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This study explores tourists' virtual reality experiences during the transition to the Metaverse.

Qualitative approach was employed to capture tourists' virtual reality experiences and knowledge of the Metaverse at two five-star hotels in Kusadasi (Republic of Turkey). The data were collected from Kusadasi using a purposive sampling technique. The research design focused on data collection with the structured interview technique. The interview form consisted of 7 questions in total, and a voice recorder was used to record the answers of the participants. After the first 4 questions were asked, the participants were presented a virtual reality experience with the virtual reality (VR) glasses. The interview was held face-to-face with thirty-five participants consisting of domestic and foreign tourists in two five-star hotels in the summer season of 2022. The collected data were analyzed with the content analysis technique and themes were created.

This study's findings enhance the conceptual capital in this emerging field and provide insights into many of the participants who have and have never experienced virtual reality applications and who are familiar and unfamiliar with the Metaverse as a concept.

This study generates empirical data that informs contemporary debates about virtual reality and the Metaverse.

The findings show that most participants have never experienced a virtual reality application. Hotels and travel agencies should be aware of this new futuristic technology before the Metaverse transition. Metaverse is for generation Y and Z instead of Baby Boomers and generation X.

This study is unique in terms of depth and fills the gap as it provides useful insights regarding the evaluation of tourists' virtual reality experiences in the transition process to the Metaverse.

This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.