Despite warnings about limited fossil fuel as well as the detrimental impacts of burning such fuels, the number of conventionally fueled vehicles in use is growing worldwide. While energy efficient vehicles (EEVs) produce far less emissions and provide power from renewable sources, EEVs are yet to get full acceptance from the local market and the penetration rate is still low. In Malaysia, thesales trend shows that there is a low level of EEV purchased, where energy efficient vehicle only owned 3 percent of market share in Malaysian automotive industry. To fill this gap, this study focuses to examine the relationship between consumers’ attitude towards EEV and their intention to purchase energy efficient vehicle. Data was collected using quantitative approach through mall intercept survey method at ten selected malls in Peninsular Malaysia. Respondents were intercepted at the entrance of the mall and were invited to take part in the survey. The complete process of data collection took five months and comprised a total of 515 respondents. The hypothesis in the present study was tested using Partial Least Squares (PLS). The analysis revealed a positive relationship exists between consumers’ attitudes on EEVs and intention to purchase EEVs. The present study provides recommendations, limitations, and suggestions for future study.
Keywords
Energy efficient vehicle; Attitude; Purchase intention; Consumer purchase behaviour.
W. Tian, Z. Liao, and J. Zhang, “An optimization of artificial neural network model for predicting chlorophyll dynamics,” Ecological Modelling, vol. 364, pp. 42–52, Nov. 2017.
R. R. Ferdinand, “A Nonlinear Coupled Phytoplankton Dynamics Model,” Numerical Functional Analysis and Optimization, vol. 23, no. 5–6, pp. 515–528, Jan. 2002.
Y. Wang and L. Li, “Li-ion battery dynamics model parameter estimation using datasheets and particle swarm optimization,” International Journal of Energy Research, vol. 40, no. 8, pp. 1050–1061, Jan. 2016.
Q. A. Chaudhry and Q. M. Al-Mdallal, “Review of design optimization of fluid machinery: applying computational fluid dynamics and numerical optimization,” Complex Adaptive Systems Modeling, vol. 7, no. 1, Jul. 2019.
S. Roy, S. Mukhopadhyay, and P. P. Sengupta, “Simulation Techniques on System Dynamics Modeling of Productioninventory Control Model,” Journal of Information and Optimization Sciences, vol. 29, no. 1, pp. 17–28, Jan. 2008.
Z. MENG and H. IGARASHI, “Offline Design of Machine Dynamics using Individual Operation Model,” The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec), vol. 2019, no. 0, pp. 2A2–M04, 2019.
F. Feng, Z. Tang, and L. Wang, “A Fault Tolerance Optimization Model of the China Railway Geographic Network Topological Structure,” Discrete Dynamics in Nature and Society, vol. 2015, pp. 1–8, 2015.
B. Liu, X. Mei, H. Jiang, and L. Wu, “A Nonpenalty Neurodynamic Model for Complex-Variable Optimization,” Discrete Dynamics in Nature and Society, vol. 2021, pp. 1–10, Feb. 2021.
L. Plachel, “A unified model for regularized and robust portfolio optimization,” Journal of Economic Dynamics and Control, vol. 109, p. 103779, Dec. 2019.
H. Chen, Y. Zhu, K. Hu, and X. He, “Hierarchical Swarm Model: A New Approach to Optimization,” Discrete Dynamics in Nature and Society, vol. 2010, pp. 1–30, 2010.
L. Wang, Y. Qin, J. Xu, and L. Jia, “A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling,” Discrete Dynamics in Nature and Society, vol. 2012, pp. 1–22, 2012.
X. Feng, Y. Zhang, Y. Li, and W. Wang, “A Location-Allocation Model for Seaport-Dry Port System Optimization,” Discrete Dynamics in Nature and Society, vol. 2013, pp. 1–9, 2013.
Q. Yan, Q. Zhang, and X. Zou, “A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint,” Discrete Dynamics in Nature and Society, vol. 2016, pp. 1–8, 2016.
D. Ying, “Optimization of Tourism Real Estate Development Project Based on Option Premium Model,” Discrete Dynamics in Nature and Society, vol. 2021, pp. 1–8, Aug. 2021.
J. D. Amato and T. Laubach, “Estimation and control of an optimization-based model with sticky prices and wages,” Journal of Economic Dynamics and Control, vol. 27, no. 7, pp. 1181–1215, May 2003.
Y. Li, Y. Huang, and Y. Zhou, “Optimization of the Actuarial Model of Defined Contribution Pension Plan,” Discrete Dynamics in Nature and Society, vol. 2014, pp. 1–7, 2014.
C. A. Ocampo and R. Mathur, “Variational Model for Optimization of Finite-Burn Escape Trajectories Using a Direct Method,” Journal of Guidance, Control, and Dynamics, vol. 35, no. 2, pp. 598–608, Mar. 2012.
H. Pan and Z. Liu, “A Queuing Network Based Optimization Model for Calculating Capacity of Subway Station,” Discrete Dynamics in Nature and Society, vol. 2017, pp. 1–7, 2017.
E. Selerio and R. Maglasang, “Minimizing production loss consequent to disasters using a subsidy optimization model: a pandemic case,” Structural Change and Economic Dynamics, vol. 58, pp. 112–124, Sep. 2021.
L. Zhu, X. Wang, Z. Zhang, and C. Lei, “Spatial dynamics and optimization method for a rumor propagation model in both homogeneous and heterogeneous environment,” Nonlinear Dynamics, vol. 105, no. 4, pp. 3791–3817, Aug. 2021.
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Author(s) thanks to Dr.Michael Gruning for this research completion and support.
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Michael Gruning
Michael Gruning
Master’s in management Analytics, Mannheim Business School, Germany.
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Cite this article
Vahe Andonians Salmas and Michael Gruning, “Assessing the Empirical Evidence of Attitude Intention Relationship in the Purchase of Energy Efficient Vehicle”, Journal of Enterprise and Business Intelligence, vol.2, no.1, pp. 001-007, January 2022. doi: 10.53759/5181/JEBI202202001.
