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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.

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.

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.

This qualitative work aims to explore the university students’ attitude toward advantages, drawbacks and prospects of ChatGPT.

This paper applies well accepted Colaizzi’s phenomenological descriptive method of enquiry and content analysis method to reveal the ChatGPT user experience of students in the higher education level.

The study’s findings indicate that ChatGPT enhances the quality of learning and facilitates faster learning among university students. However, despite numerous positive outcomes, it is noted that ChatGPT may diminish students' creativity by swiftly addressing their critical queries. Over time, students may experience a decline in patience and critical thinking skills as they excessively rely on ChatGPT, potentially leading to ethical misconduct.

This paper primarily explores the advantages and drawbacks of using ChatGPT in the university context of Bangladesh. The present study creates a platform for future research in this domain with comprehensive study design. The study results alert the policy makers to improve upcoming version of ChatGPT with convenient user experience and academicians as this paper unleash several positive as well as negative consequences of using this AI-enabled chatbot.

Protein-energy wasting is a common complication among patients with kidney failure undergoing dialysis. This study aims to develop a homemade oral nutrition supplement (ONS) to fulfill the energy and protein requirements of these patients.

Three formulations of homemade ONS were developed using soybean milk, whey protein isolates and canola oil. Two of these formulations were flavored with pineapple and honeydew juices, respectively. The energy and macronutrient contents were determined using proximate analyses, and mineral contents were determined using inductively coupled plasma optical emission spectroscopy. The acceptance of homemade ONS for five attributes, namely color, taste, odor, consistency and overall acceptability, was assessed using the nine-point hedonic scale.

The homemade ONS provided 198–212 kcal and 8.4–9.6 g protein per 100 mL, which were comparable to commercial products. Similarly, the sodium (45–65 mg/100 mL) and phosphorus (56–66 mg/100 mL) contents were on par with commercial products. However, the potassium content of homemade ONS was higher, ranging from 141 to 155 mg per 100 mL. The sensory evaluation indicated that the formulation added with honeydew juice had a similar degree of acceptance as the commercial ONS, while formulations containing pineapple juice and without added fruit juice were less favored.

A few studies have investigated the development of food products for individuals with kidney failure on dialysis. However, to the best of the authors’ knowledge, this is the first study to focus on developing a homemade ONS specifically tailored to meet the unique nutritional needs of hemodialysis patients. In addition, this research included a comprehensive assessment of the beverage’s nutritional content and sensory attributes.

This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

Environmental threats are becoming severe in developing and undeveloped countries. It urges to know how green banking operations can foster sustainable development in these regions. This study aims to provide empirical evidence of the determinants of green banking operations in Bangladesh.

Based on the socially responsible investing (SRI) theory, this study examined the hypothesized relationships using a partial least square structural equation modeling (PLS-SEM) approach. The Bayesian SEM (BSEM) through a Markov Chain Monte Carlo (MCMC) approach was also used to validate the study's first-order model.

The findings show that sustainable innovativeness, green investment and green banking policy substantially and positively change green banking operations. Notably, green investment is the most influential predictor of green banking operations, driving banks to establish sustainable economic systems within the country.

The findings offer valuable guidance for scholars, financial institutions, policymakers and bank managers to develop and implement effective strategies for green banking operations. These strategies may significantly contribute to achieving the sustainable development goals (SDGs) in Bangladesh.

This study is ground-breaking in associating sustainable innovativeness and green banking operations from a developing country. It enriches our understanding of green banking, aligning with existing literature. Additionally, PLS-SEM and BSEM provide strong validation of the proposed theoretical model.

This study aims to determine the influence of experiential value, authentic happiness and experiential satisfaction on the behavioral intention of luxury fashion.

Two hundred online questionnaires were collected from customers who rented or bought wedding dresses between 2015 and 2020, with at least one rental or purchase coming from an Indonesian bridal wedding brand that promotes the idea of marriage under Islamic law. The quantitative methodology used in this study was examined using the Structural Equation Model analysis method with the AMOS 22 software.

The results showed that all hypotheses were accepted with significant positive influences, including experiential value in halal fashion on authentic happiness, experiential satisfaction and luxury fashion behavioral intention, authentic happiness in luxury fashion behavioral intention and experiential satisfaction, as well as experiential satisfaction in the behavioral intention of luxury fashion.

The data were collected from respondents who have rented and/or purchased wedding dresses. However, the number of respondents who only rent or who only buy was not identified.

The value of experience in halal fashion needs to be increased. Based on the results of this study, it is hoped that marketers can create effective marketing policies and strategies by paying attention to the value of the consumer’s halal fashion experience because it will affect their authentic happiness, experience satisfaction and luxury fashion behavior intention.

This study has unique originality in measuring the variable of luxury fashion behavioral intention, which was adjusted to the object of research, namely luxury fashion.