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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 paper aims to explore financial anxiety and generalized anxiety as the serial mediators linking perceived organizational support (POS) to career commitment.

Data were collected from 388 managerial and nonmanagerial employees in diverse service areas, such as restaurants, airlines and hotels in Turkey. The direct and mediating effects were tested via the PROCESS macro.

Financial anxiety partly mediates the impact of POS on career commitment. The findings further reveal that financial anxiety and generalized anxiety serially mediate the effect of POS on career commitment.

Management should work with mentors to provide employees with psychosocial support during the COVID-19 pandemic. When employees perceive that the firm really cares about them and values their contribution during these challenging days, they display lower anxiety and higher career commitment. Management should also retain employees who are high on career commitment because such employees possess a sense of calling and are unlikely to quit. These implications may not be considered new. However, management would need such employees concerning the firm’s performance recovery after COVID-19.

Workers in the service industries suffer from financial and generalized anxieties and display reduced career commitment during COVID-19. However, little is known about the antecedents and outcomes of financial anxiety among hospitality and tourism workers. More importantly, no empirical piece has tested these anxiety variables as the mediators linking POS to career commitment in the pertinent literature so far.

This study aims to measure the effect of organizational culture perceptions of hotel employees on their organizational silence behavior and job performance, as well as the effect of their organizational silence behavior on their job performance.

A correlational survey model was used in this research and a questionnaire was distributed to collect the data from 389 sampled employees working in four- and five-star hotels in the Kusadasi region in Turkey.

It was found that organizational culture was not significantly related to organizational silence but that organizational culture and its dimensions were significantly related to job performance. It was also revealed that while organizational silence was not significantly related to job performance, its dimensions were significantly related to job performance.

The results of this study provide insight into organizational culture as an important factor in increasing job performance. The study also revealed how organizational silence behavior and its dimensions affect job performance. In this sense, accommodation establishments will be able to acquire new perspectives in terms of improving job performance.

This paper is deemed important, as it examined these three terms in one model in the field of tourism management. It is thought that it will contribute to the literature by closing the gap in the tourism literature while leading the way for future studies.

Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.

This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.

The most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.

Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.