The aim of this study is to evaluate the customer satisfaction for mobile applications in the Turkish banking industry. For this purpose, the last 500 customer comments of 24 different Turkish deposit banks' mobile applications are analyzed with data mining approach. In this process, the most frequent one keyword, two keywords and three keywords are identified, and the most important dimensions are classified into four different categories. Secondly, IT2 fuzzy DEMATEL methodology is considered to weight these dimensions. The findings show that operational and usability are the most important dimensions regarding the customer satisfaction in mobile applications. This situation explains that customers give importance to the quality and variability of the services given by the mobile applications. Hence, it is recommended that different services, such as credit card payment and money transferring should be provided in these applications by the banks. Another important point is that these applications should be designed effectively so that the customer can easily make their operations.

The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.

Higher technological developments, product diversity, international trade, and population growth have greatly increased the energy demand of countries. It is very significant that this growing demand should be satisfied with a safe and accessible energy source. Because of this issue, it is thought that countries should be directed towards renewable energy sources so that these countries can meet their rising energy demand without increasing their energy imports. This chapter aims to identify the causal relationship between the use of renewable energy and energy imports. Within this framework, the data between 1990 and 2015 of E7 countries (Brazil, China, Indonesia, India, Mexico, Russia, and Turkey) is taken into the consideration by using the Pedroni panel cointegration method and the Dumitrescu Hurlin panel causality analysis. Results show that there is a long-term relationship between energy imports and renewable energy usage, but there is no causal relationship between energy imports and renewable energy usage. This situation gives information that the use of renewable energy is important and effective in order to reduce imports, but using only this method is not sufficient to remove the import problem for these countries.

In this chapter, consumer perceptions of augmented reality mobile applications will be emphasized and the analysis will be carried out through the mobile application markets of two different countries. In the research, the top 20 applications were selected from the UK and USA mobile application markets and the last consumer evaluations regarding these applications were obtained. In accordance with the purpose of the research, text mining methods were used to evaluate the expressions of consumers, since data mining methodologies can contribute to a better understanding of unstructured data. In the research, top words, bigram, and trigram are used in consumer comments. Then sentiment analysis method is employed to determine the emotions in consumer comments. Authors conclude that both markets have positive polarities. While the study provides a theoretical contribution in terms of consumer evaluations and new product perception, it also contributes to the sector in terms of expressions and evaluations used by consumers.

Sepsis infection, which is one of the most important causes of death in intensive care units, is seen as a severe global health crisis. If an early diagnosis of sepsis infection cannot be made, and treatment is not started rapidly, septic shock may result in multiple organ failure and death is almost inevitable. Therefore, it is vital to establish an early diagnosis and start the treatment at once. This study aims to accomplish a new model of unsupervised machine learning using lactate and Ph laboratory test values, which are considered to be important parameters to diagnose sepsis infection. The data used in the study have been obtained from MIMIC-III international clinical database. Unsupervised machine learning has been performed via the Fuzzy-C algorithm along with validity indexes like Xie Beni on patients' data diagnosed sepsis and non-sepsis. The machine-generated ten labels at the end of the training session considering-designed validity indexes. The labelled cluster representatives have been reduced to two dimensions by Principal Component Analysis method in order to monitor the learning in a two-dimensional space. The study contributes to the literature by conducting unsupervised learning through two parameters (Lactate and Ph) and leading to multi-parameter studies. In addition, the study reports that there are five types of sepsis patterns in terms of Lactate and PH laboratory tests.