Measurement Scale

This book goes into the details of the characteristics of scale-ups, touching upon various theoretical perspectives (stages of growth models, the role of intangible capital, the tradeoffs between fixed costs and marginal costs) relating to firm growth, to provide a clear definition of this emerging concept. Given the disproportionately large economic contribution of a small number of fast-growing firms, there has been growing interest in a category of dynamic ventures called "scale-ups". The term was popularized by Silicon Valley Venture Capitalists before receiving attention from academics and public policy makers. However, there remains considerable confusion regarding what a scale-up actually is.

This book focuses on the following three key topics in social network large-scale decision-making: structure-heterogeneous information fusion, clustering analysis with multiple measurement attributes, and consensus building considering trust loss. To address the aggregation and distance measurement of structure-heterogeneous evaluation information, we propose a fusion method based on trust and behavior analysis. Then, two clustering algorithms are put forward, including trust Cop-K-means clustering algorithm and compatibility distance-oriented off-center clustering algorithm. The above clustering algorithms emphasize the similarity of opinions and social relationships as important measurement attributes of clustering. Finally, this book explores the impact of trust loss originating from social relationships on the CRP and develops two consensus-reaching models, namely the improved minimum-cost consensus model that takes into account voluntary trust loss and the punishment-driven consensus-reaching model. Some case studies, a large number of numerical experiments, and comparative analyses are provided in this book to demonstrate the characteristics and advantages of the proposed methods and models.