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This study aims to prepare some disperse dyes based on pyrazole derivatives and fully evaluate their use as azo-disperse dyes linked with pyrazolo[1,5-a] pyrimidine. This study aims to prepare dye polyester fabric at different conditions (pH and time) at a depth of 4% using a high-temperature pressure process in water.

The disperse dyes were created by dissolving enaminone and pyrazole derivatives in glacial acetic acid and fusing sodium acetate at reflux for 6 h. The chemical structure of the produced dye was studied using elemental analysis, Fourier-transform infrared spectroscopy, 1H NMR and 13C NMR spectroscopic analytical methods. The dyeing of Dispersed dyes 14–16 on polyester under diverse conditions was comprehensively investigated in this work.

The prepared Organic dyes 14–16 were found to be highly functional and suitable for this type of dyeing technique. High color strength is possessed by the materials dyed with Disperse dyes 14–16. Even in low alkaline conditions, these dyes exhibited a strong affinity for polyester fabric, changing just little in response to pH changes. The hue of the dyed polyester samples varied from beige to reddish brown and yellowish brown because of the coupler moieties.

This study is important because it offers novel dyes that may be used to dye polyethylene terephthalate fibers that exhibit remarkable brightness and levelness. It also offers a viable solution for creating a variety of colors in polyester fabrics.

Empirical examinations of initial public offering (IPO) initial returns often rely heavily on linear regression models. However, these models can prove inefficient owing to their susceptibility to outliers, a common occurrence in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve.

The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its application of the random forest method. The accuracy of random forest in comparison to other methods is evaluated. The findings indicate that the random forest model significantly outperforms other methods in all of the evaluated aspects.

The variable importance measure indicates that investors’ demand, divergence of opinion among investors and offer price are the most crucial predictors of IPO initial returns. These determinants hold particular significance due to the widespread use of the fixed-price method in Malaysia, as this method amplifies the information asymmetry in the IPO market.

To the best of the authors’ knowledge, this study is among the pioneering works in Malaysian literature to apply the random forest method to address the constraints of conventional linear regression models. This is achieved by considering a more extensive array of factors and acknowledging the influence of outliers. Additionally, this study adds value to Malaysian literature by ranking and identifying the ex-ante information that best signals the issuing firm’s quality. This contribution facilitates prospective investors’ decision-making processes and provides issuing firms with effective means to communicate their value and quality to the IPO market.

Given the increasing attention on ChatGPT in academia due to its advanced features and capabilities, this study aims to examine the links among Artificial intelligence (AI), knowledge sharing, ethics, academia and libraries in educational institutions. Moreover, this study also aims to provide a literature base while discussing recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.

The paper involves a structured interview format where a human interviewer poses questions “Qs” in ChatGPT, related to knowledge sharing, ethics, academia and libraries. Moreover a literature base is also provide to discussed recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.

The study find out that AI and ChatGPT technologies in educational institutions affect knowledge sharing, ethical consideration, academia and libraries. This study also highlights literature directions for the trends and proper use of the AI and ChatGPT among institutions, such as improving student-learning engagement.

This research contributes to the prior literature by offering an in-depth review of current uses and applications of AI and ChatGPT in educational institutions. It not only highlights key trends and innovations but also provides insights and guidelines for future research. This study also provides insights and guidelines for future research. Furthermore, the article emphasizes the potential impact of AI and ChatGPT on the future of education and technology.

The heat transport phenomenon in which energy transfers due to temperature differences is an important topic of interest for scientists in recent times. It is because of its wide range of applications in numerous domains such as electronics, heat dispersion, thermoregulation, cooling mechanism, the managing temperature in automotive mobile engines, climate engineering, magnetoresistance devices, etc. On account of such considerations, the magnetohydrodynamic (MHD) entropy rate for nanomaterial (CoFe₂O₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is analyzed. The Darcy–Forchheimer relation is utilized to describe the impact of a porous medium on a stretched sheet. Two nanoparticles molybdenum (MoS₄) and cobalt ferrite (CoFe₂O₄) are combined to make hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂). Heat flux corresponds to the Cattaneo–Christov model executed through heat transfer analysis. The influence of dissipation and heat absorption/generation on energy expression for nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is described.

Nonlinear partial differential expressions have been exchanged into dimensionless ordinary differential expressions using relevant transformations. Newton’s built-in shooting method is employed to achieve the required results.

Concepts of fluid flow, energy transport and entropy optimization are discussed. Computational analysis of local skin friction and Nusselt number against sundry parameters for nanomaterial (CoFe₂O₄/C₂H₆O₂) and hybrid nanomaterial (CoFe₂O₄+MoS₄/C₂H₆O₂) is engrossed. Larger magnetic field parameters decay fluid flow and entropy generation, while an opposite behavior is observed for temperature. Variation in magnetic field variables and volume fractions causes the resistive force to boost up. Intensification in entropy generation can be seen for higher porosity parameters, whereas a reverse trend follows for fluid flow. Heat and local Nusselt numbers rise with an increase in thermal relaxation time parameters.

No such work is yet published in the literature.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

While existing research has explored the association between supervisor support and turnover intention among construction workers, there is a notable gap in the literature concerning the potential mediating role of work engagement in elucidating this relationship, warranting further investigation. The paper, hence, aims to examine the mediating role of work engagement in the relationship between supervisor support and turnover intention among construction workers.

Based on the quantitative research method, the hypothesis was tested. The data were collected from 144 construction professionals using a structured questionnaire. Observed variables were tested using confirmatory factor analysis, and the mediating role relationship was validated using hierarchical regression.

The outcome of this study shows a significant positive impact of work engagement and supervisor support on employee turnover intention. The study further showed that work engagement plays a mediating role in the connection between supervisory support and the intention to turnover and improve project and business performance. Turnover intention, on the other hand, negatively affects project and organizational performance.

By enhancing employee work engagement and perceptions of supervisor support, the findings of this study may aid construction organizations in making better judgments regarding the likelihood of employee turnover. The effectiveness of the project and the organization will likely be greatly impacted.

The results of this study provide supporting evidence and advance efforts at reducing employee turnover intention through work engagement and supervisor support in improving project and organizational performance.

This study aims to examine the impact of logistics performance and technological innovation on environmental quality in Africa, focusing on carbon dioxide (CO2) emissions and renewable energy consumption as indicators of environmental quality.

The study employs a panel data analysis of 43 African countries over the period 1990–2021. Data on logistics performance, technological innovation, CO2 emissions, and renewable energy consumption are sourced from the World Development Indicators database of the World Bank. Grounded in the Triple Bottom Line (TBL) framework, which integrates economic, social and environmental dimensions of sustainability, the analysis utilises the generalised method of moments (GMM) technique to address the issue of endogeneity.

The regression results reveal significant relationships between logistics performance, technological innovation and environmental quality indicators. Logistics performance demonstrates a negative impact on CO2 emissions, while technological innovation positively influences renewable energy consumption. The interactive effect of logistics performance and technological innovation mitigates CO2 emissions, aligning with the TBL framework’s environmental dimension by promoting sustainability. Furthermore, trade openness exhibits a significant negative effect on both CO2 emissions and renewable energy consumption. The findings highlight the potential synergies between logistics performance and technological innovation in driving environmental sustainability while offering economic benefits and addressing social well-being.

The findings suggest the importance of prioritising investments in enhancing logistics performance and fostering technological innovation to achieve environmental sustainability goals in Africa.

To the best of the researchers’ knowledge, this study presents an initial attempt to examine the nexus between logistics performance and environmental quality in Africa using the logistics performance index. Furthermore, beyond assessing the individual effects of logistics performance and technological innovation on environmental quality, we delve into their interactive dynamics, adding novelty to the study.

The intense competition among restaurant brands made it difficult to retain and engage customers. Service innovation can play a vital role to serve this purpose, however, restaurant brands' efforts to bringing innovativeness may not yield desired results unless customers perceive them innovative. Therefore, the present study aims to investigate the role of customer perceived innovativeness (CPRI) in enhancing brand love and evangelism among customers. This study compares these effects between fast-food and continental restaurants to offer deeper insights.

Data were collected from 303 customers of fast-food and continental restaurants through self-administered structured questionnaire. The reliability and validity were established through confirmatory factor analysis. The hypotheses were tested using structural equation modeling and multi-group SEM with MPlus.

The findings of this study reveal that menu, experiential and promotional innovativeness dimensions of CPRI positively influence brand love. The effects of CPRI dimensions transcend to brand evangelism dimensions, i.e. brand purchase intension, positive brand referrals and oppositional brand referrals via brand love. The multi-group analysis showed that continental restaurants' menu innovativeness strongly impacts brand love and evangelism while promotional innovativeness matters more in the context of fast-food restaurants.

This study helps restaurant managers in devising tailor made strategies for fast-food and continental restaurants by focusing on the relevant attributes to bring innovation.

This research is one of the pioneering studies to investigate the relationship of CPRI with brand evangelism through the mediating role of brand love. This study also marks an initial attempt to compare fast-food and continental restaurants in the context of CPRI.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

This study aims to utilize Relational Job Design (RJD) theory and Conservation of Resources (COR) theory to explore the impact of RJD, particularly its key components – job impact (JI) and job contact (JC) – on employees’ quiet quitting (QQ) behavior. It also investigates the role of work alienation (WA) as a mediator and examines whether inclusive leadership (IL) has a moderating effect on these proposed relationships.

This paper used paired dyadic surveys to gather data from 166 full-time employees and their immediate supervisors used in service organisations across the United Arab Emirates (UAE). The study’s moderated mediation model was tested using hierarchical regression and the PROCESS Macro in statistical package for the social sciences (SPSS) v.26.

The results empirically support our hypotheses: RJD (JI and JC) significantly reduces employees’ feelings of WA, thereby decreasing the likelihood of engaging in QQ behaviours. As expected, the study also revealed that IL acts as a moderator by amplifying the detrimental effects of RJD (JI and JC) on feelings of alienation from one’s work, which in turn decreases the likelihood of engaging in QQ behaviour.

This study fills a gap in understanding how relational work design and IL jointly influence unfavourable workplace attitudes and behaviours. It offers a foundation for future studies on QQ in diverse cultural and organisational contexts.

This study provides practical insights for organisations aiming to tackle employee quitting behaviours. Human resource (HR) professionals and leaders should focus on enhancing both JC and JI, while actively fostering IL. These actions are critical for significantly boosting employee engagement, fostering a more motivated and connected workforce and reduce QQ behaviours in the workplace.

Although the data in this study were collected from an organisation with multiple branches across the country, the findings may still have limited generalisability to other organisations or industries. While this paper acknowledges this as a limitation, it also presents an opportunity for future research to explore QQ across diverse cultural and organisational contexts. Larger and more varied samples in future studies could provide deeper insights into the prevalence and drivers of QQ in different workplace environments.

This study explores the under-researched phenomenon of QQ, revealing how RJD can mitigate these behaviours. It uncovers new psychological mechanisms, offering actionable insights for management strategies to enhance employee commitment and reduce negative behaviours.