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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 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 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.