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This chapter provides an overview of entrepreneurship clubs. It charts the development of these organisations, as a form of extracurricular activity. It introduces different forms of entrepreneurship clubs, such as Junior Achievement (JA) and Enactus, and explains how they grew from 1919 to the present. It also illustrates the differences between self-organised clubs, organised programs using clubs as a learning method, structured societies and nationally organised cooperative societies. The second part introduces research on student clubs in entrepreneurship education. It explores the benefits of clubs. It shows that clubs assist student learning, enable the acquisition of practical skills and improve college attendance, employment opportunities and career attainment. We argue that entrepreneurship clubs have improved student learning outcomes in entrepreneurship and simulated entrepreneurial learning, while impacting student self-efficacy and intentionality as well as improving employability and social learning. The final part of the chapter provides advice and tips for educators advising student-run entrepreneurship clubs. Ultimately, the chapter explains how student clubs have developed, why they are important for student learning and how advisors can support them.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.