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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.

At the forefront of current research is the investigation of how big data analytics capability (BDAC) and artificial intelligence capability (AIC) can enhance performance in concert. Therefore, current study intended to conduct more deep research into emerging phenomena and attempts to cover the gap by exploring how entrepreneurial orientations (EO) emphasize the use of two emerging capabilities under the moderating role of environmental dynamism which in turn augment co-innovation and hotel performance.

Data were collected from four-star and five-star hotels located in Kula Lumpur and Langkawi in Malaysia. A total of 260 responses were obtained from IT staff and senior managers with the assistance of a Manpower agency for data analysis. The hypotheses were examined by analyzing the data using PLS-SEM technique through Smart PLS 3 software.

The result revealed that EO has a positive and significant effect on co-innovation (CIN). Additionally, the BDAC and AIC have been tested and proven to be potential mediators between EO and CIN. Also, environmental dynamism as moderator has positive and significant effect on BDAC and co-innovation performance, however, not significant impact on AIC and co-innovation performance. Lastly, findings displayed positive and significant moderated mediation impact of environmental dynamics on BDAC and CIN with hotel performance, but not significant influence on AIC and co-innovation with hotel performance. For theoretical corroboration of the research findings, the current study integrated EO, resource-based view theory and contingent dynamic capabilities (CDC), because neither single stance can explicate an extant research framework.

This study anticipated the several implications for the entrepreneurs of hospitality industry. Managers are recommended to invest in the entrepreneurial traits of the employees/organizations and make strategic readjustment of their capabilities for sustained business performance.

The study goes beyond the normal inquiry by investigating moderated mediation impact of environmental dynamism between two emerging capabilities, co-innovation and hotel performance relationships. Another novelty of this study is to culminate the exploitation and adoption of emerging IT-based capabilities in cross domains of management, entrepreneurship, information systems management within the hotel industry.

Nowadays, a prominent research area is the development of competitive advantages in companies, due to their environmental commitment and orientation. Based on resource-based view (RBV) and institutional theory (InT), this paper aims to investigate the influence of internal and external orientation on businesses' sustainable performance while considering the effect of sustainable supply chain management (SSCM) practices.

Data from 351 manufacturing companies in the Kingdom of Saudi Arabia have been collected and analysed through structural equation modelling (SEM) using the partial least squares (PLS) method.

The results indicated that both internal and external environmental orientation have important effects on SSCM practices, which in turn have a considerable beneficial effect on environmental, social and economic performance.

Although SSCM is constantly gaining ground in the literature, most SSCM research and models examine its effects, antecedents or motivation, mainly adopting a qualitative approach. Research on the topic adopting a large-scale empirical approach is still limited. In this context, this study contributes to the SSCM management literature by exploring the role of environmental orientation in facilitating the adoption of SSCM practices and improving companies' performance.

This study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets.

The authors use time–frequency connectedness method to study the uncertainty spillover among the asset classes.

This study’s findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility.

This study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.

This study aims to assesses the factors that affected the development of cloud-based accounting education (DCBAE) and students' academic performance at Oman Universities.

In this paper a questionnaire was used to collect data for a sample of students. Partial least squares (PLS) were used to assess the hypotheses and model.

Results indicate that human factors and cultural and social factors have a direct positive effect on the DCBAE and students' academic performance.

This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Thus, it can help universities (accounting education in particular) in the Sultanate of Oman know the factors that affect the development of accounting education and adopt policies and strategies that depend on cloud computing in education operations, even after the COVID-19 pandemic.

The main contribution of this study is to evaluate the factors that affected the DCBAE and the academic performance of students in the Sultanate of Oman from March 15, 2020 to June 2022, a period that witnessed the application of cloud-based education, either in whole or in part using a questionnaire about the opinions of students.

This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).

The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).

The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.

The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.

The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.

Contract incompleteness with deficiency, inconsistency, defectiveness, and ambiguity in contract clauses, which can cause misunderstandings and misinterpretations, may result with disputes in projects. This study aims to investigate contract incompleteness factors with a hybrid fuzzy multi-criteria decision approach.

Contract incompleteness factors were ranked by fuzzy VIKOR (Visekriterijumska Optimizacija I Kompromisno Resenje) method, and the most significant factors were subjected to fuzzy decision-making trial and evaluation laboratory (DEMATEL) to examine their causal relationships. The study is not limited to ranking the identified factors solely, since their cause-effect interactions are also essential for proper risk management in construction projects.

Hybrid use of multi-criteria analysis reveals that ambiguity in enforceability including excessive demands and significant amendments in the scope of works are the top two causal contract incompleteness factors, while lack of implementation details and focus of focal point, and insufficient supporting and technical documents are the most affected ones.

Contractual causes of disputes due to contract incompleteness factors other than requirements of the contracts have been rarely investigated in the literature. The research is one of the first studies in the literature investigating the causal relationship among factors in construction contracts, which might lead to project disputes. Findings are expected to improve contract drafting, eventually contributing to effective risk management in construction projects.

With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

This paper examines the effect of green creativity (GC) on the business sustainability (BS) of large manufacturing firms (LMFs) in Malaysia and investigates the mediating effect of total quality management (TQM) on this relationship.

A quantitative approach was adopted, using a sample of 206 LMFs; the formulated hypotheses were analysed using partial least squares structural equation modeling.

The findings revealed a significant positive effect of GC on the TQM and BS of LMFs and a significant positive effect of TQM on their BS. TQM also has a full mediating effect on the relationship between GC and BS.

One of the limitations of this study is its focus on Malaysian LMFs. It nevertheless contributes to the literature by extending knowledge concerning their TQM, GC and multi-faceted perspectives. This is largely ignored in literature and, as such, the study paves the way for additional research.

The findings of this study may be used as guidelines for CEOs, particularly on the way TQM and GC can be developed for enhanced BS, in the context of South Asian countries.

This study is the first to test the influence of GC on Malaysian LMFs’ BS and the first worldwide to investigate the mediating effect of TQM on their GC-BS relationship.

Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area.

The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI.

Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area.

Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality.