Entrepreneurial Complexity: Methods and Applications

Matthias Dehmer

ISBN 10: 0815370016 / ISBN 13: 9780815370017

Published by Taylor & Francis Inc, 2019

About this Item

This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Seller Inventory # C9780815370017

Bibliographic Details

• Title: Entrepreneurial Complexity: Methods and Applications
• Publisher: Taylor & Francis Inc
• Publication Date: 2019
• Language: English
• ISBN 10: 0815370016
• ISBN 13: 9780815370017
• Binding: Hardback
• Condition: New
• Seller catalogs:
  • Mathematics

About this title

Synopsis

Entrepreneurial Complexity: Methods and Applications deals with theoretical and practical results of Entrepreneurial Sciences and Management (ESM), emphasising qualitative and quantitative methods. ESM has been a modern and exciting research field in which methods from various disciplines have been applied. However, the existing body of literature lacks the proper use of mathematical and formal models; individuals who perform research in this broad interdisciplinary area have been trained differently. In particular, they are not used to solving business-oriented problems mathematically. This book utilises formal techniques in ESM as an advantage for developing theories and models which are falsifiable.

Features

• Discusses methods for defining and measuring complexity in entrepreneurial sciences
• Summarises new technologies and innovation-based techniques in entrepreneurial sciences
• Outlines new formal methods and complexity-models for entrepreneurship
• To date no book has been dedicated exclusively to use formal models in Entrepreneurial Sciences and Management

About the Author

Matthias Dehmer is a professor at the University of Applied Sciences Upper Austria, Steyr School of Management and UMIT – The Health and Life Sciences University in Austria. He also holds a guest professorship at Nankai University, College of Artificial Intelligence in China. His research interests are in graph theory, complex networks, complexity, data science, machine learning, big data analytics, and information theory. In particular, he is also working on machine learningbased methods to design new data analysis methods for solving problems in manufacturing and production.

Frank Emmert-Streib is a professor at Tampere University, Finland, heading the Predictive Society and Data Analytics Lab. His research interests are in the field of data science, machine learning and network science in the development and application of methods from statistics and machine learning for the analysis of big data from genomics, finance, social media and business.

Herbert Jodlbauer is a professor at the University of Applied Sciences Upper Austria, Steyr School of Management and also acts as a director of studies of the bachelor study program Production and Management and the master study program Operations Management. Furthermore, he leads the trans-faculty institute of Smart Production. His research is primarily concerned with production planning, time continuous production models, financial valuation of production related decisionmaking as well as digitalization.