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The construction industry is one of the most stressful industries. Thus, quantity surveyors (QSs) who work at sites frequently experience high levels of occupational stress. The gender of a QS also has a significant impact on his/her occupational stress. Hence, this study aims to investigate the management of occupational stress in QSs working at sites for contractors (hereinafter referred to as CQSs).

The study adopted a mixed approach using semi-structured interviews and a questionnaire survey for female and male CQSs to identify, validate and rank the stressors and symptoms of occupational stress in CQSs and the strategies of managing that stress based on their significance levels. Manual content analysis and the mean weighted rating were used to analyse the data collected.

Heavy workload was the most significant occupational stressor of CQSs, whereas sleeping disorders were their primary symptom of occupational stress. Establishing a proper work programme was identified as the most effective stress management strategy for male and female CQSs. This study shows that many site QSs are stressed owing to their heavy workloads and work obligations and that their stress-related attributes significantly depend on their genders.

This study is significant because no previous studies have been conducted on managing occupational stress in CQSs in male and female CQSs. The study findings can be used to identify the stressors and symptoms of occupational stress in CQSs early and use appropriate management strategies to enhance the work satisfaction and productivity of CQSs suffering from occupational stress.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.