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Gripped by the COVID-19 pandemic, the airline industry has faced many restrictions. Based on the determining factor of repurchase intention, this research paper addresses the roles of brand engagement (BE) and consumer trust during the pandemic.

A number of 439 out of 500 questionnaires, which were distributed among the Iranian airlines' customers, were completed and returned (an 87% response rate). SmartPLS 3 was used to carry out the confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) in order to validate the research hypotheses.

The results show that during the COVID-19 pandemic, BE had a significant effect on customer purchase intention. In addition, BE boosted customer trust and thereby increased repurchase intention. Perceived risks of COVID-19 moderated these relationships. The findings also show that airline customers are highly sensitive to various perceived risks and were especially so during the COVID-19 pandemic. These perceived risks have major impacts on customers' intention to reuse the services of airline companies. High levels of perceived risk decrease customer trust, BE and repurchase intention rates, and vice versa.

This paper provides authorities and decision-makers of the air transport industry with several practical recommendations based on the research findings. As the results confirmed, there is a worthwhile relationship between BE and customer trust; regarding the both direct and indirect benefits of customer trust for for-profit firms and the huge importance of trust in creating competitive advantages, different airline companies need to implement various strategies to engage customers with their brand names, and thereby institutionalize trust in their customers' minds during the pandemic. Since customer trust was also found to improve repurchase intention rates significantly, authorities of the airline industry must take actions to reduce feelings of mistrust and risk among consumers and increase customer trust during COVID-19.

This paper is the first to explore the effect of BE on airline customers' repurchase intentions during the COVID-19 pandemic. It has produced practical suggestions to employees and activists of the air transport industry regarding the use of BE as a tool to restore customer trust during and after the COVID-19 epidemic. This research focuses on the airline industry, which is particularly interesting given the industry's intensely competitive environment and the dramatic effect of COVID-19 on the industry.

The thermophysiological comfort of fabrics is prerequisite as customers covet adequate moisture, heat management-supported and UV protective clothing that measure up to their levels of activities and environmental conditions. Hitherto, scant tasks have been reported with the purpose of engineering both comfort and UV protection simultaneously. From that vantage point, the objective of this work is to develop a model for optimum UPF, air permeability, water-vapour resistance, thermal resistance, thermal absorptivity and areal density of knitted fabrics.

Weft knitted fabrics of various compositions were investigated. UPF was tested using the Labsphere UV transmittance analyser. The FX 3300 (Textest instruments) air permeability tester was used to test air permeability. Thermal comfort and water-vapour resistance were evaluated using the Alambeta and Permetest instruments, respectively. Based on image processing, the porosity was measured. Fabrics thickness and areal density were measured according to standard methods. Furthermore, parametric and non-parametric statistical test methods were applied to the data for analysis.

Linear regression was substantiated by Kolmogorov-Smirnov test. Then multiple linear regression of porosity and thickness together on UPF and comfort parameters were visually depicted by virtue of 3D linear plots. Residual analysis with quantile-quantile and probability plots, advocated the tests using the Shapiro-Wilk test. The result was validated by comparison with experimental data tested. The samples gave satisfactory relative errors and were supported by the z-test method. All tests indicated failure to reject the null hypothesis.

The predictive models were embedded into an interactive computer program. Fabric thickness and porosity are the inputs needed to run the program. It will predict the optimum UPF, areal density and thermophysiological comfort parameters. In a nutshell, knitters may use the program to determine optimum structural parameters for diverse permutations of UPF and thermophysiological comfort parameters; scilicet high UV protection together with low thermal insulation combined with low water-vapour resistance and high air permeability.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

The border closure and lockdowns due to Covid-19 pandemic resulted in partial closure of many industrial and commercial complexes, halted the performance of key strategic sectors such as logistics and supply chains, and thus disrupted the global value chains and the economy. The authors argue, however, that the pursuit of survival has driven companies to innovate and use digitization to overcome the negative consequences of the pandemic. More specifically, in this paper the authors aim to assess the success and challenges faced by companies in digitization policy design, adoption and implementation and their effects on firms’ operation, outputs and customer base during Covid-19.

Sixty-one samples of the companies surveyed between 10 January and 30 April 2021 were analyzed, using the Krushkal–Wallis test and Independent-Samples Mann–Whitney U test to identify the relationships between variables including operation, overall output, customer base, digitization policy, technology use and implementation costs of new technologies.

Results revealed a positive impact of digitization on the operation and overall outputs, while no effect was observed on the customer base. Analysis also showed that only 1.8% of companies were able to fully implement digitization, and that the cost of technology prevented most companies from using emerging technology or implementing their digitization policy.

While the research has practical implications, it is not without flaws. For instance, the outcome of technology varies as per geographic area and people. The study was conducted in the Sultanate of Oman, a developing country in the Middle East region; therefore, it is difficult to generalize the outcomes suited to developed countries. The developed countries usually have a population quite used to the advanced technologies so some of the issues raised in the study might not work in the logistics and supply chain sectors of the developed countries. Such countries need separate studies.

The findings will have implications for both supply chain companies as well as the technology providers. The supply chain companies will invest in technology infrastructure and add technology as an important component in their business models. The technology providers will consider the costs of implementation and adoption issues of technology in the supply chain companies.

To the best of authors' knowledge, no work has been produced on logistics and supply chain companies considering the technological sustainability during the time of Covid-19. The study will improve understanding of the digitization policy design, adoption and implementation and their effects on logistics and supply chain companies’ performance.

Facilities management (FM) plays a key role in the performance of businesses to ensure the comfort of users and the sustainable use of natural resources over operation and maintenance. Nevertheless, reactive maintenance (RM) services are characterised by delays, waste and difficulties in prioritising services and identifying the root causes of failures; this is mostly caused by inefficient asset information and communication management. While linking building information modelling and the Internet of Things through a digital twin has demonstrated potential for improving FM practices, there is a lack of evidence regarding the process requirements involved in their implementation. This paper aims to address this challenge, as it is the first to statistically characterise RM services and processes to identify the most critical RM problems and scenarios for digital twin implementation. The statistical data analytics approach also constitutes a novel practical approach for a holistic analysis of RM occurrences.

The research strategy was based on multiple case studies, which adopted university campuses as objects for investigation. A detailed literature review of work to date and documental analysis assisted in generating data on the FM sector and RM services, where qualitative and statistical analyses were applied to approximately 300,000 individual work requests.

The work provides substantial evidence of a series of patterns across both cases that were not evidenced prior to this study: a concentration of requests within main campuses; a balanced distribution of requests per building, mechanical and electrical service categories; a predominance of low priority level services; a low rate of compliance in attending priority services; a cumulative impact on the overall picture of five problem subcategories (i.e. Building-Door, Mechanical-Plumbing, Electrical-Lighting, Mechanical-Heat/Cool/Ventilation and Electrical-Power); a predominance of problems in student accommodation facilities, circulations and offices; and a concentration of requests related to unlisted buildings. These new patterns form the basis for business cases where maintenance services and FM sectors can benefit from digital twins. It also provides a new methodological approach for assessing the impact of RM on businesses.

The findings provide new insights for owners and FM staff in determining the criticality of RM services, justifying investments and planning the digital transformation of services for a smarter provision.

This study represents a unique approach to FM and provides detailed evidence to identify novel RM patterns of critical service provision and activities within organisations for efficient digitalised data management over a building’s lifecycle.

The effects of board composition on performance have long been discussed in management research using fractionalization measures. In this study, we propose an alternative measurement approach based on board polarization.

Using an exploratory analysis and applying the polarization measure to German Deutscher Aktienindex (DAX)-, Midcap-DAX (MDAX)- and Small Cap-Index (SDAX)-listed companies, this paper applies the polarization index to examine the relationship between board diversity and performance.

The results show that the polarization concept is well suited to measure principal-agent problems between the members of the management and supervisory boards. We reveal that board polarization is negatively associated with firm performance, as measured by return on investment (ROI).

This exploratory study shows that the measurement of board polarization can be linked to performance differences between companies, which offers promising starting points for further research.

This research aims to explore how social media influences the academic performance of university students in Bangladesh and examines the benefits and drawbacks of its usage.

We distributed a well-structured questionnaire among students enrolled in various programs at different universities in Bangladesh to collect data. We analyzed the data using factor analysis and regression models to uncover the impact of social media on academic performance.

Our research findings demonstrate that social media plays a crucial role in facilitating communication, information sharing and content development among university students in Bangladesh. Excessive reliance on social media can lead to dependence and hinder innovation, as students tend to excessively rely on readily available resources.

We relied on self-reported data from a limited sample size, which may affect the generalizability of our findings.

This study highlights the need to promote responsible use of social media among university students in Bangladesh to enhance their academic performance. We recommend implementing effective policy measures to control and manage undesired usage patterns, foster an intellectually equipped student body and contribute to the development of a knowledgeable and successful Bangladesh.

This research makes a significant contribution by examining the influence of social media on academic performance among university students in Bangladesh. It proposes practical policy measures to address the drawbacks associated with excessive reliance on social media, thereby contributing to decision-making and intervention strategies for promoting responsible usage.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

This paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies.

The authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution.

The authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies.

This paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies.

Health tourism is a relatively new branch of international tourism that has developed more rapidly than other tourism sectors in recent years. This study aimed to investigate the effect of government supports on health tourism development by considering the mediating role of internal and external infrastructures.

The study population consisted of all experts of two hospitals in Iran, which are frequently visited by foreign tourists (N = 151). A questionnaire, developed by combining standard and researcher-made questionnaires, was used to collect the data. The confirmatory factor analysis (CFA) model was developed in SmartPLS 3 to test the hypotheses.

The main hypothesis test results indicated that government supports positively affect the development of health tourism. Internal and external infrastructures were also found to mediate the relationship between government supports and health tourism development. In addition, the sub-hypothesis test results showed that internal and external infrastructures are positively affected by government supports, which puts forth the development of health tourism. As the results explained, the most important aspects of internal infrastructures affected by government support were health service quality, cost of health services and applying advanced medical technologies, respectively. Also, different aspects of external infrastructures affected by government supports are as follows: economic, infrastructures and cultural factors.

This study is the first of its kind to examine the impact of both medical and non-medical factors on health tourism and signifies the crucial role of governments in the development of health tourism.