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
| Preface a Editors |
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xi | |
| Contributors |
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xiii | |
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1 Entrepreneurs for Renewables: Emergence of Innovation and Entrepreneurship in Complex Social Systems |
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1 | (48) |
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2 Entrepreneurial Network Effects: Empirical Observations of Entrepreneurial Networks in a World of Complexity |
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49 | (12) |
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3 Entrepreneurial Process: The Overbearing Role of Complex Social Network |
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61 | (28) |
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4 Sustainable Entrepreneurial Activity within Complex Economic Systems |
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89 | (20) |
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5 Integration Opportunities of Stability-Oriented Processes for Real Estate Transaction Entities |
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109 | (8) |
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6 Entrepreneurial Dispositions Personality Inventory: Development and Validation |
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117 | (24) |
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Marcin Waldemar Staniewski |
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7 Mapping the Entrepreneurship from a Gender Perspective |
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141 | (30) |
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Maria del Rocio Gdlvez-Garcia |
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| Index |
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171 | |
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.
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