Skip to main content

White paper characteristics and initial coin offerings (ICO) campaign success

Date
Date
Wednesday 27 February 2019, 14.00 - 15.00
Location
1.44 Maurice Keyworth
Speaker
Xingjie Wei
Who can attend
Staff, students, alumni and external guests

Abstract

An initial coin offering (ICO) campaign is a process in which companies raise capital, but giving the investor a cryptocoin, more commonly known as a coin or a token in return for investment. ICO campaigns occur only online. Ventures only communicate with investors by providing limited documents (e.g., white papers) on the campaign site. Despite attracting significant attention from ventures, investors, and policy makers, little is known about the dynamics of ICOs. This on-going research examines whether the style of white papers affects investors’ investment decision making and to some extent determines the ICO campaign success. Especially, we focus on analysing the visual features of white papers (e.g., images) as a form of communication intermediation in a digital crowd founding environment. This study aims to help reduce the uncertainty of ICO campaign and enable investors to make better investment decisions.

About the speaker

Dr. Xingjie Wei is a Lecturer in the Centre for Decision Research, Leeds University Business School, University of Leeds. She received the Ph.D degree in Computer Science from University of Warwick and worked as a Research Associate in University of Cambridge.

Xingjie has been working in Computer Science, Psychology and Business School departments doing interdisciplinary research investigating the relationship between human factors and computational techniques, and how humans interact with new data technologies. Xingjie’s research interests lie in the interaction between computational techniques and social science approaches to understand human behaviour, which can be applied to applications for business, management and policy. Specifically, her work focuses on analysing and predicting behavioural and psychological traits (e.g., personality, interests) of human by psychological experiments, big data analytics, and machine learning modelling.