on adoption intention (AdIn) on EVs purchasing only with perceived behavior control (PBC).as mediator. In Indonesia, PBC factor influences AdIn factor which is relatively high (path coefficient=0.739). This result is indicating penetration of EVs in Indonesia can be done by prioritizing PBC factors, such as performance attributes/ ease of use (battery lifespan, charging time, maintenance, charging infrastructure availability, etc.) and usefulness (free from odd-even rule, privilege right of using bus lane, and other non-monetary incentives). Other variables such as attitude (ATT), subjective norm (SUN), and personal moral norm (PMN) have no significant effect on EVs adoption intention (AdIn). Since there are limitations of this study, so we suggest that future study can be done in normal condition (recovered from pandemic condition) and extends study to electric two-wheelers market. Additionally, future study should consider increasing portion of questionnaire respondents who have driving or taking on EVs so that study can get more accurate data analysis. Study is expected to help government to engineer intervention policies and guide automotive industries to invest in more accurate market planning. Study is also expected to support other studies in simulation (e.g., forecasting) in future.