Assessing factors determining people’s decision to adopt electric motorcycles (EMs) through the lens of the technology acceptance model (TAM)

Main Article Content

Nicholas Wilson
Sentot Basuki Prayitno

Abstract

Over recent years, the Indonesian government has been consistently encouraging its citizens to transition from traditional, fuel-based vehicles to environmentally friendly electric-powered vehicles. Among these electric vehicles, Electric Motorcycles (EMs) have made their way into the Indonesian market. However, the adoption of EMs remains relatively low compared to conventional motorcycles due to various factors. This study aims to comprehensively analyze the determinants affecting individuals’ decisions to adopt EMs, focusing on the Technology Acceptance Model (TAM) framework. Next, to collect data from respondents, a survey method employing questionnaires was utilized, employing a purposive sampling technique to ensure that all participants met the predetermined criteria. Specifically, respondents were individuals who had adopted EMs from various brands within the past year. A 5-point Likert scale was employed to gauge respondents’ opinions and perceptions. Over approximately four months, from March 2023 to July 2023, questionnaires were distributed, resulting in the successful collection of 58 valid responses. Subsequently, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the gathered data. The findings of the analysis reveal that two key factors, namely, perceived ease of use and perceived usefulness, exert significant and positive influence on individuals’ decisions to adopt electric motorcycles (EMs).


Dalam kurun waktu beberapa tahun terakhir, pemerintah Indonesia secara aktif dan masif mendorong masyarakatnya untuk mulai beralih dari kendaraan BBM konvensional ke kendaraan listrik (electric vehicle) yang lebih ramah lingkungan. Adapun salah satu jenis kendaraan listrik (electric vehicle) yang dijual (dan tersedia) di Indonesia adalah motor listrik (electric motorcycle). Namun, terlepas dari berbagai usaha yang telah dilakukan oleh pemerintah, serta dikarenakan oleh satu dan lain hal, jumlah masyarakat yang telah membeli dan menggunakan motor listrik ini cenderung masih sangat sedikit dibandingkan dengan jumlah masyarakat yang membeli motor konvensional berbahan bakar minyak. Alhasil, berdasarkan pada fenomena ini, maka studi ini mencoba untuk menganalisis faktor-faktor yang cenderung mampu memengaruhi keputusan masyarakat di dalam mengadopsi motor listrik di Indonesia dari perspektif Technology Acceptance Model (TAM). Mengimplementasikan metode survei dengan kuesioner digunakan sebagai alat untuk mengumpulkan data dari para responden, metode sampling berupa purposive sampling diterapkan dengan tujuan untuk memastikan agar seluruh responden yang berpartisipasi telah memenuhi kriteria yang ditentukan dalam penelitian ini. Adapun kriteria responden yang ditetapkan adalah masyarakat Indonesia yang telah mengadopsi motor listrik (merek apa pun) dalam kurun waktu 1 tahun terakhir. Berikutnya, sebanyak 58 data berhasil dikumpulkan dari para responden, dimana, seluruh data ini kemudian dianalisis dengan menggunakan metode PLS-SEM. Berdasarkan pada hasil analisis data yang telah dilakukan, peneliti menyimpulkan bahwa kedua faktor yang diuji, yaitu perceived usefulness dan perceived ease of use, secara signifikan memengaruhi keputusan seseorang di dalam mengadopsi motor listrik.

Article Details

How to Cite
Wilson, N., & Prayitno, S. B. (2023). Assessing factors determining people’s decision to adopt electric motorcycles (EMs) through the lens of the technology acceptance model (TAM). Jurnal Manajemen Bisnis Dan Kewirausahaan, 7(6), 1440–1451. https://doi.org/10.24912/jmbk.v7i6.26239
Section
Articles
Author Biographies

Nicholas Wilson, Universitas Bunda Mulia

Faculty of Social Sciences and Humanities, Department of Management

Sentot Basuki Prayitno, Sampoerna University

Faculty of Business, Department of Management

References

Ahmad, A., Rasul, T., Yousaf, A., & Zaman, U. (2020). Understanding factors influencing elderly diabetic patients’ continuance intention to use digital health wearables: Extending the technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 81. https://doi.org/10.3390/JOITMC6030081

Alnemer, H. A. (2022). Determinants of digital banking adoption in the Kingdom of Saudi Arabia: A technology acceptance model approach. Digital Business, 2(2), 100037. https://doi.org/10.1016/j.digbus.2022.100037

Alyoussef, I. Y. (2022). Acceptance of a flipped classroom to improve university students’ learning: An empirical study on the TAM model and the unified theory of acceptance and use of technology (UTAUT). Heliyon, 8(12), e12529. https://doi.org/10.1016/j.heliyon.2022.e12529

Arief, M., Mustikowati, R. I., & Chrismardani, Y. (2023). Why customers buy an online product? The effects of advertising attractiveness, influencer marketing and online customer reviews. LBS Journal of Management & Research, 21(1), 81–99. https://doi.org/10.1108/lbsjmr-09-2022-0052

CNN Indonesia. (2022, October 14). Berapa populasi kendaraan listrik di Indonesia saat ini? CNN Indonesia. https://www.cnnindonesia.com/otomotif/20221013160146-603-860170/berapa-populasi-kendaraan-listrik-di-indonesia-saat-ini

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Doanh, N. K., Do Dinh, L., & Quynh, N. N. (2022). Tea farmers’ intention to participate in Livestream sales in Vietnam: The combination of the Technology Acceptance Model (TAM) and barrier factors. Journal of Rural Studies, 94, 408–417. https://doi.org/10.1016/j.jrurstud.2022.05.023

Ge, Y., Qi, H., & Qu, W. (2023). The factors impacting the use of navigation systems: A study based on the technology acceptance model. Transportation Research Part F: Traffic Psychology and Behaviour, 93, 106–117. https://doi.org/10.1016/j.trf.2023.01.005

Gupta, A., Kumar, J., Tewary, T., & Virk, N. K. (2022). Influence of cartoon characters on generation alpha in purchase decisions. Young Consumers, 23(2), 282–303. https://doi.org/10.1108/YC-06-2021-1342

Hanaysha, J. R. (2018). An examination of the factors affecting consumer’s purchase decision in the Malaysian retail market. PSU Research Review, 2(1), 7–23. https://doi.org/10.1108/PRR-08-2017-0034

Helmers, E. (2022). The energy and emissions case and the lifecycle impact of electric cars. In G. Parkhurst & W. Clayton (Eds.), Electrifying Mobility: Realising a Sustainable Future for the Car (Vol. 15, pp. 33–50). Emerald Publishing Limited. https://doi.org/10.1108/S2044-994120220000015005

Hubert, M., Blut, M., Brock, C., Zhang, R. W., Koch, V., & Riedl, R. (2019). The influence of acceptance and adoption drivers on smart home usage. European Journal of Marketing, 53(6), 1073–1098. https://doi.org/10.1108/EJM-12-2016-0794

Jaiswal, D., Kant, R., Singh, P. K., & Yadav, R. (2022). Investigating the role of electric vehicle knowledge in consumer adoption: Evidence from an emerging market. Benchmarking, 29(3), 1027–1045. https://doi.org/10.1108/BIJ-11-2020-0579

Jaiswal, D., Kaushal, V., Kant, R., & Kumar Singh, P. (2021). Consumer adoption intention for electric vehicles: Insights and evidence from Indian sustainable transportation. Technological Forecasting and Social Change, 173, 121089. https://doi.org/10.1016/j.techfore.2021.121089

Jokar, N. K., Noorhosseini, S. A., Allahyari, M. S., & Damalas, C. A. (2017). Consumers’ acceptance of medicinal herbs: An application of the technology acceptance model (TAM). Journal of Ethnopharmacology, 207, 203–210. https://doi.org/10.1016/j.jep.2017.06.017

Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212

Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on Technology Acceptance Model (TAM) and Technology, Organization, and Environment (TOE) framework. Journal of Building Engineering, 45, 103518. https://doi.org/10.1016/j.jobe.2021.103518

Keni, Wilson, N., & Teoh, A. P. (2023). Antecedents of viewers’ watch behavior toward YouTube videos: evidence from the most populous Muslim-majority country. Journal of Islamic Marketing. https://doi.org/10.1108/JIMA-01-2023-0008

Kim, Y. G., & Woo, E. (2016). Consumer acceptance of a quick response (QR) code for the food traceability system: Application of an extended technology acceptance model (TAM). Food Research International, 85, 266–272. https://doi.org/10.1016/j.foodres.2016.05.002

Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503–513. https://doi.org/10.1016/j.jbusres.2018.10.021

Natasia, S. R., Wiranti, Y. T., & Parastika, A. (2021). Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach. Procedia Computer Science, 197, 512–520. https://doi.org/10.1016/j.procs.2021.12.168

Ngoc, A. M., Nishiuchi, H., & Nhu, N. T. (2023). Determinants of carriers’ intentions to use electric cargo vehicles in last-mile delivery by extending the technology acceptance model: A case study of Vietnam. International Journal of Logistics Management, 34(1), 210–235. https://doi.org/10.1108/IJLM-12-2021-0566

Novita, & Rowena, J. (2019). Determinant factors of Indonesian people’s fish purchase intention. British Food Journal, 123(6), 2272–2277. https://doi.org/10.1108/BFJ-01-2019-0067

Oh, J., & Yoon, S. J. (2014). Validation of Haptic Enabling Technology Acceptance Model (HE-TAM): Integration of IDT and TAM. Telematics and Informatics, 31(4), 585–596. https://doi.org/10.1016/j.tele.2014.01.002

Okpala, I., Nnaji, C., & Awolusi, I. (2022). Wearable sensing devices acceptance behavior in construction safety and health: Assessing existing models and developing a hybrid conceptual model. Construction Innovation, 22(1), 57–75. https://doi.org/10.1108/CI-04-2020-0056

Putra, I. G. W. S. C., Wijaya, R. W. N., & Noverin, D. T. (2022). Perbandingan pengaruh promotion mix terhadap keputusan penggunaan digital wallet pada e-marketplace Tokopedia dan Shopee. BISMA: Jurnal Bisnis Dan Manajemen, 16(1), 1–9. https://doi.org/10.19184/bisma.v16i1.23972

Putri, G. A., Widagdo, A. K., & Setiawan, D. (2023). Analysis of financial technology acceptance of peer to peer lending (P2P lending) using extended technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 100027. https://doi.org/10.1016/j.joitmc.2023.100027

Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended Technology Acceptance Model (TAM). Computers and Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732

Ramkumar, M., Schoenherr, T., Wagner, S. M., & Jenamani, M. (2019). Q-TAM: A quality technology acceptance model for predicting organizational buyers’ continuance intentions for e-procurement services. International Journal of Production Economics, 216, 333–348. https://doi.org/10.1016/j.ijpe.2019.06.003

Roberts, C. (2022). Easy street for low-carbon mobility? The political economy of mass electric car adoption. In G. Parkhurst & W. Clayton (Eds.), Electrifying Mobility: Realising a Sustainable Future for the Car (Vol. 15, pp. 13–31). Emerald Publishing Limited. https://doi.org/10.1108/S2044-994120220000015004

Rodrigues, A., Godwin, B. J., & George, J. P. (2023). Brand anthropomorphism’s impact on real estate purchase decisions of young buyers in India and the underlying reliance on spatial memory. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/IJHMA-12-2022-0178

Rouidi, M., Elouadi, A. E., Hamdoune, A., Choujtani, K., & Chati, A. (2022). TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: A systematic review. Informatics in Medicine Unlocked, 32, 101008. https://doi.org/10.1016/j.imu.2022.101008

Schade, W., Kley, F., Köhler, J., & Peters, A. (2012). Contextual requirements for electric vehicles in developed and developing countries: The example of China. In R. L. Mackett, A. D. May, M. Kii, & H. Pan (Eds.), Sustainable Transport for Chinese Cities (Vol. 3, pp. 231–253). Emerald Group Publishing Limited. https://doi.org/10.1108/S2044-9941(2012)0000003013

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009

Statista. (2023). Number of motorcycles in use in Indonesia from 2017 to 2022. Statista. https://www.statista.com/statistics/978944/indonesia-number-of-motorcycles-use/#:~:text=In 2022%2C the number of,far ahead of passenger cars.

Susilawaty, L., & Wilson, N. (2021). Peranan benefits, trust serta ease of use terhadap usage intention pada sektor e-payment di Jabodetabek. Jurnal Muara Ilmu Ekonomi dan Bisnis, 5(2), 307–320. https://doi.org/10.24912/jmieb.v5i2.11852

Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers and Education, 53(3), 1000–1009. https://doi.org/10.1016/j.compedu.2009.05.017

Türker, C., Altay, B. C., & Okumuş, A. (2022). Understanding user acceptance of QR code mobile payment systems in Turkey: An extended TAM. Technological Forecasting and Social Change, 184, 121968. https://doi.org/10.1016/j.techfore.2022.121968

Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information and Management, 51(2), 249–259. https://doi.org/10.1016/j.im.2013.12.003

Wang, C., Ahmad, S. F., Bani Ahmad Ayassrah, A. Y. A., Awwad, E. M., Irshad, M., Ali, Y. A., Al-Razgan, M., Khan, Y., & Han, H. (2023). An empirical evaluation of technology acceptance model for artificial intelligence in e-commerce. Heliyon, 9(8), e18349. https://doi.org/10.1016/j.heliyon.2023.e18349

Wibisono, K., & Keni. (2023). Pengaruh perceived value, customer satisfaction, dan brand association terhadap repurchase intention. Jurnal Manajemen Bisnis dan Kewirausahaan, 7(4), 750–759. https://doi.org/10.24912/jmbk.v7i4.25360

Wilson, N., Keni, & Tan, P. H. P. (2019). The effect of website design quality and service quality on repurchase intention in the E-commerce industry: A cross-continental analysis. Gadjah Mada International Journal of Business, 21(2), 187–222. https://doi.org/10.22146/gamaijb.33665

Wilson, N., Keni, & Tan, P. H. P. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers’ loyalty in china. Gadjah Mada International Journal of Business, 23(3), 262–294. https://doi.org/10.22146/gamaijb.32106

Winata, A. P., & Keni. (2023). Faktor-faktor yang memengaruhi keputusan pemilihan universitas di Jakarta. Jurnal Manajemen Bisnis dan Kewirausahaan, 7(4), 793–802. https://doi.org/10.24912/jmbk.v7i4.25378

Yankun, S. (2020). An empirical study on the influencing factors of consumers’ willingness to use pure electric vehicle based on TAM model. 2020 16th Dahe Fortune China Forum and Chinese High-Educational Management Annual Academic Conference (DFHMC), 289–292. https://doi.org/10.1109/DFHMC52214.2020.00063

Zhang, B. S., Ali, K., & Kanesan, T. (2022). A model of extended technology acceptance for behavioral intention toward EVs with gender as a moderator. Frontiers in Psychology, 13, 1080414. https://doi.org/10.3389/fpsyg.2022.1080414

Zhong, B., Huang, Y., & Liu, Q. (2021). Mental health toll from the coronavirus: Social media usage reveals Wuhan residents’ depression and secondary trauma in the COVID-19 outbreak. Computers in Human Behavior, 114, 106524. https://doi.org/10.1016/j.chb.2020.106524