Analysing the Impact of Social Influence on Electric Vehicle Adoption: A Deep Learning-Based Simulation Study in Jharkhand, India
DOI:
https://doi.org/10.22399/ijcesen.371Keywords:
Green Mobility, Electric Vehicles, EV Charging Infrastructure, Sustainable Transportation, Emission ReductionAbstract
The transition towards sustainable transportation, particularly in regions like Jharkhand, has gained paramount importance amidst escalating environmental concerns and evolving market dynamics. This study delves into the consumer psyche regarding green mobility adoption, aiming to decipher the critical factors influencing individuals' future intentions towards eco-friendly transportation options. Utilizing a comprehensive survey questionnaire, data was gathered from 300 respondents, focusing on variables such as awareness of green mobility, the perceived importance of environmental impact, social media influence, peer recommendations, availability of green mobility services, cost considerations, government support, convenience of green transportation, and awareness campaigns. The collected data underwent rigorous regression analysis to uncover correlations and predictive insights. The regression model revealed a notable R-squared value of 0.765, indicating a substantial portion of variance in future intentions is explained by the chosen predictors. Among these, variables like the perceived importance of environmental impact, government support, and convenience of green mobility emerged as statistically significant influencers, suggesting their pivotal role in shaping consumer behaviour towards sustainable transportation. Contrarily, factors such as social media influence, peer recommendations, availability of green mobility services, cost considerations, and awareness campaigns exhibited non-significant coefficients, implying a lesser impact on individuals' future intentions in this context. These findings bear significant implications for stakeholders involved in promoting green mobility solutions. Ultimately, this study contributes to the ongoing discourse on sustainable mobility by shedding light on the multifaceted dynamics influencing consumer decision-making using Python a deep learning simulation, thereby guiding strategic interventions for a greener, more eco-conscious future.
References
Chawla, U., Mohnot, R., Mishra, V., Singh, H. V., & Singh, A. K. (2023). Factors influencing customer preference and adoption of electric vehicles in India: a journey towards more sustainable transportation. Sustainability, 15(8), 7020.
Amin, A., Altinoz, B., & Dogan, E. (2020). Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization. Clean Technologies and Environmental Policy, 22, 1725-1734.
Mishra, S., & Malhotra, G. (2019). Is India ready for e-mobility? An exploratory study to understand e-vehicles purchase intention. Theoretical Economics Letters, 9(02), 376.
Tarigan, A. K. (2019). Expectations, attitudes, and preferences regarding support and purchase of eco-friendly fuel vehicles. Journal of Cleaner Production, 227, 10-19.
Jha, R., & Singh, M. K. (2024). Electric Mobility Adoption in India–Policy & Initiatives to Promote E-Mobility in Jharkhand. International Management Review, 20(1), 63-74.
Kumari, N., Bara, N., Jha, B. K., & Choudhary, S. B. (2022). Socio-psychological spectrum of farmer's and their perception about climate change in Ranchi, Jharkhand. Indian Journal of Extension Education, 58(2), 92-95.
Igual, L., & Seguí, S. (2024). Regression analysis. In Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (pp. 99-123). Cham: Springer International Publishing.
Huang, H. H., & He, Q. (2024). Nonlinear regression analysis. arXiv preprint arXiv:2402.05342.
Rohilla, K., Desai, A., & Patel, C. R. (2024). A Cutting-Edge Examination of the Dichotomy of Electric Vehicles as a Symbol of “Sustainable Mobility” and “Clean Energy”. Journal of The Institution of Engineers (India): Series A, 105(1), 209-227.
Le, T. T., Tran, P. Q., Lam, N. P., Tra, M. N. L., & Uyen, P. H. P. (2024). Corporate social responsibility, green innovation, environment strategy and corporate sustainable development. Operations Management Research, 17(1), 114-134.
Kumar, B. A., Jyothi, B., Singh, A. R., Bajaj, M., Rathore, R. S., & Berhanu, M. (2024). A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape. Scientific Reports, 14(1), 3261.
Echeverría, L., Gimenez-Nadal, J. I., & Molina, J. A. (2022). Green mobility and well-being. Ecological Economics, 195, 107368.
Batool, T., Ross, V., Blerk, J. V., Neven, A., Dendale, P., Janssens, D., ... & Brijs, K. (2024). Promoting sustainable transportation: a transtheoretical examination of active transport modes. Sustainability, 16(2), 472.
Sestino, A., Amatulli, C., & Guido, G. (2024). Consumers’ innovativeness and conspicuous consumption orientation as predictors of environmentalism: an investigation in the context of smart mobility. Technology Analysis & Strategic Management, 36(1), 59-72.
Pal, K., Saraswat, D., & Budhraja, N. (2023). An Integrated Entropy-TOPSIS Approach for Electric Vehicle Selection. Int. J. Exp. Res. Rev, 36, 311-318.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Orr, G. (2003). Diffusion of innovations, by Everett Rogers (1995). Retrieved January, 21, 2005.
Banister, D. (2008). The sustainable mobility paradigm. Transport policy, 15(2), 73-80.
Geels, F., Kemp, R., Dudley, G., & Lyons, G. (2011). Automobility in transition?: A socio-technical analysis of sustainable transport.
Jha, K., Srivastava, S., & Jain, A. (2023, March). Integrating Global and Local Features for Efficient Face Identification Using Deep CNN Classifier. In 2023 International Conference on Device Intelligence, Computing and Communication Technologies,(DICCT) (pp. 532-536). IEEE.
Jha, K., Srivastava, S., & Jain, A. (2024). A Novel Texture based Approach for Facial Liveness Detection and Authentication using Deep Learning Classifier. International Journal of Computational and Experimental Science and Engineering, 10(3);323-331. https://doi.org/10.22399/ijcesen.369
Gärling, T., & Schuitema, G. (2007). Travel demand management targeting reduced private car use: effectiveness, public acceptability and political feasibility. Journal of social issues, 63(1), 139-153.
Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (4th ed., pp. 151–192). McGraw-Hill.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
