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This study explores how auditors' emotions, specifically negative moods triggered by flight delays, impact auditing quality.

Utilizing flight delays during audit assignments as a mood indicator, weather conditions at departure airports serve as an instrumental variable to provide a robustness check between flight delays and audit outcomes, employing a two-stage least squares model.

The findings suggest that such negative moods improve auditing effort and quality, as evidenced by reduced future accounting restatements and increased audit fees. The positive effect of flight delays on auditing quality is consistent across different tests and measures.

This study highlights the significance of auditors' emotional states on their professional performance, indicating a unique angle on auditing quality research by focusing on the emotional well-being of auditors as influenced by external factors such as flight delays.

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

This study attempts to answer how values and holiday preferences were shaped by the pandemic, how travellers view the future of tourism and how they are willing to contribute to potential changes. Furthermore, it examines the impact of socio-structural background factors, basic values and holiday preferences, and pandemic-related factors on the views of post-pandemic tourism.

A longitudinal online survey was conducted in which 155 frequent travellers were interviewed both before and during the pandemic about their values and holiday preferences, attitudes towards travelling during the pandemic, and their prospective views regarding tourism.

The findings revealed that values remained rather stable, but nature experiences, heritage tourism and beach offers gained more relevance when it came to holiday preferences. Concerning travellers’ expectations of future tourism, environmental concern was ranked higher than economic profit. However, those striving for self-direction, stimulation and city tourism offers stated to be less willing to restrict their travel behaviour in the future.

Although our study is just based on a convenience sample, the authors were still able to address notable research gaps. First, because a longitudinal design was selected, it was possible to investigate any potential transitions in basic values and travel style and trace these changes back to the pandemic. Second, thanks to a sophisticated online survey, all concepts could be measured with well-developed scales, which increased the quality of the measurements and led to stable results. Third, young travellers can be considered proponents of future travel styles. Their way of acting and thinking about future tourism could significantly impact the prospective direction of tourism.

This study makes a valuable contribution to changing holiday preferences and provides useful insights for the tourism industry about travellers’ willingness to change their travel behaviour.

Since this study primarily considers human values and socio-structural factors, the findings are of particular interest from a sociological perspective and are also interpreted from this viewpoint.

This study is one of only a few longitudinal studies focusing on holiday preferences and shifting values during COVID-19 and attempting to detect crucial drivers of potential tourism transformations in terms of perceptions from the demand side.