Analysing the Impact of Social Influence on Electric Vehicle Adoption: A Deep Learning-Based Simulation Study in Jharkhand, India

Abstract

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.