A aceitação de veículos elétricos: um modelo derivado da TAM
DOI:
https://doi.org/10.5585/remark.v23i4.24015Palavras-chave:
Veículos elétricos, Modelo TAM, Intenção de uso, Comportamento do consumidorResumo
Objetivo: Este estudo tem por objetivo desenvolver e testar empiricamente uma derivação do Modelo de Aceitação de Tecnologia (TAM) sobre a aceitação de veículos elétricos (VEs).
Abordagem Teórica/Método: A análise foi baseada num modelo teórico original adaptado e derivado do Modelo de Aceitação de Tecnologia (TAM), uma teoria baseada em sistemas de informação que modela como os usuários passam a aceitar e usar uma tecnologia. Utilizou-se um questionário online aplicado em estudantes de pós-graduação no território Brasileiro, e as respostas analisadas por meio de equações estruturais. A amostra final foi composta por 209 pós-graduandos de três universidades no Brasil. O instrumento de coleta de dados foi validado por Análise Fatorial Confirmatória. Para o teste de hipóteses, foi aplicada a análise de caminhos (Path Analysis).
Resultados: Os resultados indicaram que há uma relação positiva entre as intenções de uso de veículos elétricos e as percepções de Facilidade de Uso, Utilidade Verde, e Prontidão da Infraestrutura em Potencial. Os resultados também demonstraram que não há evidências de uma relação positiva direta entre o fator Preocupação Ambiental e a intenção de uso de VEs.
Contribuições teóricas e para a gestão: O modelo teórico original aqui proposto e testado contribui para a teoria do TAM. Ao estudar fatores que estimulam a intenção de uso e o comportamento do consumidor em potencial de VEs no mercado brasileiro, contribui-se para futuras políticas gerenciais e governamentais neste segmento.
Relevância/Originalidade: O modelo teórico acrescenta fatores originais ao TAM, para explicar a aceitação dos VEs.
Downloads
Referências
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Ahmed, M., Almotairi, M. A., Ullah, S., & Alam, A. (2014). Mobile banking adoption: a qualitative approach towards the assessment of TAM model in an emerging economy. Academic Research International, 5(6), 248.
Anderson, H. (2014). The Future of Electric Cars. Retrieved from http://large.stanford.edu/courses/2014/ph240/anderson-h1/ , accessed on December 4th, 2022.
Anfavea (2023). Anuário da Indústria Automobilística Brasileira. Retrieved from https://anfavea.com.br/site/wp-content/uploads/2023/04/ANUARIO-ANFAVEA-2023.pdf, accessed on March 24th, 2024.
Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the association for information systems, 8(4), 7.
Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge, NY.
Chen, S. Y. (2016). Green helpfulness or fun? Influences of green perceived value on the green loyalty of users and non-users of public bikes. Transport Policy, 47, 149-159. https://doi.org/10.1016/j.tranpol.2016.01.014
Chen, S. Y. (2016). Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system. Transportation Research Part A: Policy and Practice, 88, 58-72. https://doi.org/10.1016/j.tra.2016.03.008
Chen, A., Lu, Y., & Wang, B. (2017). Customers’ purchase decision-making process in social commerce: A social learning perspective. International Journal of Information Management, 37(6), 627-638. https://doi.org/10.1016/j.ijinfomgt.2017.05.001
Cheng, H. H., & Huang, S. W. (2013). Exploring antecedents and consequence of online group-buying intention: An extended perspective on theory of planned behavior. International Journal of Information Management, 33(1), 185-198. https://doi.org/10.1016/j.ijinfomgt.2012.09.003
Costa, E., Horta, A., Correia, A., Seixas, J., Costa, G., & Sperling, D. (2021). Diffusion of electric vehicles in Brazil from the stakeholders' perspective. International Journal of Sustainable Transportation, 15(11), 865-878. https://doi.org/10.1080/15568318.2020.1827317
Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020). Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renewable and Sustainable Energy Reviews, 120, 109618. https://doi.org/10.1016/j.rser.2019.109618
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Fang, Y., Wei, W., Mei, S., Chen, L., Zhang, X., & Huang, S. (2020). Promoting electric vehicle charging infrastructure considering policy incentives and user preferences: An evolutionary game model in a small-world network. Journal of cleaner production, 258, 120753. https://doi.org/10.1016/j.jclepro.2020.120753
Franke, T., & Krems, J. F. (2013). What drives range preferences in electric vehicle users? Transport Policy, 30, 56-62. https://doi.org/10.1016/j.tranpol.2013.07.005
Fishbein, Martin; Ajzen, Icek. (1977): Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, v. 10, n. 2.
Funke, S. Á., Sprei, F., Gnann, T., & Plötz, P. (2019). How much charging infrastructure do electric vehicles need? A review of the evidence and international comparison. Transportation research part D: transport and environment, 77, 224-242. https://doi.org/10.1016/j.trd.2019.10.024
Greaves, M., Zibarras, L. D., & Stride, C. (2013). Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. Journal of Environmental Psychology, 34, 109-120. https://doi.org/10.1016/j.jenvp.2013.02.003
Greene, D. L., Kontou, E., Borlaug, B., Brooker, A., & Muratori, M. (2020). Public charging infrastructure for plug-in electric vehicles: What is it worth?. Transportation Research Part D: Transport and Environment, 78, 102182. https://doi.org/10.1016/j.trd.2019.11.011
Habich-Sobiegalla, S., Kostka, G., & Anzinger, N. (2018). Electric vehicle purchase intentions of Chinese, Russian and Brazilian citizens: An international comparative study. Journal of cleaner production, 205, 188-200. https://doi.org/10.1016/j.jclepro.2018.08.318
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7): Pearson, Upper Saddle River.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Evaluating model fit: a synthesis of the structural equation modelling literature. In 7th European Conference on research methodology for business and management studies. pp. 195-200.
Hsu, C. L., & Chen, M. C. (2018). How does gamification improve user experience? An empirical investigation on the antecedences and consequences of user experience and its mediating role. Technological Forecasting and Social Change, 132, 118-129. https://doi.org/10.1016/j.techfore.2018.01.023
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hulin, C., Netemeyer, R., & Cudeck, R. (2001). Can a reliability coefficient be too high? Journal of Consumer Psychology, 10(1/2), 55-58.
Kahn, M. E. (2007). Do greens drive Hummers or hybrids? Environmental ideology as a determinant of consumer choice. Journal of Environmental Economics and Management, 54(2), 129-145. https://doi.org/10.1016/j.jeem.2007.05.001
Kaplan, S., Monteiro, M. M., Anderson, M. K., Nielsen, O. A., & Dos Santos, E. M. (2017). The role of information systems in non-routine transit use of university students: Evidence from Brazil and Denmark. Transportation Research Part A: Policy and Practice, 95, 34-48. https://doi.org/10.1016/j.tra.2016.10.029
Kautish, P., & Dash, G. (2017). Environmentally concerned consumer behavior: evidence from consumers in Rajasthan. Journal of Modelling in Management, 12, 712–738. https://doi.org/10.1108/JM2-05-2015-0021
Kenny, D. A. (2015). Measuring model fit. enny, D. A. (2011). Measuring model fit. Retrieved from http://davidakenny.net/cm/fit.htm . Accessed on December 4th 2022.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications, NY.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84. https://doi.org/10.1037/1082-989X.4.1.84
Maniatis, P. (2016). Investigating factors influencing consumer decision-making while choosing green products. Journal of Cleaner Production, 132, 215-228. https://doi.org/10.1016/j.jclepro.2015.02.067
Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519-530. https://doi.org/10.1093/biomet/57.3.519
Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhyā: The Indian Journal of Statistics, Series B, 115-128.
Marôco, J. (2010). Análise de equações estruturais: Fundamentos teóricos, software & aplicações. Report Number, Pêro Pinheiro, Portugal.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
Minton, A. P., & Rose, R. L. (1997). The effects of environmental concern on environmentally friendly consumer behavior: An exploratory study. Journal of Business research, 40(1), 37-48. https://doi.org/10.1016/S0148-2963(96)00209-3
I. G. E. Outlook (2020). Entering the decade of electric drive. International Energy Agency. Retrieved from https://www.coleurope.eu/global-ev-outlook-2020-entering-decade-electric-drive , accessed on 01/02/2023.
Ozaki, R., & Sevastyanova, K. (2011). Going hybrid: An analysis of consumer purchase motivations. Energy policy, 39(5), 2217-2227. https://doi.org/10.1016/j.enpol.2010.04.024
Park, E., Kim, H., & Ohm, J. Y. (2015). Understanding driver adoption of car navigation systems using the extended technology acceptance model. Behaviour & Information Technology, 34(7), 741-751. https://doi.org/10.1080/0144929X.2014.963672
Pires, P. J., Da Costa Filho, B. A. (2008). Fatores do índice de prontidão à tecnologia (TRI) como elementos diferenciadores entre usuários e não usuários de internet banking e como antecedentes do modelo de aceitação de tecnologia (TAM). Revista de Administração Contemporânea, v. 12, n. 2, p. 429-4. https://doi.org/10.1590/S1415-65552008000200007
Raubenheimer, J. (2004). An item selection procedure to maximize scale reliability and validity. SA Journal of Industrial Psychology, 30(4), 59-64.
Rietmann, Nele, and Theo Lieven. "How policy measures succeeded to promote electric mobility–Worldwide review and outlook." Journal of cleaner production 206 (2019): 66-75.
Ringle, C. M., Da Silva, D., & de Souza Bido, D. (2014). Modelagem de equações estruturais com utilização do SmartPLS. REMark-Revista Brasileira de Marketing, 13(2), 56-73.
Russell, M. L. (1978). Behavioral consultation: Theory and process. The Personnel and Guidance Journal, 56(6), 346-350. https://doi.org/10.1002/j.2164-4918.1978.tb04645.x
Schweizer, K. (2010). Some guidelines concerning the modeling of traits and abilities in test construction. European Journal of Psychological Assessment, 26 (1), pp. 1-2. https://doi.org/10.1027/1015-5759/a000001
Souza, A. C. D., Alexandre, N. M. C., & Guirardello, E. D. B. (2017). Propriedades psicométricas na avaliação de instrumentos: avaliação da confiabilidade e da validade. Epidemiologia e serviços de saúde, 26, 649-659. https://doi.org/10.5123/S1679-49742017000300022
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of management information systems, 11(1), 167-187. https://doi.org/10.1080/07421222.1994.11518035
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
Wang, B., Shen, Y., & Jin, Y. (2017). Measurement of public awareness of climate change in China: Based on a national survey with 4,025 samples. Chinese Journal of Population Resources and Environment, 15(4), 285-291. https://doi.org/10.1080/10042857.2017.1418276
Wang, N., Tang, L., & Pan, H. (2019). A global comparison and assessment of incentive policy on electric vehicle promotion. Sustainable Cities and Society, 44, 597-603. https://doi.org/10.1016/j.scs.2018.10.024
Weston, R., & Gore, P. A., Jr. (2006). A Brief Guide to Structural Equation Modeling. The Counseling Psychologist, 34(5), 719–751. https://doi.org/10.1177/0011000006286345
Woo, J., Choi, H., & Ahn, J. (2017). Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity generation mix: A global perspective. Transportation Research Part D: Transport and Environment, 51, 340-350. https://doi.org/10.1016/j.trd.2017.01.005
Wu, J. H., Wu, C. W., Lee, C. T., & Lee, H. J. (2015). Green purchase intentions: An exploratory study of the Taiwanese electric motorcycle market. Journal of Business Research, 68(4), 829-833. https://doi.org/10.1016/j.jbusres.2014.11.036
Wu, J., Liao, H., Wang, J. W., & Chen, T. (2019). The role of environmental concern in the public acceptance of autonomous electric vehicles: A survey from China. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 37-46. https://doi.org/10.1016/j.trf.2018.09.029
Yusoff, M. S. B., Rahim, A. F. A., Mat Pa, M. N., See, C. M., Ja'afar, R., & Esa, A. R. (2011). The validity and reliability of the USM Emotional Quotient Inventory (USMEQ-i): its use to measure Emotional Quotient (EQ) of future medical students. International Medical Journal, 18(4), 293-299.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2024 ReMark - Revista Brasileira de Marketing
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
