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This study aims to investigate the impact of the transformational leadership style on the behaviour of restaurant employees. Also, it was aimed to investigate the effect of transformational leadership on trust and the effect of trust on altruistic intention and organizational commitment.

The study integrates insights from transformational leadership to provide a fresh perspective to advance comparative organizational behaviour research. To test the hypotheses, the authors conduct a multiple analysis with observations from Turkey getting staff in culinary department with a quantitative survey.

This study equips different professional entities in the food and beverage industry with useful, contextualized links between transformational leadership. According to results, the perspective of transformational leadership style affects the concepts of trust, altruistic value and organizational commitment positively. Charisma, moral modelling and individualized consideration had a significant effect on trust. Also, trust has a significant effect on altruistic intention and organizational commitment.

The present study incorporated confidence as a mediating variable; however, it is recommended that alternative scales be used in subsequent research endeavours. Future research endeavours may incorporate theoretical frameworks such as theory of planned behaviour or stimulus-organism-response.

Transformational leadership style is a good acquisition for restaurant employees. There is a healthier and safer job sharing in these restaurants. This can be interpreted as a more satisfied customer. A good leader has a great contribution to the future and sustainability of the business.

This research created a new model and examined employees’ views on the company and its management. As a result of the analysis, it was determined that charisma, moral modelling and individualized consideration had a significant effect on trust.

This assists in learning better service quality developing and business practices to augment culinary staff, thereby maximizing their valuable contributions to tourism growth. This research created a new model and examined employees’ views on the company and its management.

This study aims to measure whether admittance in the quarantine ward due to COVID-19 can affect one’s mental health. Nowadays, many countries worldwide are battling with the threat of the COVID-19 contagion, and it is difficult to understand how the pandemic leaves psychological impacts on one’s well-being.

This research used qualitative and quantitative approaches to assess the psychological impacts of quarantine due to COVID-19. Population of the present study were 250 patients who were admitted in quarantine centres of Pakistan. The data analysis was conducted through univariate analysis using (ANVOVA) software.

This study found that patients who were quarantined due to the COVID-19 infection displayed multiple psychological symptoms such as a lack of self-control, anxiety, low general health and vitality, depression and negative well-being.

There is an urgency to provide psychological treatments to each afflicted person and their family members to establish a healthy community.

This research investigates whether admittance in the quarantine ward due to COVID-19 can affect mental health in Pakistan.

The purpose of this paper was to investigate the prevalence rates of stress, depression and anxiety and their sociodemographic factors linked with the Indian population following the second round of COVID-19 in India.

A cross-sectional study was carried out using an online questionnaire. In total, 505 individuals participated through convenience sampling. To measure anxiety, depression and stress, the Depression Anxiety Stress Scale (DASS-21), a 21-statement self-reported questionnaire, was used.

Multiple regression analyses were performed to evaluate the sociodemographic characteristics associated with depression, stress and anxiety. Results indicated salary/allowances reduction and alcohol consumption were associated with depression. Multiple regression also indicated that salary/allowances reduction, smoking status and alcohol consumption were associated with stress. In addition, this research also showed that chronic disease, salary/allowances reduction, smoking status and alcohol consumption were associated with anxiety.

During the second COVID-19 wave in India, various individuals were affected. Anxiety, depression and stress were common among Indians after the second wave of COVID-19. Along with other actions to restrict the development of COVID-19, the Indian Government and mental health specialists must pay close attention to the inhabitants' mental health. More large-scale studies on various occupations should be conducted, and new mental health factors should be included.

This study provides empirical insights related the sociodemographic factors and stress, anxiety and depression.

This qualitative study aimed to explore the barriers and facilitators faced by workers during the COVID-19 pandemic restrictions in Ecuador. It focused on three work modalities: on-site, telework and mixed or hybrid. It also inquired into practical implications for management based on the workers’ experiences.

Between October and December 2021, 41 semistructured interviews were conducted to delve into the experiences of Ecuadorian workers. Thematic content analysis was employed for data charting and analysis.

Barriers and facilitators varied according to each working modality, although some were shared, contingent upon the specific contextual factors and job characteristics. The findings suggest that organizations should consider implementing flexible working hours and modalities, provide safe workspaces, ensure appropriate technology and connectivity, support employees and maintain their health and well-being.

This research explores the experiences of teleworkers, on-site workers and hybrid workers during the COVID-19 pandemic in an under-researched labor market within a developing country. The study provides valuable insights that highlight the potential for management development initiatives specifically tailored to hybrid work environments.

This purpose of this study was to investigate the role of nurses’ resilience as an indicator of their mental health on sick leave absenteeism during the COVID-19 pandemic.

This descriptive-analytical study was conducted in 2020 to identify the predictors of absenteeism among 260 nurses working in two training hospitals delivering specialized services in the treatment of COVID-19 patients. Data was collected through the use of standard questionnaires including demographic information, nurses’ resilience, intention for job turnover and absenteeism from the workplace. To predict sick leave absenteeism, regression analyses were implemented.

Study results revealed that the most influencing features for predicting the probability of taking sick leave among nurses were marital status, tenacity, age, work experience and optimism. Logistic regression also depicted that nurses who had less faith in God or less self-control were more likely to take sick leave.

The resilience of nurses working in the COVID-19 pandemic was relatively low, which needs careful consideration to apply for organizational support. Main challenge that most of the health systems face include an inadequate supply of nurses which consequently lead to reduced efficiency, poor quality of care and decreased job performance. Thus, hospital managers need to put appropriate managerial interventions into practice, such as building a pleasant and healthy work environment, to improve nurses’ resilience in response to heavy workloads and stressful conditions.

To the best of the authors’ knowledge, this is the first study to examine such a relationship, thus contributing findings will provide a clear contribution to nursing management and decision-making processes. Resilience is an important factor for nurses who constantly face challenging situations in a multifaceted health-care system.

Considering the world population, an additional 415.1 billion m² of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.

A mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.

This study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.

This study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.

This study aims to investigate the impact of job loss on the mental health of individuals in Tunisia during the COVID-19 crisis.

In this research, the authors use the counterfactual decomposition technique and the potential outcome approach. In the first part, the authors calculated mental health indicators for all individuals included in the sample based on the World Health Organization-5 items. The individuals were then grouped into two subpopulations: the first group included those who had lost their jobs and the second group included individuals whose status in the labor market had remained unchanged. In the second part, the authors used the Blinder and Oaxaca decomposition to explain the mean difference in the mental health scores between the two groups and determine the factors contributing to this difference.

The empirical results identified symptoms of depressed mood, decreased energy and loss of interest in several individuals. Based on these three symptoms, the authors were able to classify individuals into three types of depression: mild, moderate and severe. In addition, it appeared that job loss had significantly contributed to the worsening mental health of the individuals.

Although the psychological impact of the COVID-19 outbreak among health-care professionals has been the subject of other studies in health literature on Tunisia, to the best of the authors’ knowledge, no research has addressed the impact of job loss on the mental health of Tunisian workers. Thus, this study fills this gap in the literature.

During the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.

The authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.

The authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.

From the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.