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This paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.

Taking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.

The results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.

This paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

To contribute to achieving the Sustainable Development Goals, this study aims to explore how to encourage innovative green behaviors among college students and the mechanisms behind the formation of green innovation behavior. Specifically, this study examines the influences of schools, mentors and college students themselves.

A multilevel, multisource study involving 261 students from 51 groups generally supported this study’s predictions.

Proenvironmental and responsible mentors significantly predicted innovative green behavior among college students. In addition, creative motivation mediated the logical chain among green intellectual capital, emotional intelligence and green innovation behavior.

The study findings offer new insights into the conditions required for college students to engage in green innovation. In addition, they provide practical implications for cultivating green innovation among college students.

The authors proposed and tested a multilevel theory based on the ability–motivation–opportunity framework. In this model, proenvironmental and responsible mentors, green intellectual capital and emotional intelligence triggered innovative green behavior among college students through creative motivation.

The composition of author teams is a significant factor affecting the novelty of academic papers. Existing research lacks studies focusing on institutional types and measures of novelty remained at a general level, making it difficult to analyse the types of novelty in papers and to provide a detailed explanation of novelty. This study aims to take the field of natural language processing (NLP) as an example to analyse the relationship between team institutional composition and the fine-grained novelty of academic papers.

Firstly, author teams are categorized into three types: academic institutions, industrial institutions and mixed academic and industrial institutions. Next, the authors extract four types of entities from the full paper: methods, data sets, tools and metric. The novelty of papers is evaluated using entity combination measurement methods. Additionally, pairwise combinations of different types of fine-grained entities are analysed to assess their contributions to novel papers.

The results of the study found that in the field of NLP, for industrial institutions, collaboration with academic institutions has a higher probability of producing novel papers. From the contribution rate of different types of fine-grained knowledge entities, the mixed academic and industrial institutions pay more attention to the novelty of the combination of method indicators, and the industrial institutions pay more attention to the novelty of the combination of method tools.

This paper explores the relationship between the team institutional composition and the novelty of academic papers and reveals the importance of cooperation between industry and academia through fine-grained novelty measurement, which provides key guidance for improving the quality of papers and promoting industry–university–research cooperation.

The purpose of this study is to examine how the interplay of stressor (e.g. fear of missing out, FoMO) and strains (e.g. perceived social overload, communication overload, information overload and system feature overload) in social networking sites (SNS) use can contribute to users’ SNS fatigue from a configurational view.

Data were collected among 363 SNS users in China via an online survey, and fuzzy-set qualitative comparative analysis (fsQCA) was applied in this study to scrutinize the different combinations of FoMO and overload that contribute to the same outcome of SNS fatigue.

Six combinations of casual conditions were identified to underlie SNS fatigue. The results showed that FoMO, perceived information overload and system feature overload are the core conditions that contribute to SNS fatigue when combined with other types of overloads.

The current work supplements the research findings on SNS fatigue by identifying the configurations contributing to SNS fatigue from the joint effects of stressor (FoMO) and strain (perceived social overload, communication overload, information overload and system feature overload) and by providing explanations for SNS fatigue from the configurational perspective.

In Tunisia, foreign commercial exchanges are predominantly maintained via ocean freight and accomplished through eight major ports. The latter play a critical role in the Tunisian economy, whereby nearly 30.7 million tons of goods were transited in 2018. Maintaining their efficiency therefore remains a very important objective to achieve. In this context, the present study is designed to investigate the Tunisian ports respective technical efficiency over the 16-year period (2005–2020).

The stochastic frontier analysis (SFA) method is applied to measure the associated time-invariant and time-varying technical efficiency. Moreover, through technical inefficiency modeling, effects of both rail connectivity and private sector participation in handling activities on technical efficiency have also been accounted for.

The reached results turn out to reveal well that the Tunisian ports appear to operate below their production frontier, noticeably marked by persistent technical inefficiency. Additionally, the relevant estimates tend to confirm the berth variable associated importance in highlighting production related to Tunisian ports. More particularly, our analysis reveals that the private sector’s participation proves to display a significantly negative association with technical efficiency, while the ports’ rail connectivity turns out to demonstrate a significantly positive correlation with technical efficiency.

The findings of this study can provide port authorities and policymakers with insights into the technical efficiency of Tunisian ports by identifying best practices, the main factors influencing their efficiency (such as rail connectivity and private sector’s participation) and areas for improvement in these ports.

The present study stands as a pioneering attempt to examine the efficiency dimension through the implementation of panel data estimation modeling frameworks, particularly the random-effects and the Battese and Coelli (1995) approaches, applied to measure the technical efficiency of the Tunisian port sector. Similarly, the present study also represents an effective attempt, whereby the effects of exogenous variables, notably the rail connectivity and private sector participation, are thoroughly considered in exploring the technical efficiency of Tunisian ports.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

Asia is one of the most important economic regions of the world. The Kra Canal is considered as a potential maritime transportation node in global trade. Indeed, for centuries, a canal across the Kra isthmus in Thailand has been proposed as holding significant economic and logistical benefits through goods transfer and exchange, yet it remains unconstructed. Common tools in feasibility studies are PESTELE and SWOT analyses, which incorporate cost-benefit and value analyses. The purpose of this paper is to assess the feasibility of the Kra Canal using approaches to complement existing studies.

In this paper, we combine quantitative and qualitative methodologies to holistically assess the feasibility of the Kra Canal. Quantitatively, we consider the economic feasibility of the canal using Net Present Value to evaluate its potential financial benefits. Qualitatively, we then present expert perspectives gathered from in-depth interviews with field experts (n = 20) to identify key aspects of complexity related to the economic, political and overall feasibility of the canal.

We discuss the possibility of the canal’s construction and describe possible future impacts the canal could have on the region geopolitically, for example, in its role in China’s One Belt One Road initiative and also in how Singapore and Tanjung Pelapas in Malaysia might react. Ultimately, we find significant complexities with the Kra Canal project, ones which anyone contemplating its construction would, we argue, be wise to consider.

This paper combined qualitative and quantitative methods to explore the economic, political and overall feasibility of the Kra Canal. The methodologies and findings supplement past relevant studies and help evaluate the economic, political and overall feasibility of the Kra Canal’s investment project. This is particularly timely considering its potential ability as a route given recent geopolitical events and, for example, as part of China’s Belt and Road initiative and its ability to reduce carbon and sulphur emissions.

This study aimed to identify factors influencing AI/chatbot usage in education and research, and to evaluate the extent of the impact of these factors.

This study used a mixed approach of qualitative and quantitative methods. It is based on both primary and secondary data. The primary data were collected through an online survey. In total, 177 responses from teachers were included in this study. The collected data were analyzed using a statistical package for the social sciences.

The study revealed that the significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations.

This study highlighted the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and developing strategies for the efficient adoption of AI/interactive tools in academia and research to enhance the overall quality of learning experiences.

The present study adds value to the existing literature on AI/interactive tool adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, it also indirectly helps achieve various UNSDGs, such as 4, 9, 10 and 17.

The unique nature, complicated design, hazardous activities and complex work environment involved in the high-rise construction projects constitute significant risks worldwide. In the Indian context, construction safety management in high-rise construction projects is crucial due to the presence of significant occupational risks and hazards at the workplace. Occupational hazards lead to accidents that severely affect human health and result in substantial financial losses.

The study aims to present a hybrid risk assessment method (RAM) and the technique for order of preference by similarity to ideal solution (TOPSIS) method to detect and evaluate occupational risks in different construction activities through a questionnaire survey approach.

Aroundsix types of construction activities and corresponding ten risks are identified and evaluated during the study. Based on the calculation of risk scores, the findings imply that “roof work activities,” “finishing work,” “mechanical, electrical and plumbing work (MEP)” are hazardous construction activities, while, among the corresponding ten risks, “workers falling from height” is the most prominent risk among the majority of activities. Other risks include “risk due to fire and electric accidents” and “struck by falling objects,” which are the major risks in high-rise construction projects.

Theoriginality of the paper lies in its activity-based risk assessment and ranking of hazards in high-rise construction projects. By integrating theoretical insights with practical applications, the study attempts to enhance occupational safety and reduce accidents on construction sites, thereby significantly contributing to both academia and industry practices.