Huiwen Wang – författare

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field.

Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field.

Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

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.