Influential Factors Affecting the Adoption Intention of Electric Vehicles in Indonesia: An Extension of the Theory of Planned Behavior

Authors

  • Daffa Refor Multi Ray Bina Nusantara University
  • Christian Harito Bina Nusantara University

DOI:

https://doi.org/10.21512/emacsjournal.v5i3.10525

Keywords:

Electric Vehicle (EVs), Greenhouse gas emission (GHG), EV Adoption, Sustainability

Abstract

The primary goal of the thesis was to examine the factors that affect the willingness of people in Indonesia to adopt Electric Vehicles (EVs). Given the pressing need in Indonesia to address energy shortages and reduce greenhouse gas emissions, this research aimed to investigate the elements that influence people's inclination to use EVs. In this study, questionnaires were used as a means of measurement. Respondents were provided with a brief explanation before completing the survey. Using an extended TPB (Theory of Planned Behavior) model, the research analyzed the adoption intentions of 310 respondents from Indonesia, following a minimum sample guideline of 200. The collected data was analyzed using smartPLS4 to extract insights. The empirical analysis of the research focused on five key factors: attitude, subjective norms, perceived behavioral control, environmental concern, and moral norms. Notably, the empirical results showed that while attitude had an insignificant impact on the adoption intention of EVs in Indonesia, the other factors subjective norms, perceived behavioral control, environmental concern, and moral norms had a significant and positive influence on the intention to embrace electric vehicles in the country. Based on these findings, it can be concluded that the extended TPB model is suitable for predicting the adoption intention of electric vehicles. Considering these results, the study explores the implications for EV adoption in Indonesia, offering valuable insights and recommendations for future research and for the Indonesian government's decision-making process regarding the factors that influence EV adoption.

Dimensions

Plum Analytics

Author Biographies

Daffa Refor Multi Ray, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering

Christian Harito, Bina Nusantara University

Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering

References

Ajanovic, A., & Haas, R. (2018). Electric vehicles: solution or new problem?. Environment, Development and Sustainability, 20, 7-22.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

AQI. (2022). Indonesia Indeks Kualitas Udara.

Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of marketing research, 14(3), 396-402.

BI. (2020). Latest Economic Developments Outlook.

Chin, W.W. (1998), The partial least squares approach to structural equation modeling”, in Marcoulides, G.A. (Ed.), Modern Methods for Business Research, Mahwah, Erlbaum, 295 (2), 295-358.

Cohen, J. (1988), Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum, Mahwah, NJ, Creative Education 8(6).

Daziano, R.A., Bolduc, D. (2013). Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model.Transportmetrica A: Transport Science, 9(1), 74-106

Dolce, P., Esposito Vinzi, V., Lauro, C. (2017). Predictive path modeling through PLS and other component-based approaches: methodological issues and performance evaluation. in Latan, H. and Noonan, R. (Eds), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, Springer International Publishing, Cham. 153-172.

Egbue, O., Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy Policy, 48. 717–729.

ESDM. (2021). Indonesia Energy Transition Outlook.

Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: a comparison of four procedures. Internet Research, 29(3), 430-447.

Ghozali, I. (2018). Aplikasi Analisis Multivariate dengan Program IBM SPSS 25. Badan Penerbit Universitas Diponegoro: Semarang

Hair, J.F., Ringle, C.M., Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2). 139-151

Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2014), A Primer on Partial Least Squares Structural Equation Modeling, Sage, Thousand Oaks, CA.

Hair, J.F., Sarstedt, M., Ringle, C.M. (2020), Rethinking some of the rethinking of partial least squares. European Journal of Marketing, Forthcoming, 53(4).

Hair, J., Rishe, J. J., Sarstedt, M., Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Reviw. 31(1). 2-24.

Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. and Thiele, K.O. (2017). Mirror, Mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45 (5), 616-632.

Henseler, J., Ringle, C.M., Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing”, in Sinkovics, R.R. and Ghauri, P.N. (Eds) Advances in International Marketing, Emerald, Bingley, 20. 277-320.

Henseler, J., Ringle, C.M. and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1). 115-135.

Henseler, J., Hubona, G.S., Ray, P.A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems, 116(1). 1-19.

Hu, L., Bentler, P.M. (1999). Cutoff criteria for fit indexes in convariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55.

Indonesian Central Bureau of Statistics. (2021). Hasil Sensus Penduduk 2020. Badan Pusat Statistik, (5)

Indonesian Central Bureau of Statistics. (2020). Development of Motor Vehicles by Type. Badan Pusat Statistik.

IQAir. (2021). World Air Quality Report.

Maghfifiroh, M.F., Pandyaswargo, A.H., Onoda, H. (2021). Current Readiness Status of Electric Vehicles in Indonesia: Multistakeholder Perceptions. Sustainability, 13(23).

Nordlund, A., Jansson, J., Westin, K. (2016). New Transportation Technology: Norm Activation Processes and the Intention to Switch to an Electric/Hybrid Vehicle, Transportation Research Procedia, 14, 2527-2536.

Oelschlaeger, M.A.X. (2019). The Myth of the Technological Fix. Southwest. J. Philos

OECD. (2019). Indonesia’s Effort to Phase out and Rationalise Its Fossil-Fuel Subsidies.

President of Republic of Indonesia. (2019). Peraturan Presiden Nomor 55 Tahun 2019 tentang Percepatan Program Kendaraan Bermotor Listrik Berbasis Baterai (Battery Electric Vehicle) untuk Transportasi Jalan; Indonesia.

PwC. (2020). Oil and Gas in Indonesia. Taxation Guide

Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the academy of marketing science, 40, 509-525.

Santoso, M., Lestiani, D. D., Kurniawati, S., Damastuti, E., Kusmartini, I., Atmodjo, D. P. D., ... & Suprayadi, L. S. (2020). Assessment of urban air quality in Indonesia. Aerosol and Air Quality Research, 20, 2142-2158.

Subekti, R.A., Sudibyo, H., Susanti, V., Saputra, H.M., Hartanto, A. (2014). Peluang dan Tantangan Pengembangan Mobil Listrik Nasional. LIPI Press, Jakarta.

Shalender, K., & Sharma, N. (2021). Using extended theory of planned behaviour (TPB) to predict adoption intention of electric vehicles in India. Environment, Development and Sustainability, 23(1), 665-681.

Shmueli, G. (2010). To explain or to predict?.

Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, 553-572.

Tu, J. C., & Yang, C. (2019). Key factors influencing consumers’ purchase of electric vehicles. Sustainability, 11(14), 3863.

Wang, C., Quddus, M., & Ison, S. (2013). A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK. Transportmetrica A: Transport Science, 9(2), 124-148.

Wirabrata, A. (2019). Percepatan Program Kendaraan Bermotor Listrik di Indonesia. Jakarta

Wondola, D.W., Aulele, S.M., Lembang, F.K. (2020). Partial Least Square (PLS) Method of Addressing Multicollinearity Problems in Multiple Linear Regressions (Case Studies: Cost of electricity bills and factors affecting it). Journal of Physics: Conference Series, 1463

Zhong, S., Yu, Z., Zhu, W. (2019). Study of the effects of air pollutants on human health based on baidu indices of disease symptoms and air quality monitoring data in Beijing, China. International Journal of Environmental Resrach and Public Health, 16(6)

Downloads

Published

2023-09-30

Issue

Section

Articles
Abstract 411  .
PDF downloaded 563  .