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Electric vehicle adoption (EVA) drives sustainability by significantly reducing carbon emissions and reliance on fossil fuels. Despite EVA’s notable advantages from existing literature and its evolving nature, a gap persists in evaluating EVA research. This research presents a systematic literature review, offering insights into the current state of EVA advancements.

This study amalgamates various factors influencing EVA and elucidates their associations, fostering sustainable transportation. To evaluate progress in this domain, we adopt the Theory-Context-Characteristics-Methodology (TCCM) framework, systematically assessing the theories, contextual factors, characteristics and methodologies employed in EVA research to support efficient decision-making.

The study reveals 18 theories, prominently including the theory of planned behavior, innovation diffusion theory, technology acceptance model and UTAUT. The study identifies diverse factors such as perceived risk, effort expectancy, social norms, performance expectancy, government policy, personal norms, attitude, perceived behavioral control, subjective norms, demographics and ecological knowledge as pivotal in shaping attitudes and intentions toward electric vehicle adoption. Furthermore, structured equation modeling emerges as the predominant methodology, while including alternative approaches enriches the methodological landscape, contributing to a more comprehensive understanding of the factors driving EV adoption.

The insights gained from this research can inform policymakers, manufacturers and researchers, ultimately contributing to the global transition towards more sustainable transportation solutions.

This research’s cardinal contribution lies in developing an integrated theoretical framework, a novel approach that offers a structured and holistic perspective on the multifaceted determinants of EVA. This framework not only illuminates the intricate relationships among these variables but also opens up exciting avenues for future research.

This study aims to explore tourist perceptions and behaviors toward electric vehicles (EVs) in the Delhi National Capital Region using the technology continuance theory.

An online survey involving 226 respondents uses structural equation modeling to analyze correlations among factors, including perceived enjoyment, facilitating conditions, ease of use, satisfaction, cost, image and performance.

This study reveals that enhancing perceived enjoyment and facilitating conditions can improve the user-friendliness of EVs. Additionally, reducing perceived cost, enhancing image and improving perceived performance can increase the perceived usefulness of EVs. Perceived ease of use strongly influences user satisfaction, while perceived usefulness and satisfaction positively impact users’ attitudes and intentions to use EVs. Although factors such as experience, environmental consciousness, age and gender influence perceptions, focusing on enjoyment, facilitating conditions, cost, image and performance can significantly enhance user satisfaction and intention to use EVs.

The findings underscore several actionable recommendations for businesses and policymakers to boost EV adoption at tourist destinations. The potential benefits of EV adoption, such as improved environmental sustainability, enhanced technological image and increased tourist satisfaction, can serve as a source of inspiration and motivation. Enhancing user experience by prioritizing comfort and convenience in EV design is crucial. Addressing cost concerns through incentives and cost-effective pricing strategies can make EVs more appealing. Marketing campaigns highlighting environmental benefits and technological advancements can improve EV image and performance perception. Prioritizing tourists’ satisfaction and support services is essential, along with educational campaigns to increase awareness. Infrastructure development, including expanding charging networks, and supportive policies like tax incentives, can further encourage EVs adoption, accelerating the transition to sustainable transportation.

This research contributes to understanding tourist perspectives on EV adoption within the context of sustainable tourism and technology adoption.

This research aims to identify and prioritize the circular economy (CE) benefits (CEBs) due to the adoption of CE enablers (CEEs) in the Indian manufacturing organization context.

This research proposes a hybrid framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy TODIM (an acronym in Portuguese for Interactive Multicriteria Decision-Making) techniques. It identifies the CEEs and CEBs based on literature review and validated through industrial experts. Further, this research conducts an empirical case study to demonstrate the applicability of the proposed framework.

The result shows that CE enabler SE1 (clear vision, support and commitment from top management for CE adoption) is the most critical enabler for CE implementation. The CE benefit CEB1 (improves the value chain of products and mitigating environmental damage during product life cycle phase) is the most significant benefit derived from the adoption of CEEs. The proposed framework will provide a more accurate, structural and systematic approach to the business organizations for achieving the CEBs in a stepwise manner through the effective adoption of CEEs.

The findings of this research are nation-specific and based on a case study of single manufacturing industry. Thus, the result obtained can vary from case to case and nation to nation.

A deep understanding of each CEEs and CEBs would help build confidence among decision-makers and industrial practitioners to eliminate the risks associated with CE implementation.

Part quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts.

The proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part.

The classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

A real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

The purpose of this paper is to examine the risk perception of project sponsors in financing of public–private partnership (PPP) infrastructure projects in India.

The methodology used is survey questionnaire that seeks the perception of risk managers in PPP projects. Rating and relative ranking of risk at various phases of PPP project have been analyzed and supplemented by unstructured interviews.

This paper shows that the perception of project sponsors for various levels of project risk categories differ significantly in PPP infrastructure projects. The practices of assessing risk and handling differ among the financing institutions. The ranking of risks shows a disagreement among respondents for relative importance. The project financiers that include major banks and financial institutions funding for the PPP infrastructure projects perceive risks differently, and their disagreement on the relative importance of risks may create a sub-optimality in risk management, and the essence of project sponsorship may be lost.

This paper examines the perceptions of the various risks involved in PPP infrastructure project financing. The authors emphasize on the infrastructure projects in the transportation and energy sector that are undertaken in the PPPs. This research can further be extended to the other infrastructure sectors such as roads, shipping and communication.

Experiences reveal that risk perception profoundly influence the implementation of infrastructure projects involving PPPs. To ensure smooth implementation and success of PPP infrastructure projects, the project sponsors must align, synchronize and develop consensus on the various funding and non-funding risks into the project curriculum.

The PPP infrastructure projects carry huge investment and are of strategic importance to the nation and society. In order that the provision of infrastructure which can be most economically and efficiently delivered through PPPs, the risk concordance assumes crucial importance.

The authors believe that this research may provide new direction to the visible and invisible misbalances in risk postures of project partners, which has been a cause of concern to the government and policymakers in India in the recent times.