Data-Driven Decision Making in Entrepreneurship

Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019 alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days when organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research is primarily conducted in business schools. There needs to be more integration of industrial-organizational psychology and entrepreneurship. This book was designed to do just that: to demonstrate how organizational psychology — with a focus on human capital data and analytics — can advance the science and practice of entrepreneurship. 　

This book is purposefully designed to address the unique idiosyncrasies of the science, research, and practice of startups and the entrepreneurial ecosystem. Each chapter takes a science-practice perspective, highlighting a specific human capital management topic (e.g., learning and development, team effectiveness, human capital due diligence) and discusses how leveraging data can help enhance decision-making. The volume is grounded in sound theory and practice of organizational psychology, entrepreneurship, and management. It is divided into three parts: (1) human capital assessment and development for startups, (2) understanding startup situations, environments, and support systems, and (3) measuring startup-level performance.