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"This edited volume brings together some of the best papers from the 2022 Conference on Partial Least Squares Structural Equation Modeling (PLS-SEM), held at the Babeș-Bolyai University, Cluj, Romania. The volume seeks to expand the current research on PLS-SEM and promote the method’s application in the scientific community. It gathers research from scholars in many different fields who work on the advancement of PLS-SEM and who apply the method to explain and predict behavioral phenomena.Researchers today can draw on a wide array of different PLS-SEM-based algorithms, complementary methods, and model evaluation metrics. Tying in with these developments, the first part of this book documents methodological advances of PLS-SEM, which extend the researchers’ current toolbox of methods. The following parts demonstrate state-of-the-art applications of PLS-SEM in various fields such as consumer behavior, hospitality, human resource management, entrepreneurship, and organizational behavior. Special emphasis is placed on studies that apply complementary methods to offer a more nuanced analysis of the research questions."

"Pengetahuan tentang elemen-elemen yang dapat meningkatkan pengalaman pengguna (user experience/UX) ketika menggunakan sistem operasi merupakan hal yang penting. Hal ini dibutuhkan bagi pengembang sistem operasi untuk membangun sistem operasi yang baik dari segi fungsi atau kegunaan (usability), dan juga dari segi pengaruh tampilannya (affect). Pendekatan yang digunakan dalam penelitian ini untuk mendapatkan elemen atau indikator tersebut adalah dengan melakukan pemodelan (PLS-SEM). Dimana hingga saat ini pendekatan UX secara kuantitatif, khususnya pemodelan, masih sedikit. Penelitian dilakukan dengan memberi tugas-tugas tertentu terkait dengan aplikasi Windows 10 Technical Preview (sebagai representasi) kepada subjek penelitian. Hasil penelitian menunjukkan bahwa pada masa orientasi, pengaruh tampilan (affect) secara subjektif merupakan hal yang lebih penting dari kegunaan (usability) objektif untuk meningkatkan penilaian pengguna (user value) dan pengalaman pengguna secara keseluruhan (overall UX). Lalu, dari masing-masing elemen UX yang terdiri dari usability, affect, dan user value tersebut, didapatkan indikator-indikator yang dapat menjadi prioritas dalam membangun sebuah sistem operasi. Sebagai tambahan, dilakukan retrospective think aloud untuk mendapatkan umpan balik pengguna.

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Knowledge about elements that can improve user experience when using an operating system is important. It is needed by operating system developer to build the operating system that is good from its usability, and also from its display affect. Approach that is used in this research to get the element or indicator is by doing the modeling (PLS-SEM). Whereas until this day quantitave UX approach, especially modeling, is little. The research is did by giving particular tasks about Windows 10 Technical Preview applications (as representation) to research subject.

Research result shows that in orientation state, subjective affect from display is more important than objective usability to increase user value and overall user experience. Then, from each UX elements that are usability, affect, and user value, it is obtained each indicators that can become priority when building up operating system. As an addition, retrospective think aloud is conducted to get feedback from user."

"Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. "