The theme parks, whose number has increased rapidly since the end of World War II, are frequently visited by visitors who want to spend their leisure time in recent years. The theme parks, which are pioneers among entertainment centers with the development of the experience economy, should give importance to satisfaction to create competitive advantages in terms of visitor satisfaction. Satisfaction is a very key factor, especially in the competitive environment in the entertainment and tourism industry. So, entertainment and tourism businesses should determine well how to provide and measure satisfaction. This research focuses on identifying key criteria that affect theme park satisfaction. Satisfaction criteria were determined by using the literature and some contributions of the authors. The criteria were evaluated by asking the importance levels of the satisfaction criteria to the visitors who experienced the theme park. Level Based Weight Assessment (LBWA) Model, a new model, is used in multi-criteria decision-making methods in evaluating the determined criteria and determining the weight coefficients. With the findings of the study, the criteria that affect customer satisfaction were determined. To achieve long-term success and competitive advantage, it is necessary to focus on customer satisfaction criteria. Therefore, among the criteria determined, it is recommended that theme park managers should necessary to focus more on which criteria. This study presents results that should be taken into account in solving problems, improvement plans, and marketing strategies.

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Retailing is considered to be one of the sectors that have been growing in recent years and making significant contributions to the country's economy. In this sector where competition is intense, firms have to evaluate their financial performance in order to compete and see their current situation in the sector. In this study, it is aimed to identify the financial performances of firms in the BIST retail trade sector with FUCOM and VIKOR, which are multi criteria decision making (MCDM) methods. Financial ratios were used to determine the financial performances of the three firms with the most branches in the BIST retail trade sector between the years 2019-2021, and the weights of these financial ratios were determined by the FUCOM method. The performance ranking of retail trade firms was made using the VIKOR method using the weights obtained.

Market power in electronic marketplaces is increasingly developing into a monopoly. Digital pioneers like Amazon and eBay do not want to give up their market share easily. Especially, Small and Medium Enterprises, which try to offer their services online, have great difficulties to gain market share. To understand the causes, we start a systematic literature review. The results shows that building trust is a necessary precondition for success in e-commerce. For many small and medium-sized companies, entering e-commerce market is a completely new business field. Therefore, they lack the knowledge and experience to successfully build online trust. To solve this problem, we use Design Science Research approach and develop a Trust Guide for online trust building. The Trust Guide is a set of design principles derived from trust theories. Our aim is to give Small and Medium Enterprises practical guidance and help them to successfully build online trust in e-commerce.

In recent years, digital innovations especially emerged depend on Blockchain technology have caused a substantial transformation in the finance sector as in other sectors. Different financial assets have been revealed and began to be used as an investment tool along with this transformation in the markets. Cryptocurrencies that have a digital structure hold an important place among these assets. Dramatically increases in the daily transaction volume of currencies in the market have brought along different types of risks. These risks raised uncertainty on these currencies. Moreover, because cryptocurrencies are mostly used for the purpose of investment and speculation, it is important to understand the volatility movements and co-movements of cryptocurrencies and is substantially important, particularly because volatility can influence investment decisions. This study aims to determine the volatility transmission between cryptocurrencies to find useful answers about the volatility and the efficiency of markets. Daily logarithmic return series between 18 January 2018 – 14 February 2021 were used to analyze the volatility of five of the most common cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), IOTA by applying the RALS-ADF test, EGARCH, and DCC-GARCH models. We determined whether the market is efficient or not, and tested the existence of the asymmetric effect and volatility transmission in the market. According to our results, volatility shocks are not obtained persistent for only BTC. Furthermore, the presence of asymmetric effects and leverage effect valid for four cryptocurrencies. While asymmetric effects observed for BTC, no leverage effect has been observed during the period. We also analyzed nine pair-wise cryptocurrencies applying the DCC-GARCH model and we found that dynamic conditional correlation coefficients are statistically significant and positive for each pair.

The verbal definition of the sample median sounds a bit strange in the early Statistics courses in the context of being non-mathematical or non-functional. It is also interesting that estimators based on an analytical calculation such as the sample mean are equally strange, but not seen as strange by the students as the median estimator. In this study, we have expanded the studies on teaching the sample median with its optimization definitions. We have also shown that such definitions provide a natural way of understanding the sample median in multivariate case and regression analysis. Seeing that statistical estimators, from the simplest to the most complex, are obtained as a solution to an optimization problem can pave the way for other types of insights.

Along with technological innovations and developments experienced in the second half of the twentieth century, very important changes have occurred in healthcare. Many different, complex and economically expensive services are being tried to be carried out together. For this reason, it is finally crucial that the health services delivered by providers to scarce resources are delivered effectively and efficiently to people without sacrificing quality. Today, the most important problem of the production of healthcare services is the resource shortage as it is in other sectors. Efficiency, quality and competition are important criteria in the production and delivery of health services. Reducing costs in the production of health services is one of the main health policies for many world countries. These policies have made it necessary for international competitiveness, product and service sectors to continually improve their performance. In this study, the efficiency evaluation of the health care services of OECD were examined using Data Envelopment Analysis and Malmquist Total Factor Productivity. Considering the input values of the countries, the results of the input-oriented model and Malmquist productivity index were analyzed with the fixed return assumption according to the scale.

With the growth of the tourism industry, tourism demand forecasting has become an important research topic. Recently researches have shown that Google Trends(GT) data with the help of Google can positively affect the forecast of tourist arrivals. However, the use of this data directly can cause some errors. This article provides suggestions on how the calculation differences according to the same time at different time intervals in GT data (which is obtained on an hourly, daily, monthly and yearly basis) can be eliminated. In this study, it is aimed to examine the effect of GT data for Antalya, Turkey's favorite tourist destination by the Russians. In addition, the multivariate time series models are used to see separately and together the effects of international trade (IT), weather conditions (WC) and number of flights (FN) variables on tourism data, as well as GT data. As a result, it has been seen that the tourist arrival can be forecasted better with the GT (AGT) data, which is recommended to be used by adjusted.

Artificial neural networks, is one of the most preferred artificial intelligence techniques in the modeling of complex systems today and the models are based on the working structure of the nerve cells in the human brain. Autism spectrum disorder is a complex neuro-developmental disorder that is congenital or occurs at an early age. Since early diagnosis has a very important role in the treatment, there are many studies on this subject. In this study, a subset of current autism spectrum disorder data obtained from UCI machine learning repository for adolescents has used. In order to test the success of the model, after the necessary preprocesses have performed on the data set, the data has separated into training and test set and classified with the trained network. As a result, 100% accuracy rate in the training set and 96.77% accuracy rate in the test set are achieved. Sensitivity, Specificity and F-measure values obtained in the test set are 0.94, 1.0 and 0.97, respectively and reveals the model success.

This study aims to create an index, which will be used for monitoring financial stability for Turkey. For this purpose, firstly the concept of financial stability was defined. As a result of the literature review, the variables considered to give information about financial stability were examined and the variables were grouped by principal component analysis. Finally, generated factors were subjected to logistic regression. The variables which were examined in the study include a total of 57 quarters, which is between the second quarter of 2003 and the second quarter of 2017. Three different dependent variables were used in the logistic regression analysis, each of which was formed by taking into consideration the “break points” in the independent variable series. All variables included in the financial stability index were standardized. The financial stability index was formed with the results obtained by the logistic regression analysis, and then; the contribution of the variables to financial stability was interpreted. It will be possible that the stakeholders in the financial system have information about the expected change in the level of financial stability by monitoring the variables which were examined and used in the analysis.