Traffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010–2017 in Sarıyer district of Istanbul, Turkey’s largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran’s I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.

This study aims to assess both the stress levels of employees and identify the sources of stress. Data for the research were collected through questionnaires distributed to 60 employees via online surveys. The instruments utilized for data collection included the personal information form, organizational stress scale, and stress coping scale. The initial phase of data analysis focused on revealing the demographic characteristics of the workers. Subsequently, analyses were conducted to explore potential correlations between organizational stress, stress-coping mechanisms, and demographic variables. While socio-demographic variables are commonly treated as control variables in organizational stress research, this study distinguishes itself by placing them at the forefront. Its objective is to comprehend the influence of gender, age, educational background, marital status, income levels, titles, and total work experience on individuals' perspectives regarding organizational stress and stress coping. Examining survey data from Assist Company, a subsidiary of Turk Telekom, the study uncovers that male employees tend to display greater resilience to organizational stress. Additionally, unmarried workers demonstrate a higher efficacy in coping with stress compared to their married counterparts. The paper concludes with implications for managerial applications, theoretical insights, and suggestions for future research directions. In conclusion, the study recommends that firms consider implementing stress coping methods, techniques, and programs. Furthermore, it suggests organizations identify the specific factors within their structure that contribute to stress among employees.

Hotel businesses have an important place in the tourism sector. Hotel businesses want better customer service by meeting their demands and expectations, thus attracting more customers than rival businesses. One of the most critical factors in tourism enterprises' customer competition is providing quality service. For this purpose, it can benefit from the Kano model. The Kano model is a practical approach that tries to understand customer satisfaction by categorizing the expectations of the customers. In the study, the Kano model was applied in the hotels of a hotel chain in Istanbul. In addition, the average satisfaction coefficient (ASC) values were calculated to determine the important levels of service qualities. According to the results obtained, it has been observed that customer expectations are in the one-dimensional (O) and attractive (A) categories. In addition, the reception staff's importance of customer privacy was the most valued service quality by the customers. Request access from your librarian to read this chapter's full text.

The housing price index is one of the key economic indicators reflecting changes in housing prices in a country or region. This index is of critical importance, particularly for investors and financial institutions, in making short- and long-term investment decisions. Various macroeconomic variables affect the housing price index. Therefore, examining the effects of macroeconomic factors on the housing price index is of great importance. In this study, using data from the period 2020:4–2024:12, the effects of macroeconomic variables such as the consumer price index (CPI) and the industrial production index (IPI) on the TR3 region housing price index (HPI3) were analyzed using the ARDL bounds test method. According to the long-term analysis results, the CPI variable has a negative effect on HPI3 at a 10% significance level, and a 1% increase in CPI reduces HPI3 by 0.49%. The IPI was found to have a positive effect on the HPI3 at the 5% significance level, with a 1% increase in the IPI increasing the HPI3 by 7.98%. The error correction coefficient was calculated as -0.17, indicating that short-term imbalances will return to long-term equilibrium within approximately 6 months. In the short-term analysis, the current, one-period, and three-period lagged values of CPI were found to have positive and significant effects on HPI3. These findings reveal that inflation may have a positive effect on housing prices in the short term.