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Blockchains used by e-commerce consortia are a novel form of governance that facilitates coordination and collaboration among the numerous organisations that comprise e-commerce supply chains. Despite the increasing prevalence of consortium blockchain networks for e-commerce, there is a limited understanding of the economic and social dynamics that influence the behaviour of blockchain consortium members. By utilising transaction cost theory and social exchange theory, this research investigates the interplay between blockchain transaction-specific investment (BTSI), trust, adaptive collaboration (ADC) and the overall performance of supply chains in consortium blockchains

A quantitative research approach was employed to collect data from a representative sample of blockchain organisations affiliated with e-commerce consortium blockchains worldwide. Following this, the data obtained from 361 participants were analysed using descriptive and inferential statistics.

The results of our study indicate that BTSI has a substantial impact on trust. Furthermore, trust plays a pivotal role in shaping ADC, and ADC, in turn, acts as a mediator in the relationship between trust and performance outcomes.

This study underlines these economic and social dynamics in the evolving context of consortium blockchain networks, offering insights into their significance within a technology-driven environment.

Unverified information avoidance behavior refers to the conscious effort made by individuals to avoid consuming information that has not been verified by credible sources. This behavior is essential in preventing the spread of misinformation that can hinder effective public health responses. While previous studies have examined information avoidance behavior in general, there is a lack of research specifically focusing on the avoidance of unverified information during health crises. This study aims to fill this gap by exploring factors that lead to social media users’ unverified information avoidance behavior during health crises, providing novel insights into the determinants of this protective behavior.

We based our research model on the health belief model and validated it using data collected from 424 individuals who use social media. The proposed model was tested by using the partial least squares structural equation modeling (PLS-SEM) approach.

Our results indicate that individuals’ government social media participation (following accounts and joining groups) affects their health beliefs (perceived severity and benefits of information avoidance), which in turn trigger their unverified information avoidance behavior.

Our study contributes to the current literature of social media crisis management and information avoidance behavior. The implications of these findings for policymakers, social media platforms and theory are further discussed.

This chapter highlights the key areas for the leadership and innovation research agenda in 2035. This agenda will direct researchers’ focus to the core transversal skills that individuals must have amidst the shift toward a greener and digitalized economy. Such skills include leadership, management, creativity, communication, and adaptability. The role of macro governmental policies and micro organizational policies is of great significance to ensure the implementation (if any) of these changes and of core to the research agenda. This chapter will also guide researchers to the challenges at the higher education level that need to be addressed to ensure the balance between the skills and knowledge acquired by workers through education and the needs of businesses in order to increase productivity and innovation.

This study aims to develop an electrochemical sensor for the detection of benzophenone (BP) as an alternative to conventional techniques that are known, expensive, complex and less sensitive.

The developed sensor is a platinum electrode modified with a plasticized polymer film based on ß-cyclodextrin, using PVC as the polymer, PEG as the plasticizer and ß-CD as the ionophore. This sensor is characterized by various techniques, such as optical microscopy, scanning electron microscopy and cyclic voltammetry. This latter is also used for analyzing kinetic processes at the electrode/electrolyte interface and to evaluate the selectivity and sensitivity of the sensor.

The results highlight the performance of our sensor. In fact, it exhibits a linear response extending from 10⁻¹⁹ to 10⁻¹³ M, with a correlation coefficient of 0.9836. What is more, it has an excellent detection limit of 10⁻¹⁹ M and a good sensitivity of 21.24 µA/M.

The results of this investigation demonstrated that the developed sensor is an analytical tool of choice for the monitoring of BP in the aqueous phase. The suggested sensor is fast, simple, reproducible and inexpensive.

Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.

Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.

We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.

Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.

This paper aims to present an overview of permanent magnet linear flux-switching machines (PMLFSM), field excited LFSM and hybrid excited LFSM (HELFSM) topologies as presented in literature for transportation systems such as high-speed trains and maglev systems.

The structural designs of different configurations are thoroughly investigated, and their respective advantages and disadvantages are examined. Based on the geometry and excitation sources, a detailed survey is carried out. Specific design and space issues, such as solid and modular structures, structure strength, excitation sources placement, utilization of PM materials, and flux leakage are investigated.

PMLFSM provide higher power density and efficiency than induction and DC machines because of the superior excitation capability of PMs. The cost of rare-earth PMs has risen sharply in the past few decades because of their frequent use, so the manufacturing cost of PMLFSM is increasing. Owing to the influence of high-energy PMs and magnetic flux concentration, the efficiency and power density are higher in such machines. PM is the only excitation source in PMLFSM and has constant remanence, limiting its applications in a wide speed operation range. Therefore, the field winding is added in the PMLFSM to flexibly regulate the magnetic field, making it a hybrid excited one. The HELFSM possess better flux linkage, high thrust force density and better flux controlling ability, leading to a wide speed range. However, the HELFSM have problems with the crowded mover, as PM, field excited and armature excitation are housed on a short mover. So, for better performance, the area of each excitation component has to compete with each other.

Transportation of goods and people by vehicles is becoming increasingly prevalent. As railways play a significant role in the transportation system and are an integral part of intercity transportation. So, this paper presents an overview of various linear machines that are presented in literature for rail transit systems to promote sustainable urban planning practices.

This study aims to use geographical information on social media for public opinion risk identification during a crisis.

This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.

In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.

Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

This study examines the effects and driving mechanisms of relational bonds (i.e. financial, social, and structural bonds), live-streaming shopping atmosphere factors (i.e. suspense, entertainment, perceived crowdedness, and vicarious experience), consumer empowerment and customer commitment on consumers’ impulse consumption behavior. Additionally, the study examines the moderating influence of product involvement and collectivism.

An online survey was conducted with 665 valid respondents. The authors empirically validated the collected data through the partial least squares structural equation modeling (PLS-SEM) technique, complemented by the artificial neural network (ANN) analysis.

The results suggest that financial bonds, structural bonds, suspense, entertainment, and vicarious experience promote consumer empowerment, which in turn leads to customer commitment and impulse consumption behavior. Second, collectivism moderates the relationship between customer commitment and impulse consumption behavior.

This study provides empirical evidence that relational bonds and live-streaming shopping atmosphere factors play predictive roles in enhancing consumer empowerment, which further promotes impulse consumption behavior through customer commitment. Also, collectivism is found as a moderator.

Retail investors are prone to be affected by information dissemination in social media with the rapid development of Web 2.0. The purpose of this study is to recognize the factors that may impact users' retweet behavior, namely information dissemination in the online financial community, through machine learning techniques.

This paper crawled data from the Chinese online financial community (Xueqiu.com) and extracted author-related, content-related, situation-related, stock-related and stock market-related features from the dataset. The best information dissemination prediction model based on these features was determined by evaluating five classifiers with various performance metrics, and the predictability of different feature groups was tested.

Five prevalent classifiers were evaluated with various performance metrics and the random forest classifier was proven to be the best retweet prediction model in the authors’ experiments. Moreover, the predictability of author-related, content-related and market-related features was illustrated to be relatively better than that of the other two feature groups. Several particularly important features, such as the author's followers and the rise and fall of the stock index, were recognized in this paper at last.

This study contributes to in-depth research on information dissemination in the financial domain. The findings of this study have important practical implications for government regulators to supervise public opinion in the financial market.

This study proposes a theoretical model to explain user intention to continue engaging with Social Commerce (SC) from a habit perspective. The research uses social impact theory, user personal traits, and SC quality to explain how user habits are formed in SC, leading to continued usage.

The study collected data from 868 Indonesian respondents using a cross-sectional field design. SEM analysis confirmed the proposed theoretical model, calculating direct, indirect, and moderating effects.

The results showed that the social aspect is the dominant construct influencing users’ habit of using SC. Further, the outcome indicates that habit significantly predicts Continuity usage. Profoundly, subjective norms are the most significant predictors of habit, followed by self-efficacy, content quality, and herd behavior. Meanwhile, Trust and Social Support only indirectly affect Habit through self-efficacy and content quality, respectively, as mediators. Additionally, the moderating effect analysis revealed that age and gender play a role in habit formation.

This study specifically explores the factors affecting the development of habits in SC usage, leading to repeated behaviors. This area has not been thoroughly examined in previous research. Therefore, this study seeks to address this gap by investigating how habits are formed through social interactions on SC platforms. Understanding habit formation provides an alternative way of comprehending the continued use of SC, as it is considered a significant factor that leads to continued intention.