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This study aims to examine the influence of perceived organizational support (POS) on boundary-spanning behaviors (BSBs) among frontline employees in the hospitality industry. It also considered perceived supervisory support (PSS) as a moderating factor within a conceptual model.

Data were gathered from 651 full-time hospitality employees across 12 hotels in China. The analysis of the data used confirmatory factor analysis and structural equation modeling.

The findings revealed that POS influences hospitality boundary spanners’ BSBs, specifically external representation (ER), internal influence (II) and service delivery (SD). In addition, PSS moderates the relationship between POS and these frontline employees’ behaviors.

This study offers practical strategies for hospitality professionals to enhance frontline employees’ BSBs and foster supportive workplaces that drive employee excellence. These strategies encompass cultivating a supportive organizational culture, implementing supportive measures, fostering a sense of belonging among employees and ensuring supervisors’ well-being and competence in supporting their teams during daily interactions. These actions effectively motivate customer-contact employees to excel in their performance.

Fostering a helpful attitude in frontline employees is crucial for service firms’ success. Hospitality organizations must provide support to achieve this. Few studies have explored how organizational support contributes positively to the BSBs of customer-contact employees. This study goes beyond oversimplification and delves into the nuanced interplay between perceived support (POS and PSS) and hospitality frontline employees’ BSBs, focusing on ER, II and SD. The moderated mediating model enhances the understanding of support dynamics in the organizational context.

(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO₂) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO₂) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.

The two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).

(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.

(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.

(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO₂) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO₂) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.

The drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.

All the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

Guided by the affective events theory (AET), the purpose of this paper was to explore the impact of interpersonal trust in peers, as an affective work event, on two affect-driven behaviors (i.e. job performance and organizational citizenship behavior toward individuals [OCBI]) via positive affect during the Covid-19 pandemic, particularly in the Asia–Pacific region.

This study is quantitative in approach, and longitudinal survey study in design. The authors collected data from lecturers in 2020 at the beginning, at the end and two months after the first Covid-19 lockdown in Malaysia. Then, the authors utilized the efficient partial least squares (PLSe2) estimator to investigate the relationships between the variables, while also considering gender as a control variable.

The findings show that positive affect fully mediates the relationship between interpersonal trust in peers and job performance and partially mediates the relationship between interpersonal trust in peers and OCBI. Given that gender did not demonstrate any significant relationships with interpersonal trust in peers, positive affect, job performance and OCBI, the recommended policies can be universally developed and applied, irrespective of the gender of academics.

This research contributes originality by integrating the widely recognized theoretical framework of AET and investigating a less explored context, specifically the Malaysian higher education sector during the challenging initial phase of the Covid-19 pandemic. Furthermore, the authors adopt a novel and robust methodological approach, utilizing the efficient partial least squares (PLSe2) estimator, to thoroughly examine and validate the longitudinal theoretical model from both explanatory and predictive perspectives.

Employee’s innovative behavior as a team allows the organization to achieve its goals; however, team green creativity requires transformational and entrepreneurial leader support. Therefore, the study explores the impact of green transformational and entrepreneurial leadership on team innovative behavior and green new product development with the mediating role of team green creativity.

A survey was conducted to collect data from 455 employees working in the hospitality industry via a self-administered questionnaire, and hypotheses were analyzed using the partial least squares structural equation modeling PLS-SEM technique using Smart-PLS 4.0.

The results indicate that green transformational and entrepreneurial leadership styles positively and significantly affect team innovative behavior and new green product development performance. Furthermore, findings show that team green creativity partially mediates the relationship between green transformational and entrepreneurial leadership on team innovative behavior, and new green product development performance.

The results of this study provide insights to hospitality professionals pursuing the improvement of team innovative behavior and new green product development performance through team green creativity and leadership styles.

This study is useful for organizations that target new green product development performance and establish higher green innovative behavior cohesively among its team members through these robust leadership styles.

This study is the first attempt to provide a valuable contribution to the growing field of green leadership styles on team innovative behavior and new green product development performance through team green creativity.

This paper aims to create a comprehensive framework for selecting alternative materials in construction projects, integrating building information modeling (BIM) and the particle swarm optimization (PSO) algorithm. Materials comprise 60%–65% of the total project cost, and current methods require significant time and human resources.

A prototype framework is developed that considers multiple criteria to optimize the material selection process, addressing the significant investment of time and resources required in current methods. The study uses surveys and interviews with construction professionals to collect primary data on alternative materials selection.

The results show that integrating BIM and the PSO algorithm improves cost optimization and material selection outcomes.

This comprehensive tool enhances decision-making capabilities and resource utilization, improving project outcomes and resource utilization. It offers a systematic approach to evaluating and selecting materials, making it a valuable resource for construction professionals.

Although numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.

This study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.

The findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R² values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.

The authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.

This study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling.

Organizations encourage green creativity among their employees to reduce environmental pollution and attain sustainable development. Green, inclusive leadership can produce positive outcomes and influence employees' green innovation. However, green inclusive leadership and green creativity are empirically less examined in the sustainability literature. Therefore, this study theorized a conceptual model based on the organizational creativity theory to explore the influence of green inclusive leadership on employees’ green creativity. It also aimed to explore the intermediating effect of green passion and green absorptive capacity on the relationship between green inclusive leadership and green creativity.

Data were gathered through a self-administered questionnaire-based survey from 540 employees of a manufacturing enterprise in China. The hypotheses were analyzed using the partial least squares structural equation modeling (PLS-SEM) technique.

The findings reveal that green inclusive leadership positively and significantly influences employees' green creativity. Moreover, the results show that green passion and green absorptive capacity play positive mediating roles in the relationship between green inclusive leadership and green creativity.

This study offers practical implications for Chinese manufacturing enterprises, where green inclusive leadership is essential to enhance green passion, green absorptive capacity and green creativity among employees.

Drawing upon the organizational creativity theory, this research study is novel because it is one of the few empirical research studies to explore green inclusive leadership and green creativity in Chinese manufacturing enterprises. It further establishes the positive mediating role of green passion and green absorptive capacity in the relationship between green inclusive leadership and green creativity.

This study aims to explore a novel model that integrates the Kairat-II equation and Kairat-X equation (K-XE), denoted as the Kairat-II-X (K-II-X) equation. This model demonstrates the connections between the differential geometry of curves and the concept of equivalence.

The Painlevé analysis shows that the combined K-II-X equation retains the complete Painlevé integrability.

This study explores multiple soliton (solutions in the form of kink solutions with entirely new dispersion relations and phase shifts.

Hirota’s bilinear technique is used to provide these novel solutions.

This study also provides a diverse range of solutions for the K-II-X equation, including kink, periodic and singular solutions.

This study provides formal procedures for analyzing recently developed systems that investigate optical communications, plasma physics, oceans and seas, fluid mechanics and the differential geometry of curves, among other topics.

The study introduces a novel Painlevé integrable model that has been constructed and delivers valuable discoveries.

Early prediction of any type of cancer is important for the treatment of this type of disease, therefore, our target to evaluate whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels (using EIS), could help in the treatment of breast cancer or not? Human epidermal growth factor receptor 2 (HER2) overexpression is an important biomarker for treatment selection in earlier stages of cancers. The combined detection of the HER2 gene in plasma for blood cancer provides an important reference index for the prognosis of metastasis to other tissues. For this purpose, the authors fabricated and characterized a model wireless biosensor-based electrochemical impedance spectroscopy (EIS) for detecting HER2 plasma as therapeutics.

Most sensors generally are fabricated based on a connection between component of the sensors and the external circuits through wires. Although these types of sensors provide suitable sensitivities and also quick responses, the connection wires can be limited to the sensing ability in various devices approximately. Therefore, the authors designed a wireless sensor, which can provide the advantages of in vivo sensing and also long-distance sensing, quickly.

The biosensor structure was designed for detection of HER2, HER3 and HER-4 from lab-on-chip approach with six units of screen-printed electrode (SPE), which is built of an electrochemical device of gold/silver, silver/silver or carbon electrodes. The results exhibited that the biosensor is completely selective at low concentrations of the plasma and HER2 detection via the standard addition approach has a linearity plot, therefore, by using this type of biosensors HER2 in plasma can be detected.

This is then followed by detecting HER2 in real plasma using standard way which proved to have great linearity (R² = 0.991) proving that this technique can be used to detect HER2 solution in real patients.