Globalization and related technological developments have increased competition and put organizations in a struggle to survive in an extremely difficult market condition. Organizations adopt their technologies and infrastructures to today’s conditions to gain a sustainable competitive advantage and maintain their existence. However, all these efforts of organizations are easily imitated by rival companies and may cause the loss of competitive advantage.

The resource-based view provides additional information that strategic human resource is the scarce, valuable, inimitable and non-substitutable resources of organizations, thus enabling them to gain a competitive advantage. According to this view, the competitive advantage of the organization is achieved by combining valuable internal resources. Nowadays, sustainable competitive advantage is provided not by environmental factors or physical capital but by human capital (employees) efficiency. In this context, the attitudes and behaviors of employees who make up human resources are important for their organizational success. Many studies emphasize that positive attitudes and behaviors (e.g. innovative work behavior, work engagement, job satisfaction) in employees have important contributions to the success of the organization, while similarly negative attitudes and behaviors (e.g. organizational silence, turnover intention) are one of the main reasons for the failure of the organization.

In our book, literature has been created on the definition, antecedents, and organizational results of new and current concepts that are newly conceptualized in the literature and are still in their infancy. It is considered that this cumulative literature within the book will be a source for future studies on these subjects and will provide important insights for practitioners.

Intense competition, fast changing technology, increased and diverse customer demand, and fast and reliable services and goods delivery expectations have reduced the profit margins for products. To be able to obtain a sustainable economic success, companies search for ways to offer the existing products via innovative ways. Revenue management is a tool that helps firms in this purpose. On the other hand, to match and even exceed customer anticipations, firms need to understand customer preferences and offer products accordingly. Customers can now reach wide range of competitor products due to online services. Hence it became much more important to apprehend and integrate customer choices into decision support tools. At this point, choice models are used to mathematically understand the customer choice preferences among a set of presented alternatives. This book combines revenue management and choice models to present a methodology to understand customer choices and integrate these to revenue management methods to be able to offer the best set of products to the customers. Also real life applications for hotels have been presented for different methods that are discussed and also proposed in this book with respective algorithms. When combined with decision support tools, concepts presented in this book offers a significant revenue increase for companies.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.