This book offers a comprehensive journey through the evolving world of cryptocurrencies and their intersection with modern econometric forecasting. From the origins of Bitcoin and the rise of decentralized finance to the emergence of smart contracts and digital communities, readers gain a deep understanding of blockchain’s economic and societal impact. Bridging theory and practice, the book explores ARIMA models, machine learning, and neural networks as powerful tools for forecasting in a data-driven economy. It also addresses critical issues such as model interpretability, ethics, and regulatory challenges. Ideal for economists, data scientists, and crypto enthusiasts alike, this work emphasizes the importance of interdisciplinary thinking in navigating the future of finance, technology, and economic modeling in an increasingly digital world.

SMEs have been recognized as important actors of commercial activity most specifically since the second half of the 20th century. Although understanding its customer potential has been named a top priority among many sectors, there is a limited body of knowledge regarding the buying behaviour of SME’s. Many studies and industry practices frame the context within corporate or individual buying behaviour, however few researchers mention SME’s. When mentioned they tend to tie with elitist attributes and consider them as small versions of big enterprises. Studies and industry practices are conformist and assume that SME’s buying behaviour consists of nothing but -ignoring impulsive tendencies- ‘normative’ or ‘conservative’ typologies. However, the lack of application makes this dichotomy untested, which is also what this book aims to scrutinize.