The transport sector is the leading source of growing greenhouse gas (GHG) emissions globally. To consider environmental degradation aspects due to transport, electric vehicles (EVs) have the prospect to lead road transport to electric mobility from conventional petroleum vehicles. Despite various eco-friendly benefits, the EV market penetration ratio is very low, especially in developing countries. The primary reason for low penetration is consumer limited motivation and knowledge about the EVs features. This paper uses a unified theory of acceptance and technology (UTAUT) model to assess consumer motivation and environmental knowledge towards EVs. This research used convenience random sampling to collect data and analyzed the results using the Partial Least Squares (PLS) method on the example of 199 respondents from Malaysia. The study results revealed that factors identified in the motivational context significantly influence consumer intentions to purchase EVs. Perceived environmental knowledge and technophilia have been included in UTAUT from a motivational perspective. Furthermore, a significant relationship between effort expectancy, social influence, technophilia, perceived environmental knowledge, and purchase intention towards electric vehicles has been observed, without performance expectancy. The study findings serve to inform policymakers and automakers to formulate effective marketing strategies to enhance consumer motivation, knowledge, and value creation for EVs in a sustainable era. Ultimately, the policies will help to encourage consumers to buy eco-friendly vehicles that will help reduce transport carbon emissions and attain sustainable development goals (SDGs).

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.

Since December 2019, the COVID-19 epidemic has been spreading all over the world. This epidemic has brought a risk of death in the daily activity (physical and social) participation that influences travellers’ physical, social, and mental health. To analyze the impact of the COVID-19-induced daily activities on health parameters of higher education institutes, 150 students of the Universiti Teknologi PETRONAS, Perak, Malaysia, were surveyed through an online web survey using random sampling techniques. The data were analyzed through RStudio and SPSS using multilevel linear regression analysis and Hierarchical Structural Equation Modeling. The estimated results indicate that restricting individuals from doing out-of-home activities negatively influences physical and social health. A unit increase in the in-home maintenance activities during the COVID-19 pandemic introduced a daily increase of 0.5% in physical health. Moreover, a unit increase in the in-home activities at leisure time represents a 1% positive improvement in social health. Thus, physical activity has proven to be beneficial in improving physical and social health with severe COVID-19. In contrast, the coefficient of determination (R²) for all endogenous variables ranges from 0.148 to 0.227, which is incredibly acceptable in psychological research. For a healthier society with a better quality of life, this study adopted multidisciplinary approaches that are needed to be designed.

It is acknowledged that quality of life is related to economic development in a country or region. The impact of the ongoing transfer to the digital economy on economic growth, sustainable development, and quality of life has been a recent academic focus, finding that the development of digitalisation of the economy and society is happening at a comparable pace. Yet, despite the availability of governmental support and overall strategic orientation of top managers, there is evidence of low digital/advanced manufacturing technologies adoption readiness across economic sectors. Therefore, the object of the research is the digital/advanced manufacturing technologies adoption readiness, which can be assessed on both industrial and organisational levels. The authors carried out an expert poll that has shown low digital/advanced manufacturing technologies adoption readiness on the industrial level (average readiness of 2.18 across estimated industries). For this reason, the focus subject of the research is the evaluation method for determining barriers of digital transformation on organisational level. The review of the academic literature and available digital maturity assessment models has shown the lack of metrics for barrier evaluation. The objective of the research is to develop a method for digital-transformation barrier evaluation. An analytical research method is used to develop a ready-to-use method in a form of a questionnaire that can be applied to measure an overall digital-transformation barrier level and identify problem areas for a manufacturing enterprise. The developed method is approbated in two large industrial companies. The overall barrier equals 39.3% for a power engineering company, which is a deterrent barrier. The overall barrier equals 75.0% for a machine-building company, which is a limiting barrier. The results of approbation correspond with results of the expert poll: the power engineering company has a lower barrier level, which is in line with the engine building and turbine engineering industry showing higher levels of readiness to adopt digital technologies. This finding supports the hypothesis about the rationality of the author’s method.

А group of authors of Peter the Great St. Petersburg Polytechnic University, working within the framework of the activity of Coordination Council for the Federal Educational and Methodical Associations on education in the field of “Engineering, Technology and Technical Sciences” have undertaken a comprehensive study dedicated to the processes of the digital economy formation. More than two hundred primary sources of reference have been analyzed. Several main groups of risks associated with the transition to global digitalization have been defined and classified. Further research results described in this article allow to characterize the specific features of the occurrence of risks in the Russian Federation and to determine the ways of reducing these risks.In addition to the six groups of risks which are currently possible to arise and which are characteristic of the entire world space, the authors have revealed a number of several additional risks typical only of Russia. One of the key areas in Russia that poses a whole range of various risks is the education system. The authors have analyzed and classified the policies and moves suggested by the researchers and politicians to reduce the likelihood of these specific risks occurrence.The qualitative leap of the educational process in Russia is possible only by means of the formation of some new competence profiles of educational institutions graduates with reference to the digital economy development conditions. The authors have defined seven types of competencies that are relevant for the transition of Russia to the digital economy and developed twelve pilot educational modules necessary for their formation.

The object of research is the potential application of digital twins and neural network modeling for optimizing construction processes. Method. Adopting a perspective approach, the research conducts an extensive review of existing literature and delineates a theoretical framework for integrating digital twins and neural network modeling technologies. Insights from the literature review inform the development of methodologies, while case studies and practical applications are explored to deepen understanding of these integrated approaches to system construction optimization. Results. The review yields the following key findings: Digital Twins: Offer the capability to create high-fidelity virtual representations of physical construction systems, enabling real-time data collection, analysis, and visualization throughout the project lifecycle. This allows for proactive decision-making, improved constructability analysis, and enhanced coordination between design and field operations. Neural Network Modeling: Possesses the power to learn complex relationships from vast datasets, enabling predictive modeling and optimization of construction system behavior. Neural networks can be employed to forecast project timelines, identify potential risks, and optimize construction scheduling and resource allocation. Integration of Digital Twins and Neural Networks: Presents a transformative avenue for optimizing construction processes by facilitating data-driven design, predictive maintenance of equipment and infrastructure, and real-time performance monitoring. This synergistic approach can lead to significant improvements in construction efficiency, reduced project costs, and enhanced overall project quality.