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The study seeks to propose a linear model by applying complexity theory and resource-based theory to investigate how hotels achieve competitive advantage and organizational performance during the Covid-19 pandemic from the perspective of hotel leaders.

Using a standardized questionnaire and convenience sampling approach hotel managers and administrative employees were surveyed online. A total of 354 participants from five provinces in China were examined using Smart PLS and fsQCA 3.0 for analysis. The utilization of the asymmetric method facilitates the elucidation of relationships that may not be readily apparent when employing conventional symmetric approaches.

The results display a significant impact of transformational leadership (TL) on market orientation (MO), competitive advantage (CA) and organizational performance (OP). The results show numerous combinations using fsQCA that can be utilized to increase OP within the hotel industry.

At present, there is a lack of substantial empirical evidence to comprehensively investigate the impact of TL on MO, CA and OP in the field of hospitality research specifically in the context of the Covid-19. The study also contributes by providing an explanation of the factors that contribute to the development of a higher organizational performance base through TL, MO and CA during the Covid-19 pandemic.

This study aims to assess the efficacy of thermal analysis of concrete slabs by including different insulation materials using ANSYS. Regression equations were proposed to predict the thermal conductivity using concrete density. As these simulation and regression analyses are essential tools in designing the thermal insulation concretes with various densities, they sequentially reduce the associated time, effort and cost.

Two grades of concretes were taken for thermal analysis. They were designed by replacing the natural fine aggregates with thermal insulation aggregates: expanded polystyrene, exfoliated vermiculite and light expanded clay. Density, temperature difference, specific heat capacity, thermal conductivity and time were measured by conducting experiments. This data was used to simulate concrete slabs in ANSYS. Regression analysis was performed to obtain the relation between density and thermal conductivity. Finally, the quality of the predicted regression equations was assessed using root mean square error (RMSE), mean absolute error (MAE), integral absolute error (IAE) and normal efficiency (NE).

ANSYS analysis on concrete slabs accurately estimates the thermal behavior of concrete, with lesser error value ranges between 0.19 and 7.92%. Further, the developed regression equations proved accurate with lower values of RMSE (0.013 to 0.089), MAE (0.009 to 0.088); IAE (0.216 to 5.828%) and higher values of NE (94.16 to 99.97%).

The thermal analysis accurately simulates the experimental transfer of heat across the concrete slab. Obtained regression equations proved helpful while designing the thermal insulation concrete.

This study aims to develop a four-dimensional (4D) textile composite that self-forms upon thermal stimulation while eliminating thermomechanical programming steps by using fused deposition modeling (FDM) 3D printing technology, and tries to refine the product development path for this composite.

Polylactic acid (PLA) printing filaments were deposited on prestretched Lycra-knitted fabric using desktop-level FDM 3D printing technology to construct a three-layer structure of thermally responsive 4D textiles. Subsequently, the effects of different PLA thicknesses and Lycra knit fabric relative elongation on the permanent shape of thermally responsive 4D textiles were studied. Finally, a simulation program was written, and a case in this study demonstrates the usage of thermally responsive 4D textiles and the simulation program to design a wrist support product.

The constructed three-layer structure of PLA and Lycra knitted fabric can self-form under thermal stimulation. The material can also achieve reversible transformation between a permanent shape and multiple temporary shapes. Thinner PLA deposition and higher relative elongation of the Lycra-knitted fabric result in the greater curvature of the permanent shape of the thermally responsive 4D textile. The simulation program accurately predicted the permanent form of multiple basic shapes.

The proposed method enables 4D textiles to directly self-form upon thermal, which helps to improve the manufacturing efficiency of 4D textiles. The thermal responsiveness of the composite also contributes to building an intelligent human–material–environment interaction system.

The construction industry has been traditionally referred to as slow when it comes to technological transformation. This study aims to investigate and present a scorecard of the construction industry in the past decade, the paper adopted Bibliometrics. The study identified the various digital transformation (DT) aspects in the construction industry and future research directions are also identified.

To achieve the aim of this research, an inductive approach was adopted through a grounded theory strategy. Secondary data was retrieved from the Scopus database and analysed using Biblioshiny and VOSviewer. The data was retrieved through specific keywords related to the study focus.

The study also proposed a balanced flow model for DT discussion in the construction industry. DT in the construction industry disrupts every aspect of the industry, albeit at different rates due to the existing barriers; hence, the study identified areas that require further research. It, thus, provides a theoretical and practical basis for researchers and practitioners alike.

The study reviewed the DT research discuss in the construction industry. It is worthy of note that this is the first study that analyses the DT of the construction industry in the past decade.

Given resource constraints such as time and staffing, hotels cannot respond to all negative online reviews (NORs). Therefore, this study investigates (1) what types of NORs hotels should prioritize responding; and (2) what response strategies are more effective in handling different types of NORs to minimize the negative ramifications.

Four experiments in the context of hospitability were used to test the hypotheses.

Our findings show that NORs with implicit conclusions (e.g. “I do not believe that is a good choice, you know what I mean.”) are more dissuasive than NORs with explicit ones (e.g. “Do not buy it.”) because the former NORs are perceived as more objective than the latter NORs. More importantly, our results show that firms do not need to respond to explicit NORs. When responding to implicit NORs, firms should prioritize those related to service failures caused by external (e.g. weather, technological misfunction) rather than internal (e.g. poor management, employee skills) factors.

Our studies focus on the language styles of Chinese NORs, and future research should investigate how language styles influence dissuasion in other languages.

Our results show that NORs with implicit conclusions negatively impact consumer attitude and thus hurt performance more significantly than those with explicit conclusions. Therefore, firms should allocate limited staffing and resources to NORs with implicit conclusions. When responding to implicit NORs, firms should select NORs that can be attributed to external factors.

Our findings shed light on the importance of the language styles of NORs and provide marketers with insights into how to handle NORs. Our results reveal that consumers perceive higher objectivity of NORs when these reviews are implicit than when they are explicit. Furthermore, this study contributes to the online review literature by suggesting that firms should tailor their response strategies for NORs based on the reviewers’ language styles.

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

In this paper, based on the density functional theory (DFT) and finite element method (FEM), the elastic, buckling and vibrational behaviors of the monolayer bismuthene are studied.

The computed elastic properties based on DFT are used to develop a finite element (FE) model for the monolayer bismuthene in which the Bi-Bi bonds are simulated by beam elements. Furthermore, mass elements are used to model the Bi atoms. The developed FE model is used to compute Young's modulus of monolayer bismuthene. The model is then used to evaluate the buckling force and fundamental natural frequency of the monolayer bismuthene with different geometrical parameters.

Comparing the results of the FEM and DFT, it is shown that the proposed model can predict Young's modulus of the monolayer bismuthene with an acceptable accuracy. It is also shown that the influence of the vertical side length on the fundamental natural frequency of the monolayer bismuthene is not significant. However, vibrational characteristics of the bismuthene are significantly affected by the horizontal side length.

DFT and FEM are used to study the elastic, vibrational and buckling properties of the monolayer bismuthene. The developed model can be used to predict Young's modulus of the monolayer bismuthene accurately. Effect of the vertical side length on the fundamental natural frequency is negligible. However, vibrational characteristics are significantly affected by the horizontal side length.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

Studies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.

The authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.

The results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.

This study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.