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We seek to contribute to the literature by comparing and analyzing the relationship between Australian and Taiwanese students regarding environmental value attitude, action intention and green marketing intention. Specifically, by comparing the green marketing intention of hospitality and tourism (H&T) students in the East with that in the West.

A well-designed curriculum examines student thinking and behavior (learn). This study compares and analyzes the value and attitude and the application of big data to green marketing among Taiwanese and Australian university students using the stimulus-organism-response (S-O-R) model. Structural equation modeling (SEM) was used to test the hypotheses in a sample of 633 H&T students in Taiwan (389) and Australia (244).

This study also shows how the national differences between Australia and Taiwan have interference effects on the relationship between value attitudes and action intentions and between action intentions and the green marketing intention. We also combined the application of big data and related variables and estimated the mediating effect of related variables to evaluate the impact on action intentions and green marketing of big data applications.

There are significant differences in the sustainable behavior and intentions of H&T higher education students that reflect the educational differences between the East and the West. These different results may be due to a lack of natural resources and the relatively smaller size of Taiwan. With the strengthening of environmental action intention (AI) and green marketing intention (GMI), Taiwanese hospitality and management (H&M) students' sense of crisis increases, and their performance in GMI is slightly higher than that in Australia.

These findings indicate that it is necessary to consider students' ecological concepts, environmental knowledge, environmental value attitude and environmental action intention to improve their intentions to engage in green marketing under the stimulus-organism-response (S-O-R) framework. We also found that environmental knowledge has a mediating effect on the relationship between ecological concepts and environmental value attitude.

This article specifies the theoretical influence of future time perspective (FTP) on the behavior of the parties involved in organizational dispute resolution processes.

This study employed a novel qualitative systematic reinterpretation methodology. A software-assisted qualitative content analysis for the systematic reinterpretation of 141 academic publications on organizational conflicts, dispute resolution, and dispute system design processes was performed to elicit crucial points at which FTP that was not originally specified is theoretically emerged in those processes.

The sorted findings detail 829 critical points (themes) in those processes where FTP has theoretically emerged. The results confirm that FTP has a comprehensive theoretical presence (81.3%) in the discourse on organizational conflicts, dispute resolution and dispute system design processes. Furthermore, when the relevant parties’ FTP is operative, their conflict prevention approach is widespread, and the parties perceive workplace relationships and dispute resolution processes as dominant.

The novelty of this study is related to its effort to identify and diagnose theoretical situations within the discourse on organizational dispute resolution processes in which the effect of FTP (either positive or negative) on the present-time behavior of parties within those processes is demonstrated. This work addresses this issue through unique qualitative systematic reinterpretation, which differs from other types of research syntheses of secondary data.

The purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.

This research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.

The study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.

The study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.

The research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.

The research holds significant social implications by contributing insights that extend beyond the realm of sports.

The originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

The research identifies literature on Home-Based Businesses (HBBs) from 2000 to August 2023, focuses on their economic roles, challenges for entrepreneurs and success strategies, reflecting societal and technological changes. This guides future studies and highlights knowledge gaps.

A systematic literature review of published, peer-reviewed research between the years 2000 and 2023 is performed to examine how research on HBBs has changed over time, areas needing more study and how research has been done.

A total of 58 articles were analyzed and categorized into five distinct themes. Key insights into the evolution, significance and multifaceted aspects of HBBs are presented, revealing the impact and role of these businesses in a modern economic context.

The synthesis of existing literature enhances our understanding of recent studies on HBBs, focusing on challenges, and identifies promising directions for future research.

This study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.

The sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.

Sawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.

Sawtooth is limited to flying optics AM process.

Development of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

This study aims to present an integrated green transport sustainability model (GTSM) to comprehensively understand and explain the multifaceted dynamics of green transport initiatives. The purpose of the study is to evaluate gaps in understanding the interactions between socio-economic and environmental goals in green transport systems using structural equation modelling (SEM) to help in drafting sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study examines different constructs that collectively influence green transport policy effectiveness (GTPE). Ultimately, the study aims to provide a robust framework for improving the effectiveness of green transport policies and regulations.

Grounded in empirical evidence, the study utilizes SEM to demonstrate the interplay between policymaking, socio-economic factors, technological consideration and environmental outcomes in green transport. The research framework is developed based on the comprehensive review of the literatures to embrace sustainability in transportation considering stakeholders perceptions. The study navigated a GTSM under socio-economic and environmental goals for road-mapping sustainability and larger acceptance of green transportation.

It is found that technological advancements in transportation are the most significant determinants of GTPE. This implies the need to develop advancements in technologies to embrace the larger acceptance of green transport. Promotion of environmentally sustainable transportation practices, socio-economic factors and use of eco-friendly transportation modes are also found as significant predictors of GTPE, which suggested that the policies aimed at up-gradation of socio-economic standards and the use of environment friendly modes of transport can help in promoting the active involvement of stakeholders to use green transportation.

The study originally investigated critical constructs to assist in preparing sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study reciprocated its originality by presenting an integrated model related with green transport sustainability dimensions based on theoretical constructs to examine the interplay between policy effectiveness, technological advancements, socio-economic factors and environmental outcomes. The study addressed the key pillars of green transportation and originally highlighted the importance of socio-economic factors and technological advancements in advancing green transport sustainability. It is recommended that the policymaker should make investments in green transport infrastructure and should design a policy for integration of green transportation with a focus on the engagement of all stakeholders for practical implementations.

The aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model’s accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.

The study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model’s advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.

The study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.

This study’s originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI’s role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts.

The purpose of this study was to investigate the impact of using a jigsaw learning strategy integrated with computer simulation (JLSICS) on the academic achievement and attitudes of students, along with exploring the relationships between them in the process of learning about acids and bases.

The research design used in the study was quasi-experimental, using non-equivalent comparison groups for both pre- and post-tests. A quantitative approach was used to address the research problem, with three groups involved: two experimental and one comparative group. The treatment group, which received the JLSICS intervention, consisted of two intact classes, while the comparison group included one intact class. Data collection involved achievement tests and attitude scale tests on acid and base. Various statistical analyses such as one-way analysis of variance, one-way multivariate analysis of variance, Pearson product-moment correlation, mean and standard deviation were used for data analysis.

The study’s results revealed that the incorporation of the JLSICS had a beneficial influence on the academic achievement and attitudes of grade 10 chemistry students towards acid and base topics. The JLSICS approach proved to be more successful than both conventional methods and the standalone use of the jigsaw learning strategy (JLS) in terms of both achievement and attitudes. The research demonstrated a correlation between positive attitudes towards chemistry among high school students and enhanced achievement in the subject.

The study only focused on one specific aspect of chemistry (acid and base chemistry), which restricts the applicability of the findings to other chemistry topics or subjects. In addition, the study used a quasi-experimental design with a pretest-posttest comparison group, which may introduce variables that could confound the results and restrict causal inferences.

This study addresses the gap in instructional interventions and provides theoretical and practical insights. It emphasizes the importance of incorporating contemporary instructional methods for policymakers, benefiting the government, society and students. By enhancing student achievement, attitudes and critical thinking skills, this approach empowers students to take charge of their learning, fostering deep understanding and analysis. Furthermore, JLSICS aids in grasping abstract chemistry concepts and has the potential to reduce costs associated with purchasing chemicals for schools. This research opens doors for similar studies in different educational settings, offering valuable insights for educators and policymakers.

The originality and value of this study are in its exploration of integrating the jigsaw learning strategy with computer simulations as an instructional approach in chemistry education. This research contributes to the existing literature by showing the effectiveness of JLSICS in improving students’ achievements and attitudes towards acid and base topics. It also emphasizes the importance of fostering positive attitudes towards chemistry to enhance students’ overall achievement in the subject.

The onset of a health crisis has substantially crippled the hotel industry, causing employees' fears of an imminent job loss. This study investigates how hotel employees' perceived job insecurity affects work engagement and psychological withdrawal behavior. Additionally, it explores the mediating role of work engagement between job insecurity and psychological withdrawal behavior, along with examining the moderating effects of employee resilience on the links between job insecurity, work engagement and psychological withdrawal behavior.

Adopting simple random sampling, a total of 357 completed questionnaires by Thai frontline hotel employees in 36 four- and five-star international hotel chains in Phuket, Thailand, were analyzed. Data analyses were undertaken by SPSS version 25.0 and partial least squares structural equation modeling (PLS-SEM) version 4.0.9.1.

Results indicate that perceived job insecurity diminishes work engagement and leads to psychological withdrawal behavior. Work engagement is found to partially mediate the connection between job insecurity and psychological withdrawal behavior. Furthermore, employee resilience lessens the impact of job insecurity on work engagement while reinforcing the link between work engagement and psychological withdrawal behavior.

The study findings offer valuable practical implications, illustrating how Thai hospitality firms can cultivate effective talent management practices to develop and enhance employees' skills, engagement and enthusiasm in their work. These practices can assist employees in coping with and managing their perceptions of job insecurity during turbulent times and uncontrollable crises.

This study creates a compelling framework to elucidate the connections among COVID-19-intensified job insecurity, work-related outcomes and personal factors. It introduces a previously underexamined perspective that enriches the authors' theoretical comprehension of how personal resources, like employee resilience, serve as protective factors, shaping employee behavior and performance amidst job insecurity. Moreover, the study advocates for a synthesizing approach, emphasizing the integration of various theoretical perspectives and past literature, particularly when research gaps cannot be sufficiently addressed by a single theory.