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The Indian government has set an ambitious target for reducing the import of fossil fuels by 10% and introducing an all-electric car fleet by 2030. The Government of India launched the National Electric Mobility Mission Plan (NEMMP) 2020 in 2013 to promote Electric Vehicles (EVs) in India with the objective of providing incentives for use of EVs; encouraging research & development in the areas of battery technology, system integration, testing infrastructure; and promoting charging infrastructure. The Indian government is also working on a scheme by which an electric car can be purchased free of cost: zero down payments, and monthly payments out of savings on the cost of petrol. It is envisaged that sooner or later, e-vehicles will transform the automobile market and provide environmental sustainability to the society. Political stability to provide stable policies is expected to play a key role in driving the growth of such vehicles. So far, preliminary research has been undertaken on perception of Indian Society on EVs. Based on empirical research, this paper attempts to address the gap. A study was conducted from November 2016 to April 2017 in Delhi-NCR with a sample size of 220 professionals working in manufacturing and service industry to understand the upcoming green transport facilities and their perceived environmental benefits as perceived by the residents of the society. Convenience sampling was used to collect the data. The Study highlighted that the design and utility of the EVs need to be reshaped so that it can compete with the gasoline vehicles in the current environment. Almost 95% of the respondents are ready to pay a premium for new technology or EVs. The study revealed that infusion of capital support and government subsidies can play a key role in acquiring new customers and establishing the market for EVs in the Indian market. The results show that there is a need to enhance awareness of NEMMP scheme within the society so that the EV market share can be increased. The results highlight that with availability of options, society will use the transport system which is environment friendly.

Do social movement organizations increase the supply of a public good? We address this question by investigating the role of generalist social movement organizations and technology-focused organizations for the development of the electric vehicle (EV) charging infrastructure in California from 1995 until 2012. We find that increases in the membership of Electric Auto Association (EAA) chapters in the cities of California enhanced the number of EV charging stations set up in each city. Our analyses also show that the organizational diversity of the environmental movement spurred the growth of EAA membership but did not directly increase the establishment of charging stations.

Purpose of This Chapter: This chapter aims to clarify how employee voice (EV) is conceptualized in the extant literature. Then, the present chapter develops a model of EV based on previous studies from 1970 to 2022.

Design / Methodology / Approach: In all, 53 articles were investigated in this study – the focus was on articles in the field of management. The target articles were limited to those which were written in English and were indexed by the Web of Science (WOS).

Findings: The concepts related to EV have been presented in five main dimensions. The content analysis results revealed a theoretical agreement over the central elements reflecting an integrated description of EV.

Research Limitations: The study was limited to the sources published in the WOS database, which could affect the scope of findings.

Research Implications: The conclusion showed that EV is a multi-dimensional, emerging field that has been undergoing a definitional debate in the literature. This concept indicates an ongoing responsibility for the public’s benefit, welcomes different financial activities to supply market forces’ requirements, and investigates society’s existing demands.

Originality: It represents the first attempt to conduct an EV systematic literature review based on the definition of key factors and the complex relations in a typical EV process. Also, according to the review of selected articles from 1970 to 2022, there has been no coherent conceptualization of employees’ voices.

Aim/Purpose: This chapter supplies an overview of extant literature investigating consumer preferences and purchase behaviour towards Electric vehicles in India. The present research applied the predictive and prescriptive analysis methods to understand the consumer purchase intentions evolved with the advancement of technologies. The study considers the security and privacy of data for companies and consumers.

Design/Methodology/Approach: The conceptual model is analysed via path analysis using interviews with a set of people, and online survey data is collected from Indian users and prospects. Secondary data research methods are used to make interpretations.

Findings: The research found that consumer preferences and purchase behaviour about electric vehicles have a wide scope in marketing decision-making. The findings support linking social cognitive perception and attitude intent with mitigating socio-demographic variables and mediating attitudes towards Battery Electric Vehicles (BEV).

Research limitations/implications: The present chapter provides implications for future interdisciplinary research addressing consumer behaviour with mentioned technologies. This study proposes some theoretical and policy implications and supplies guidance to industry-specific consumers, suppliers, policymakers and business professionals to encourage the adoption of BEVs in the emerging transportation industry. The research explores the future possibility and extent to which the consumer prefers and buys EVs.

Originality/Value: This study is based on the ‘perception-attitude-intention’ socio-psychological linkage framework. Earlier studies overlooked the effects of socio-psychological characteristics and socio-demographic moderators when considering the adoption of BEVs.

Despite efforts to reduce environmental pollution and wasteful fossil fuel use, electric vehicles (EVs) are still rare on the road. Why is it so challenging to get widespread EV adoption? One significant factor on which it heavily depends is one's awareness and understanding of EVs. However, due to an absolute lack of knowledge on the part of the populace, this factor becomes a huge impediment to the uptake of EVs. A systematic review of the electronic database Scopus for the years 2003–2022 was carried out on ‘EV awareness and adoption of EV’ while considering the ‘Preferred Reporting Items for Systematic Reviews and Meta-analysis’ (PRISMA) standards. A three-step identification process resulted in the ultimate detection of 41 papers, which were then thoroughly examined. A conceptual framework that encompasses the three key awareness aspects that influence EV adoption is developed. To encourage greater uniformity among EV researchers, this study's conclusions serve as a foundation for operationalising upcoming research efforts within a predetermined framework. The authors must therefore be optimistic that lingering technological, legislative, cultural, behavioural and business-model barriers may be overcome over time through widespread dissemination of knowledge and awareness related to EVs, making it possible for everyone to switch to greener, more economical and more efficient transportation solutions.

The study investigates the effects of energy resource efficiency on the triple themes of sustainable development (economic, social and environmental dimensions). We adopt a quantitative research method, and the required macroeconomic data were extracted from World Development Indicators for a period of 30 years (1991–2020). The extracted data were analysed using correlation analysis and linear regression. Ultimately, the estimations from the three models produced mixed results. Energy resource efficiency (EFF) exerts a significant positive effect on economic sustainability (ECS), a significant negative effect on social sustainability (SOS) and a significant negative effect on environmental sustainability (EVS). However, claims on government (COG) exerted an insignificant negative effect on ECS, an insignificant negative effect on SOS and a significant positive effect on environmental sustainability (EVS). In practical terms, the findings are consistent with previous empirical studies, and they also validate X-efficiency theory (XET) and resource curse theory (RCT). The study concludes with implications, limitations and further research directions.

This chapter argues that the primary reason for the underdeveloped state of microfoundations research in language and communications studies is methodological. It asserts that the long-standing methodological division between micro and macro analyses (traditionally small-scale and large-scale, respectively) has led to their continued theoretical separation. The paper draws from Giddens’ theory of structuration and newly developed computational methods to outline an alternative, mixed-methods framework for discourse analysis that the author calls Recursive Analysis (RA). The author demonstrates the application of the RA framework through a case study of the electric vehicle industry that aligns small-scale and large-scale textual analysis to generate theoretical insights.

Facial expression recognition by human observers is affected by subjective components. Indeed there is no ground truth. We have developed Discrete Choice Models (DCM) to capture the human perception of facial expressions. In a first step, the static case is treated, that is modelling perception of facial images. Image information is extracted using a computer vision tool called Active Appearance Model (AAM). DCMs attributes are based on the Facial Action Coding System (FACS), Expression Descriptive Units (EDUs) and outputs of AAM. Some behavioural data have been collected using an Internet survey, where respondents are asked to label facial images from the Cohn–Kanade database with expressions. Different models were estimated by likelihood maximization using the obtained data. In a second step, the proposed static discrete choice framework is extended to the dynamic case, which considers facial video instead of images. The model theory is described and another Internet survey is currently conducted in order to obtain expressions labels on videos. In this second Internet survey, videos come from the Cohn–Kanade database and the Facial Expressions and Emotions Database (FEED).

This chapter explores how technology availability and costs influence public opinion, vehicle ownership decisions, travel, and location choices. Attitudes towards electric vehicles (EVs) are considered within the broader context of other linked technological trends affecting automobility, with a particular focus on the shift to (electric powertrain) autonomous vehicles (AVs).

This chapter draws upon modelling of quantitative survey data from 1,426 Americans, which employed regression analysis to predict and understand variables linked to the preference for an AV over a human driver, percentage of trips taken by an AV, percentage of trips using dynamic ride-sharing (DRS) inside a shared autonomous vehicle (SAV), and factors affecting EV charging access in home and at work/school.

The findings show that full EV charging times significantly affect decisions for next household vehicle purchase. A lack of charging ability at home appears to be a significantly greater hindrance to respondents’ willingness to purchase full EVs than does a lack of charging ability at work. And home location choice impacts of AVs are not expected to be substantial. Considering future EV/AV ride-sharing (an important component of sustainable future mobility systems), DRS may ease congestion if SAV riders widely adopt DRS for work and school trips; however, sharing with strangers may not be popular in practice.

This chapter is useful to manufacturers and fleet operators for pricing and marketing decisions, and public transit authorities/providers can benefit from understanding evolving travel choices and land use patterns to craft equitable policies, and model future transportation demand to help plan services and infrastructure projects.