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This paper examines the role of a sustainability business model in clarifying a firm's sustainability value articulation (SVA) to achieve sustainability outcomes and examining the moderating role of supplier involvement practices SIP and technology systems integration (TSI) in attaining sustainability outcomes.

Drawing upon the foundational principles of business model innovation, specifically articulation and implementation, the authors formulated a theoretical construct and empirically validate it through analysis of data collected from 692 manufacturing firms dispersed across 23 countries.

The research shows that focusing on SVA significantly improves how a company implements sustainability efforts internally (ISI) and externally (ESI), leading to better social and environmental outcomes. It also highlights that SIP improve the relationship between SVA, ESI and ISI. Similarly, TSI boosts the effect of internal and external sustainability efforts on both social (SOP) and environmental performance (EnP).

While acknowledging the inherent constraints of survey-based research methodologies, this study offers a theoretical and verified approach for manufacturers to achieve comprehensive sustainability. It emphasizes the need for clear, actionable sustainability goals that can be met through both internal operations and external partnerships.

This study clarifies how manufacturers can implement sustainable business models, emphasizing the importance of clear sustainability goals and initiatives both within and outside the company. It highlights the dual aspects of supplier engagement through operational tactics (ESI) and strategic collaborations (SIP).

This study reveals a thrilling truth: when companies champion clear sustainability goals, they unlock powerful strategies that revolutionize practices within their walls and in their external dealings. It is not just about going green; it is about weaving financial prosperity, social responsibility and environmental stewardship into the very fabric of their business models. But there is more – by cleverly engaging suppliers and harnessing cutting-edge technology, companies are not just participants in the green revolution; they are leading it, crafting a world where business thrives alongside the planet and its people.

This research stands out for its empirical analysis of how manufacturing firms implement sustainability innovations at the plant level, an area previously underexplored despite extensive theoretical work on sustainability-centric business models.

For decades, the fast population growth worldwide was interrelated with the adopted rapid lifestyle behavior that relies on the extensive use of fossil fuels. This primary energy source has caused various urban and environmental impacts, such as global warming, air pollution, and so forth. Consequently, the identified circumstance issues have caused many health, social, and economic hindering effects for global citizens. It poses an existential threat to humanity and the global earth's ecosystem. The alarming levels of urban pollution emissions are putting enormous challenges to the related stakeholders (governments, businesses, investors, automakers, and citizens) to admit the need to decarbonize the global economy and reach sustainable development goals (SDGs) for cleaner and smarter cities across borders. As such, a vital part of a smart city is the transport sector. The road transport sector, precisely, is one of the primary consumers of fossil fuels that contribute to high carbon dioxide emissions. Accordingly, it is essential to adopt novel and sustainable urban transport solutions and promote the achievement of the SDG's eleventh goal for sustainable cities and communities. This chapter provides insight into the present global energy situation with particular attention to the road transport sector. Indeed, it highlights different emerging technologies for a sustainable and smart urban mobility future that will mitigate the environmental situation thanks to the development of drive and internet telecommunication technologies. Furthermore, we aim in this chapter to study the international progress of the transition project using the Political, Economic, Social, Technological, Environmental, and Legal (PESTEL) analysis methodology. This study is to pinpoint opportunities for project development and the challenges that set back its evolution.

The current booming development of smart cities poses new requirements and challenges for their internal infrastructure development. This article aims to explore the questions of: What is the level of social sustainability of smart city infrastructure today? and What are the core contents and paths to improve this level?

With the theme of public participation in the social sustainability evaluation of smart city infrastructure in the context of big data, this study mainly makes a systematic literature review of the Web of Science's Science Citation Index Expanded and Social Sciences Citation Index databases. After collection and screening, 199 documents were finally obtained.

It is found that the level of social sustainability of smart city infrastructure is still low, and public participation can provide solutions to the difficulties and challenges involved in its development, while big data technology can broaden the channels for public participation and promote the development of smart city-related components in the process, including smart city infrastructure.

This article summarizes the internal mechanisms of smart cities at the theoretical level and analyzes the social sustainable development of smart city infrastructure. In practice, the shortcomings in this field are identified and suggestions are provided on how to carry out digital public participation, which has practical reference value.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

This study systematically maps the research trends in the domain of “shadow education” over the last 40 years using metadata extracted from the SCOPUS database. The results reveal that the outputs of shadow education research have grown exponentially within the last decade. Bray and his colleagues from the University of Hong Kong, East China Normal University, and the Education University of Hong Kong have been the most prolific and influential research team. They are followed by Park and Byun from the USA, who have mostly worked on East Asian contexts. The USA, Hong Kong, South Korea, and the People’s Republic of China, have been the main sources of contributions and the University of Hong Kong has been the leading university in this field. Educational studies, economics, psychology, linguistics, and sociology have been the main disciplines researched within shadow education. Shadow education studies have revealed how shadow education can be a major instrument for maintaining and exacerbating social inequalities. They have also largely focused on the tangible (quantifiable) benefits related to improving students’ examination results. This study’s results stress the importance of regulating the private tutoring market, suggesting areas for ongoing research.

This study aims to explore the impact of carbon neutral announcements on “stock market value” of publicly listed companies in China.

The event study approach is adopted. Market, market-adjusted, Carhart four-factor model and a cross-sectional regression model are employed to examine the impacts of carbon neutral announcements on “stock market value” of Chinese companies based on data from 188 carbon neutral announcements.

Carbon neutral announcements positively impact Chinese shareholder value. Carbon neutral announcements at the strategic level have a more positive and significant impact on Chinese stock market value. Innovative carbon neutral announcements do not significantly cause Chinese stock market reactions. Companies have more positive and significant stock market reactions when the companies make carbon neutral announcements that reflect high supply chain network resilience and heterogeneity and strong supply chain network relationships.

The findings uncover the business value of carbon neutral activities and provide operations managers in developing countries insights into how to improve enterprises' market value by actively implementing carbon neutral activities.

This paper is the first trial to apply an event study to examine the relationship between carbon neutral announcements and Chinese stock market value from the perspective of announcement level and type and supply chain networks. This paper introduces corporate reputation theory and enriches the application of corporate reputation theory in the field of low-carbon environmental protections and supply chains.

The study aims to determine the outcomes of mentorship in an online game system, as well as the characteristics of good mentors.

A combination of anonymized survey measures and in-game behavioral measures were used to power longitudinal analysis over an 11-month period in which protégés and non-mentored new players could be compared for their performance, social connections and retention.

Successful people were more likely to mentor others, and mentors increased protégés' skill. Protégés had significantly better retention, were more active and much more successful as players than non-protégés. Contrary to expectations, younger, less wealthy and educated people were more likely to be mentors and mentors did not transfer their longevity. Many of the qualities of the mentor remain largely irrelevant—what mattered most was the time spent together.

This is a study of an online game, which has unknown generalizability to other games and to offline settings.

The results show that getting mentors to spend dedicated time with protégés matters more than their characteristics.

Good mentorship does not require age or resources to provide real benefits.

This is the first study of mentorship to use survey and objective outcome measures together, over time, online.

The study aims to pioneer an innovative approach for the evaluation of government portal websites (GPWs) by introducing an eye-tracking-based method. The research meticulously pinpoints and analyses the distinct usability issues and challenges that users encounter while navigating and interacting with GPWs.

This study devises an eye-tracking-based GPW usability evaluation approach, which focuses on the major functions (i.e. government information disclosure, government services and interactive responses) of GPWs. An Entropy Weighted Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS) method is employed to process eye-tracking indicator results for deriving GPW usability results.

The proposed approach is demonstrated to assess the usability of 12 GPWs in pilot smart cities in China, and it is found that most GPWs have lower-than-average usability. GPWs with low usability require more cognitive load that exhibit increased fixation and saccade. The comparisons among the GPW usability results from (1) the eye-tracking experiment, (2) questionnaire surveys and (3) the ready-made performance evaluation report validate the effectiveness of eye-tracking-based GPW usability evaluation.

The work contributes to shifting the GPW usability evaluation approach from a subjective judgment paradigm to an objective paradigm, as well as provides implications for enhancing GPW usability, including improving search function, reducing website complexity and prioritizing user needs.