Entrepreneurship Viability Index: A New Model Based on the Global Entrepreneurship Monitor (GEM) Dataset [1st ed.] 9783030546434, 9783030546441

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Contributions to Management Science

Nezameddin Faghih Ebrahim Bonyadi Lida Sarreshtehdari

Entrepreneurship Viability Index A New Model Based on the Global Entrepreneurship Monitor (GEM) Dataset

Contributions to Management Science

The series Contributions to Management Science contains research publications in all fields of business and management science. These publications are primarily monographs and multiple author works containing new research results, and also feature selected conference-based publications are also considered. The focus of the series lies in presenting the development of latest theoretical and empirical research across different viewpoints. This book series is indexed in Scopus.

More information about this series at http://www.springer.com/series/1505

Nezameddin Faghih • Ebrahim Bonyadi • Lida Sarreshtehdari

Entrepreneurship Viability Index A New Model Based on the Global Entrepreneurship Monitor (GEM) Dataset

Nezameddin Faghih UNESCO Chair Professor Emeritus Cambridge, MA, USA

Ebrahim Bonyadi GEM Office University of Tehran Tehran, Iran

Lida Sarreshtehdari GEM Office University of Tehran Tehran, Iran

ISSN 1431-1941 ISSN 2197-716X (electronic) Contributions to Management Science ISBN 978-3-030-54643-4 ISBN 978-3-030-54644-1 (eBook) https://doi.org/10.1007/978-3-030-54644-1 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book, published during the COVID-19 outbreak, is dedicated to all healthcare providers worldwide, who genuinely serve humanity and sacrifice their lives to save others.

Foreword

The Global Entrepreneurship Monitor (GEM) is a well-known research project that provides an annual assessment of the national level of entrepreneurial activity in multiple, diverse countries. GEM is considered to be one of the largest ongoing studies of entrepreneurial dynamics in the world. It is also considered to be one of the most accurate studies of entrepreneurism. But more recently, entrepreneurial activities in a number of countries have begun to diverge from the characteristics that have long been used to categorize who are entrepreneurs and what are entrepreneurial businesses. Since 2004, the World Economic Forum (WEF) has ranked countries based on the criteria in its Global Competitiveness Index. While WEF bases its results on three discreet economic criteria (i.e., factor-driven economy, efficiency-driven economy, innovation-driven economy), with factor-driven economy considered the least developed and innovation-driven economy the most developed. Initially the factordriven economies were considered to be based on extractive industries, such as subsistence agriculture. But soon they were heavily reliant on unskilled labor and the extraction of natural resources, such as oil. This specific category of factor-driven economy (referred to as rentierism) still exists, particularly in the oil-rich countries. As this book discusses, there are multiple reasons that influence the choice of entrepreneurial activity that are unique to some of the lesser-developed countries. For example, these lesser-developed countries have entrepreneurs who do not want to follow the entrepreneurial patterns that wealthier countries see as “viable,” or even innovation-driven. This is certainly the case in many parts of the Middle East and North Africa (MENA) where a business failure can still mean long jail sentences. Despite many years of the OECD complaining that some countries in the MENA region have not yet eliminated the Bankruptcy Law, there has been no movement by the governments beyond discussions. Therefore, the risks of attempting new, truly innovative ideas presently have no safe place in these countries. Likewise, creating a startup that most people in the country have never heard of, never seen, nor understand, would not be accepted, even when the entrepreneur has the ideal skill set. vii

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Foreword

There are a number of examples, of other characteristics that the authors of this book discovered, which have detrimental outcomes for the entrepreneurs who undertake specific business categories combined with the development phase of the country. Two examples follow that point to the dependency on the economic development level of the country: – Entrepreneurship viability has likely been overlooked when the entrepreneur originates in a factor-driven economy. – Entrepreneurial capabilities are largely underreported, or even underestimated, when the overall economy is in the efficiency-driven stage. Step by careful step, this book’s authors lay out their case, raising questions about whether or not budding entrepreneurs from the lesser-developed countries are being considered as carefully as they should be. Most recently, the worldwide impact of the COVID-19 virus teaches us that even the most developed economies are not able to predict whether the economy, jobs, and/or budding entrepreneurs will even be able to survive. Despite these contemporary drawbacks, some entrepreneurs will have the chance to flourish! Who are they? How will we recognize them? Presently the world confronts an unexpected and dangerous virus that destroys livelihoods and replaces active businesses with a “black cloud” that raises questions about when, if at all, the business might return to operational survival and business growth. Combined with a genuine “rethink” of whether or not the factor-driven and/or efficiency-driven economies have truly been replaced by the innovationdriven ones, the authors describe how an overlooked group of characteristics has a vital role to play in determining the best outcome. Using a variety of mathematical models, the authors describe why and how the entrepreneurs in any of the three economies should be judged and whether or not a specific entrepreneur has a better chance of success in a factor-driven, efficiency-driven, or innovation-driven economy. The authors show that the life expectancy and the economic success of the entrepreneur are linked to factors that are not always directly attached to the entrepreneur. For example, the specific economy and the sophistication level of the country’s population also play a strong role in business success. The authors provide details for each category of business and why it is not consistently appropriate for new entrepreneurs to only strive to be innovation-driven. Additionally, the authors have described the factors to be considered when estimating the life expectancy of the new or existing entrepreneurial business. Lenders, angel investors, and others with a financial interest in entrepreneurial businesses will be very interested in how these aspects can be more accurately evaluated, both in terms of costs and capabilities, as well as the likely life cycle (i. e., the duration) of the business itself.

Foreword

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“Entrepreneurship Viability Index, Based on Global Entrepreneurship Monitor (GEM)” helps us to understand how even more viable businesses could exist in many more countries than is the case today. I encourage others to read this book and to help identify these new categories of budding entrepreneurs. Meknes, Morocco April 2020

Victoria Hill

Foreword

Over the past two decades, the Global Entrepreneurship Monitor (GEM) has generated a truly vast supply of data from more than 50 countries. Perhaps surprisingly, relatively few scholars have attempted to move beyond simple statistical analyses. However, the best efforts have been fruitful. Professor Faghih’s effort here is a welcome addition to that list. An immense amount of energy went into this along with reflecting intense curiosity. It also reflects significant expertise at building sound economic indices. Readers will enjoy the depth of analysis that went into this volume. It is not an easy read, largely because it is so detailed in its analyses. For those who are skeptical of the findings, the book provides more than enough detail that they can assess for themselves. Many studies like this lack the transparency that this volume offers. With all the work that the Entrepreneurship Viability Index (EVI) reflects, it is refreshing to read an author who put in so much work and would be thrilled if other authors built on his work. The proof of any index is its predictive validity. I look forward to seeing how predictive this work is. In the Ewing Marion Kauffman Foundation’s great ESHIP initiative to support the building of entrepreneurial ecosystems, we have seen all too closely how important metrics are and how invaluable are predictive analytics. If this work lives up to its genesis, we will have a terrific tool for moving forward. Boise, ID, USA April 2020

Norris Krueger

xi

Preface

This book is an introduction to “Business Viability Analysis.” This toolset refers to the use, and importance, of long-term studies to predict future entrepreneurial business outcomes more accurately. By integrating the most significant factors that impact entrepreneurial outcomes, this toolset provides a better understanding of the characteristics that contribute to the most likely long-term business successes. The global economic crises have changed the international business environment and increased business risks. As a result, investors are more cautious, and the allocation of capital for new business startups has become more restricted. One of the most important ways to attract investors to these new business startups is to present a measurable guarantee of the expected success of the business after launch. The duration of these startups not only refers to the quality of economic life and contribution of the entrepreneurs, but also implies that the health of the economy in the launch country will grow and flourish. One of the main objectives of this book is to analyze the entrepreneurial activities’ lifetime using the Global Entrepreneurship Monitor (GEM) dataset. The Global Entrepreneurship Monitor (GEM) is the only global research source that collects entrepreneurship across countries. Its main mission is gathering data about entrepreneurial activities at the country level and then evaluating the data by computation of several of the most important entrepreneurial indicators. In order to cover both individual and environmental factors, this institute runs two projects named “National Expert Survey (NES)” and “Adult Population Survey (APS)” per year. The NES focuses on environmental factors influencing the quality of the business sector, and the APS gathers the data measuring the individual factors. The combination of these factors creates reliable criteria for assessing the situation of entrepreneurial activities across the GEM members. In fact, the data gathered in the NES and APS projects not only report the environmental and individual factors at the country level but the interactions of both datasets lead to qualitative indicators whose main applicability is in scientific research. In addition to releasing annual reports about the entrepreneurship status of member countries, the GEM consortium attempts to create opportunities for scholars xiii

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Preface

and policymakers to obtain comprehensive information about the status quo of global entrepreneurship. Thus, plenty of GEM-based studies have emerged rapidly in recent years that will, in turn, contribute to science. One of the approaches exploited by researchers is using powerful and scientific theories to create the indices. The creation of indices relating to entrepreneurial activities will, in turn, allow researchers to compare and rank countries at the global level. The computed indices can also help to provide policymakers with factual studies, not only of their own countries, but of the various entrepreneurial activities occurring among the GEM member countries. On the other hand, the accumulation of the GEM resources and increasing the size of datasets gathered over the past decade lead to considerable complexity in the appraisal of the indices. This will lead to confusion among analysts who only occasionally review the data. Hence, it is very critical to produce indices covering a greater portion of phenomena; taken altogether, this data produces another view of entrepreneurship outcomes. For example, a frequent combination of individual factors might include environmental factors, entrepreneurial motivation, and possibly even other factors derived from a wide variety of outcomes. The questionnaire of the GEM consortium does not include variables about the “life expectancy” of specific businesses. By using a combination of the rates of total early-stage entrepreneurial activities, established entrepreneurial activities, and the rate of exit from business, we were able to create another index named Entrepreneurship Viability Index. However, after producing this new index, we noticed that the average of the Entrepreneurship Viability Index in high-income countries was consistently higher than in the lower-income countries. On the other hand, by applying this index and some statistical methods (e.g., bootstrap simulation, Kolmogorov–Smirnov hypothesis test, and maximum likelihood estimator), we demonstrated that the “entrepreneurship viability data” come from a Weibull distribution. By fitting the Weibull probability density function to the “entrepreneurship viability data” gives researchers a more accurate analysis of the entrepreneurship concepts. Additionally, in this study, we focused on a group of entrepreneurs whose entrepreneurial activities have a direct impact on the economic cycle and are considered as a driving force for economic development. According to the assessments conducted in this study, factor-driven economies are higher than the innovation-driven economies in the rate of entrepreneurial activities, entrepreneurial attitudes, and entrepreneurial intention. The question this poses, though, is while the indices for entrepreneurial activity, individual attitudes, and entrepreneurial intention are far higher in factor-driven economies than in the innovation-driven ones, “why are these countries placed in underdeveloped and/or low-income groups?” In response to this confusing issue, we found that a large percentage of entrepreneurial activities (especially in factor-driven countries) not only have no positive effect on economic growth but should also be deemed inefficient. Hence, using the geometric mean (GM) equation and by applying individual factors, environmental

Preface

xv

factors, and motivation index, a new index, e.g., “Entrepreneurial Capability Index,” was generated. Taking this a step farther, this study has shown that the Entrepreneurial Capability Index has a remarkably positive impact on the per-capita increase in terms of the Human Development Index and gross domestic product (GDP). This regressionbased relationship refers to the direct impact this index has on the life-health and the economic situation at the country level. Finally, by combining the interactive effect of entrepreneurial motivation and entrepreneurship viability, we created another index called “Entrepreneurship Viability Coefficient.” This index is able to measure the rate of efficient entrepreneurial activities that may be useful in Schumpeterian-based1 entrepreneurship studies. All in all, various models (including linear and nonlinear regressions) were also used to fit best models to measure the relationship between these indices and other well-defined indices (i.e., the Human Development Index, gross domestic product per capita, and Economic Resilience Index). Furthermore, in order to apply robust statistical methods, a parametric method was applied to estimate the probability density function (PDF) of the “Entrepreneurship Viability Index” (EVI). Detailed information about the methodology used for indexing new entrepreneurship-based concepts, as well as the relationships between these indices, has been presented in six chapters of this book. Cambridge, MA Tehran, Iran Tehran, Iran

Nezameddin Faghih Ebrahim Bonyadi Lida Sarreshtehdari

1 Aghion, P., Ufuk, A. and Howitt, P. The Schumpeterian Growth Paradigm Department of Economics, Annu. Rev. Econ. 2015. 7:557–75. May 8, 2015 Harvard University, Cambridge, Massachusetts: “Three important aspects for which Schumpeterian growth theory delivers predictions that distinguish it from other growth models, namely, (a) the role of competition and market structure, (b) firm dynamics, and (c) the relationship between growth and development.”

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2

Entrepreneurial Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9

3

Entrepreneurship Viability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19

4

Entrepreneurial Capability Index . . . . . . . . . . . . . . . . . . . . . . . . . . .

81

5

Entrepreneurship Viability Coefficient . . . . . . . . . . . . . . . . . . . . . . . 133

6

Research Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

xvii

List of Figures

Fig. 2.1

Motivation classifications. Source: Authors’ own figure . . . . . . . . . .

Fig. 3.1

Graphical clarification of the concepts of the probability density function (PDF) and cumulative distribution function (CDF). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The hazard rate trend of weekly foot-and-mouth disease infection for cattle holdings (solid line) and other holdings (dashed line) in Cumbria (Great Britain) in 2001. Source: Authors’ own figure . . . The probability density function (PDF) of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5, 1, 1.5, 5). Source: Authors’ own figure . . . . . . The cumulative distribution function (CDF) of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5,1,1.5,5). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The hazard function of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5,1,5). Source: Authors’ own figure . .. . . .. . .. . . .. . .. . . .. . .. . Scatter plot of the rate of entrepreneurial activities versus the rate of exit from business. Source: Authors’ own figure . . . .. . .. . . .. . .. . Entrepreneurial Activities as a parallel system. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empirical probability density function (PDF) of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The empirical cumulative distribution function (ECDF) of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Fig. 3.2

Fig. 3.3

Fig. 3.4

Fig. 3.5

Fig. 3.6 Fig. 3.7 Fig. 3.8

Fig. 3.9

16

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40 55 57

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Fig. 3.10

Fig. 3.11

Fig. 3.12

Fig. 3.13

Fig. 3.14 Fig. 3.15

Fig. 3.16

Fig. 3.17

Fig. 4.1

Fig. 4.2 Fig. 4.3 Fig. 4.4

Fig. 4.5

List of Figures

The probability density function (PDF) of the real Entrepreneurship Viability Index (EVI) and the probability density function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The empirical cumulative distribution function (ECDF) of the real Entrepreneurship Viability Index and the empirical cumulative distribution function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . The real reliability function of Entrepreneurship Viability Index (EVI) and the reliability function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure . . . . The hazard function (hazard rate) of the real Entrepreneurship Viability Index and the hazard function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure . . . . The QQ plot of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The real probability density function (PDF) and the simulated PDF (Bootstrap-based Weibull probability density function with shape parameter ¼ 1.7791 and scale parameter ¼ 8.9876). Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . The real empirical cumulative distribution function (ECDF) and the simulated CDF [Bootstrap-based Weibull Distribution Function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876)]. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . Upper figure: the real reliability function and the simulated reliability function with the use of the Weibull probability density function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876). Lower figure: the real hazard function and the simulated hazard function with the use of Weibull probability density function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The rate of entrepreneurial activities [either nascent (SU), baby (BB), or established (EB)] vs. GDP per capita. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of the rate of entrepreneurial activities vs. the Human Development Index (HDI). Source: Authors’ own figure . . . . . . . . . Scatter plot of the rate of exit from business vs. the Human Development Index (HDI). Source: Authors’ own figure . . . . . . . . . Scatter plot of the rate of TEA based on necessity motivation versus the rate of total Entrepreneurial Activities. Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . Scatter plot of the rate of TEA based on opportunity approach vs. the rate of Total early-stage Entrepreneurial Activities. Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . .

65

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91 92 94

95

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List of Figures

Fig. 4.6 Fig. 4.7 Fig. 4.8

Fig. 4.9

Fig. 4.10 Fig. 4.11

Fig. 4.12

Fig. 4.13

Fig. 4.14

Fig. 4.15

Fig. 5.1

Fig. 5.2 Fig. 5.3 Fig. A.1

Fig. A.2

Fig. A.3

The path diagram for calculating the individual factor. Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . Scatter plot of the rate of individual factor vs. the Human Development Index (HDI). Source: Authors’ own figure . . . . . . . . . Scatter plot of the pre-index of Entrepreneurial Capability vs. the Human Development Index (HDI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of the pre-index of entrepreneurial capability vs. the rate of entrepreneurial activities. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual model of the Entrepreneurial Capability Index. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of the Improvement-Driven Opportunity motive vs. the Entrepreneurial Motivation Index. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Human Devolvement Index (HDI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of Gross Domestic Product (GDP) per capita vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of rate of the Total early-stage Entrepreneurial Activities based on Improvement-Driven Opportunity motive vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot of the rate of Entrepreneurial Employee Activity (EEA) vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . .

xxi

100 104

108

110 111

113

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127

Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Entrepreneurship Viability Coefficient (EVC). Source: Authors’ own figure . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . 141 Scatter plot of the Entrepreneurship Viability Coefficient vs. the rate of exited businesses. Source: Authors’ own figure . . . . . .. . 144 Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Economic Resilience Index. Source: Authors’ own figure . . . . 147 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the factor-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the efficiency-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the innovation-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

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Fig. B.1

Fig. B.2

Fig. B.3

List of Figures

Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the factor-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the efficiency-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the innovation-driven economies. Source: Authors’ own figure . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

List of Tables

Table 1.1 Table 1.2 Table 3.1 Table 3.2 Table 3.3

Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9

Table 4.1 Table 4.2 Table 4.3

Table 4.4

Classification of the different types of entrepreneurial activities on the basis of “efficiency” and “viability” . . . . . . . . . . . . . . . . . . . . . . . Ranking the entrepreneurial activities in order of their effectivity in the economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rate of entrepreneurial activities: either nascent (SU), baby (BB), or established (EB) . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. Rate of Exit from Business (between 2017 and 2018) . . . . . . . . . . . Analysis of variance (ANOVA) for comparing the mean values of the rate of exit from business and the rate of entrepreneurial activities into three groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample average of the rate of entrepreneurial activities and the rate of exited businesses in triple groups .. . . . .. . . . . .. . . . . .. . . . . .. . Regression-based models for the rate of entrepreneurial activities and rate of exit from business . . . . . . . . . . . . . . . . . . . . . . . . . . . Entrepreneurship Viability Index (EVI) . . . . . . . . . . . . . . . . . . . . . . . . . . . The ANOVA for comparing mean values of Entrepreneurship Viability Index (EVI) into three economic groups . . . . . . . . . . . . . . . Mean value of Entrepreneurship Viability Index into triple economies . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. Suggested questions for gathering more information about entrepreneurship life span for doing more accurate survival analysis .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . The rate of individual factors affecting the entrepreneurial activities . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . .. . .. .. . The primary index (pre-index) of entrepreneurial capability . . . . Regression-based models for the primary index (pre-index) of entrepreneurial capability and the Human Development Index (HDI) . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . . . . . .. . . . Regression-based models for the pre-index of entrepreneurial capability and the rate of entrepreneurial activities . . . . . . . . . . . . . .

5 6 49 51

52 53 53 58 59 59

79 103 107

108 109 xxiii

xxiv

Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10

Table 4.11

Table 5.1 Table 5.2

Table 5.3

Table 5.4 Table 5.5 Table A.1

Table B.1

Table K.1

List of Tables

Regression-based models for the Improvement-Driven Opportunity motive vs. the Entrepreneurial Motivation Index . . Standardization models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Entrepreneurial Capability Index (ECI) based on GEM dataset in 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression-based models for the Entrepreneurial Capability Index (ECI) and Human Devolvement Index (HDI) . . . . . . . . . . . . . Regression-based models for Gross Domestic Product (GDP) per capita and the Entrepreneurial Capability Index (ECI) . . . . . . Regression-based models for the Total early-stage Entrepreneurial Activities based on Improvement-Driven Opportunity motive and the Entrepreneurial Capability Index (ECI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression-based models for the Entrepreneurial Employee Activity (EEA) and the Entrepreneurial Capability Index (ECI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Entrepreneurship Viability Coefficient (EVC) . . . . . . . . . . . . . . Different types of entrepreneurial activities in view of both the Entrepreneurship Viability Index and the entrepreneurial motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression-based models for the Entrepreneurial Capability Index (ECI) and the Entrepreneurship Viability Coefficient (EVC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression-based models for the Entrepreneurship Viability Coefficient (EVC) and the rate of exit from business . . . . . . . . . . . . Regression-based models for the Entrepreneurial Capability Index (ECI) and the Economic Resilience Index . . . . . . . . . . . . . . . . .

112 116 118 119 121

124

126 136

138

140 142 146

Model summary and parameter estimates: all eleven regression models for estimating the relationship between the Human Development Index (HDI), as a dependent variable, and the pre-index of Entrepreneurial Capability (as an independent variable) . .. . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . . .. . . .. . . .. . . . .. . . .. . . 158 Model summary and parameter estimates: all eleven regression models for estimating the relationship between Gross Domestic Product (GDP), as a dependent variable, and the Entrepreneurial Capability Index (as an independent variable) .. . . . . . . . . . . . . .. . . . . 162 The real value of the Entrepreneurial Capability Index . . . . . . . . . . 169

Chapter 1

Introduction

Undoubtedly, entrepreneurship is the most important part of the economic development of societies. Perceiving this phenomenon and studying its efficient factors is one of the most essential objectives that researchers, policymakers, and even investors in the public and private sectors follow them up. Today’s widespread volume of data reveals some of the communications between entrepreneurship and other environmental and individual parameters that previously have been very complex. Despite all the information and knowledge released by institutes and researchers in the field of entrepreneurship, the changes in the economic situation, which are rooted in its entrepreneurial activities, are beyond the control of business owners. Hence, the exact identification of the factors affecting entrepreneurship that, in turn, lead to economic development (or even the economic downturn) also entails different and more accurate methods. This book, in addition to providing the application of various methods of statistics and mathematics in discovering the hidden information in data, attempts to present three new indices to study entrepreneurship situation across GEM member countries. After the presentation of justifiable grounds in the following sections and with reference to a great deal of reports released by authors about this fact that entrepreneurship drives a country toward development, this book endeavors to compute the rate of entrepreneurial activities that lead to economic development in GEM member countries.

1.1

Background

The reasons that scholars and policymakers believe that the study of entrepreneurial activities is important are as follows:

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_1

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1. Entrepreneurship may bring innovation and technological changes. Hence, the growth of an economy can be influenced by entrepreneurial activities. For this reason, according to Schumpeter (1934), entrepreneurship is often considered a strong driving force of economic development. 2. As numerous economists have acknowledged, entrepreneurial activities are intricate processes that equilibrate through which supply and demand (Kirzner 1997) and, consequently, the ideal circumstances will emerge in the business sector of countries. 3. Entrepreneurship is a special and unique process whereby new skills and knowledge will be converted into more lucrative products and services (Shane and Venkataraman 2000). 4. Entrepreneurship has become a highly influential vocation in the global community, so its contribution to the economy and quality of life needs to be examined. So far, many researchers have gathered reliable facts that entrepreneurship is the most significant factor in life quality due to its dominant effect on economic growth. In other words, entrepreneurial activities have been considered as a driving force of economic growth. The importance of the impact of entrepreneurship to conduct economies toward positive growth is examined by many academic studies worldwide. It has been theoretically and empirically proved that the entrepreneurship phenomenon has a direct and positive impact on countries’ economic status. Hence, entrepreneurial activities, which lead to economic growth and development in societies, will result in income growth as well. As a result, naturally, economic growth leads to an increase in quality of life in its wake. Therefore, the promotion of life quality level is one of the remarkable outcomes of entrepreneurial activities. Clearly, in measuring indicators of life–health (like Human Development Index) in any society, the economic indexes (like income) play a significant role. In fact, it can be said that the role of entrepreneurial activities in the growth of the level of life quality is remarkable. Acs et al. (2008) discussed the importance of the three stages of economic development and examined the empirical evidence on the relationship between stages of economic development and entrepreneurship. They concluded that the dynamics of entrepreneurial activities can be vastly different depending on institutional context and level of economic development. In fact, they talk about a mutual relationship between entrepreneurship and economic development. Schumpeter (1934) and Zahra and Dess (2001) have acknowledged that the effect of entrepreneurial activities should not be underestimated. Glaeser (2007), Haltiwanger et al. (2013), Guzman and Stern (2015), and Levine and Rubinstein (2018) stated that entrepreneurship has long been acted as a determinant factor to increasing living standards. According to their studies, successful entrepreneurship is infrequent, and the vast majority of entrepreneurs fail to render significant innovation and creativity to run efficient businesses. Research on the status of entrepreneurial activities, especially at the global level, often emphasizes the significant role of availability of resources in the lifecycle.

1.1 Background

3

Human capital has often been considered as the main orientation of entrepreneurship studies (Lucas 1978; Kihlstrom and Laffont 1979); additionally, the sub-dimensions of human capital (like education and experience) have been scrutinized thoroughly by many researchers (e.g., Iyigun and Owen 1998; Lazear 2004, 2005; Amaral et al. 2011). As Fairlie and Robb (2009), Volery et al. (2008), Chatterji (2009), Lafontaine and Kathryn (2014), and Dunn and Holtz-Eakin (2000) mentioned, empirical studies have demonstrated that human capital, such as the obtaining of relevant market and complementary technical knowledge, can estimate entrepreneurial success. For example, Lafontaine and Kathryn (2014) have shown that the former business experience of an owner increases the lifetime of the next business even after controlling the effects of personal behavior. Chatterji (2009) mentions that investments started by former employees of a company are better than other newcomers, which seems to be of advantage that results from the carrier experience of the prior market. Getting adequate skills and enough financial supports may not guarantee success to set up a business. Encountering with some of the challenges of launching a successful business and increasing empirical knowledge and well-balanced swift responses to various fluctuations in the business market may be more important than anything else. Job selection models by Johnson (1978), Jovanovic (1979), and Miller (1984) emphasize that people, when they are younger, try more dangerous occupations, such as entrepreneurship. Notwithstanding their more risky choice, this choice does not necessarily make their entrepreneurial activities to be more successful, and prior studies remark an intricate relationship between optimism and performance. Moreover, successful entrepreneurs will be more effective in job creation, income generation, increasing life expectancy, promoting motivation, etc.

1.1.1

GEM Dataset

Regarding the Global Entrepreneurship Monitor annual reports, the rate of entrepreneurial activities [either nascent (SU), baby (BB), or established (EB)] in the factordriven economies is much more in the innovation-driven economies (developed countries), while the rate of exit from the business in the former group is much higher than the latter. Additionally, this annual survey refers to the high individual factors of the factordriven countries as compared to efficiency-driven or innovation-driven economies. It seems that the sub-dimensions (sub-indexes) of the individual factors (e.g., role model, perceived opportunity, perceived capability, risk-taking, intention) are in stark contrast to the economic growth which seems to be a contradiction. Based on the GEM reports, the people living in an undeveloped country may know someone who has a business activity (whether a successful business or failed one). As well as people who are living in undeveloped countries have high opportunity perception to launch a business than the people of the developed countries and

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also they perceive their own capabilities and skills for launching a business more than developed economies. They scarcely fear of failure to start up a business (or they are so risk-taker), and also a great percentage of these people intend to start a business as compared to the people who are living in an innovation-driven economy. By considering the fact that entrepreneurship is a driving force of economic development (Schumpeter 1934), the questions that come up here are as follows: 1. Why entrepreneurial activities differ across countries? 2. What are the main reasons causing exit from the business in the countries with high individual attitudes and high entrepreneurial activities? The main objective of this book is to assess and analyze the dataset of GEM’s annual survey in order to answer such questions that have confused the researchers in the field of entrepreneurship. It seems that the progression of innovation-driven economies in terms of economic and life quality is rooted in the deep attention of the people and policymakers to efficient entrepreneurial activities. That means the environmental factors existing in these countries, which are provided by policymakers, state-run entities, investment institutions, and R&D organizations, which have been set in the entrepreneurship regulations, help people to venture only into the effective and efficient businesses. Meanwhile, improper circumstances and adverse environmental conditions in the factor-driven economies make an unreliable atmosphere for the people that have to become risk-takers for launching a business. Therefore, the unstable environmental conditions induce people to decide to run an entrepreneurial activity without any premeditation and special profession. The mandatory business initiation rather than knowledge-based activities may be the main reason to see more entrepreneurial activities and also the greater exit from the business in the factor-driven economies compared to innovation-driven countries. The next sections will unfold this fact more.

1.1.2

Business Sector Classification

In order to present more evidence, we can investigate the group of entrepreneurs who have initiated their own business only to maintain the former income or because they had no better choice. Based on GEM’s researches terminology, this group of entrepreneurs has been named the percentage of entrepreneurs who have launched their own business, out of necessity, because they had no better option in the labor market. This percentage of entrepreneurs started their business/businesses without any opportunity and premeditated motivation. That is, they had to just protect their previous income to live in security. Consequently, it can be said that there are two groups of entrepreneurs: first, those who launch a business that only affects their income, and, second, those who are

1.1 Background

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Table 1.1 Classification of the different types of entrepreneurial activities on the basis of “efficiency” and “viability” Lowa efficient High efficient

Nonviable Area 1 Area 3

Viable Area 2 Area 4

Source: Authors’ own table Note that in order to hold a positive vision toward entrepreneurial activities we will use the “low efficient” business instead of “inefficient” business

a

going to start a business that, besides increasing the income of entrepreneurs, have a great effect on the life quality of the other people. The first group starts their own business with the least motivation (or no motivation), and they only are looking for their own income rather than increasing the healthy life of society. In other words, these entrepreneurs belong to the category of entrepreneurs who launch their business on a necessity1 basis. The second group includes entrepreneurs who have maximum motivation, those who proceed with their entrepreneurial activities mainly for the purpose of creating a safe space for the other citizens who inhabit that country. Therefore, these entrepreneurs will be able to increase the life-health of society (e.g., HDI, GDP, etc.). Apart from the discussion on the effective entrepreneurial activities, we are going to separate the entrepreneurs whose entrepreneurial activities influence the economic cycle of the countries and the entrepreneurs who not only have no positive effect on the economic growth but lead the life quality of the citizens toward zero. To achieve this goal, the presentation of indexes measuring the lifetime of entrepreneurial activities that are under the effect of the quality of products and services seems to be essential. Although in the GEM’s survey there is no variable including the exact lifetime of the entrepreneurial activity, but using the rate of entrepreneurial activities [either nascent (SU), baby (BB), or established (EB)] besides the rate of exit from business, we were able to make a close estimation of remaining entrepreneurship lifetime, entitled “Entrepreneurship Viability Index.” To make an investigation into the businesses so considered as the driving force of the economic growth, we categorized the Entrepreneurship Viability Index (EVI) into two groups as stratified in Table 1.1: first, the low viable businesses, and, second, the high viable entrepreneurial activities. For more details, see Table 1.1 and its descriptions set forth below. This table shows all four areas of this classification. Area 1: belongs to entrepreneurial activities, which have no effect (or have a low effect) on the economic cycle and, in addition, will be soon destroyed. Area 2: includes the businesses that have a little positive effect on the economic cycle but last for a long time.

Throughout the remainder of this book, the words “necessary” and “mandatory” will be used equivalently.

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6 Table 1.2 Ranking the entrepreneurial activities in order of their effectivity in the economy

1 Introduction Rank 1 2 3 4

Area Area 4 Area 3 Area 1 Area 2

Situation High efficient and viable High efficient and non-viable Low efficient and viable Low efficient and non-viable

Source: Authors’ own table

Area 3: presents the percentage of entrepreneurial activities that have a strong positive impact on economies but last only for a few years. Area 4: pertains to the entrepreneurial activities that play a principal role in economic growth as well as are durable2 for a long time. Table 1.2 arranges these four groups in order of importance. The criterion of measuring the importance is on the economic effectivity basis. That means the viable and efficient area refers to a more effective factor in economic growth. In view of Table 1.2, only the businesses that last for a long time, as well as the portion of entrepreneurial activities that positively affect the sub-dimensions of life quality (like the economy, income, and so forth), are the driving forces of the economy that Schumpeter (1934) has talked about. On the other hand, the group that has been ranked fourth (area 2) in Table 1.2 demonstrates the entrepreneurial activities that not only have no positive effect on the life quality and economic growth but take a negative effect on the life status (we have discussed this topic comprehensively in the next chapters). In general, the adoption of various scientific methods for the determination and also the identification of effective entrepreneurship in the economy, which has always been a long time in the business and labor market, is the most important objective of this book. The meaning of efficient entrepreneurship in the economy is those entrepreneurial activities that not only enhance the quality of life of their employee(s) and the owner (s) but also create appropriate standards of living for the people living in that country. It can be assumed that with the disappearance of efficient business in society, a great part of the life quality of people is subject to risk. Nevertheless, in order to study an efficient and productive business in detail, this book provides the next chapters for the purpose of indexing the concepts of entrepreneurship viability (EV) and entrepreneurial capability (EC). Note that the data used for generating these indexes are selected from the GEM’s Adult Population Survey (APS) gathered in the years 2015 and 2018.

2 Throughout the remainder of this book, we will use either “viability” and “durability” terms equivalently.

References

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References Acs ZJ, Desai S, Hessels J (2008) Entrepreneurship, economic development and institutions. Small Bus Econ 31:219–234. https://doi.org/10.1007/s11187-008-9135-9 Amaral AM, Baptista R, Lima F (2011) Serial entrepreneurship: impact of human capital on time to re-entry. Small Bus Econ 37(1):1–21 Chatterji AK (2009) Spawned with a silver spoon? Entrepreneurial performance and innovation in the medical device industry. Strateg Manag J 30(2):185–206 Dunn TA, Holtz-Eakin JD (2000) Financial capital, human capital, and the transition to selfemployment: evidence from intergenerational links. J Labor Econ 18(2):282–305 Fairlie RW, Robb M (2009) Gender differences in business performance: evidence from the characteristics of business owners survey. Small Bus Econ 33(4):375–395 Glaeser EL (2007) Entrepreneurship and the city. National Bureau of Economic Research (No. w13551) Guzman J, Stern S (2015) Where is silicon valley? Science 347(6222):606–609 Haltiwanger J, Jarmin R, Miranda J (2013) Who creates jobs? Small vs. large vs. young. Rev Econ Stat 95(2):347–361 Iyigun MF, Owen AL (1998) Risk, entrepreneurship, and human-capital accumulation. Am Econ Rev 88(2):454–457 Johnson WR (1978) A theory of job shopping. Q J Econ 92:261–278 Jovanovic B (1979) Job matching and the theory of turnover. J Polit Econ 87(5 pt. 1):972–990 Kihlstrom RE, Laffont JJ (1979) A general equilibrium entrepreneurial theory of firm formation based on risk aversion. J Polit Econ 87(4):719–748 Kirzner I (1997) Entrepreneurial discovery and the competitive market process: an Austrian approach. J Econ Literat 35:60–85 Lafontaine F, Kathryn S (2014) Serial entrepreneurship: learning by doing? NBER Working Paper No. 20312 Lazear EP (2004) Balanced skills and entrepreneurship. Am Econ Rev Pap Proc 94:208–211 Lazear EP (2005) Entrepreneurship. J Labor Econ 23(4):649–680 Levine R, Rubinstein Y (2018) Selection into entrepreneurship and self-employment (No. w25350). National Bureau of Economic Research Lucas ER (1978, Autumn) On the size distribution of business firms. Bell J Econ 9:508–523 Miller RA (1984) Job matching and occupational choice. J Polit Econ 92:1086–1120 Schumpeter JA (1934) The theory of economic development: an inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press, Cambridge, MA Shane S, Venkataraman S (2000) The promise of entrepreneurship as a field of research. Acad Manag Rev 25(1):217–226 Volery T, Bergmann H, Gruber M, Haour G, Leleux B (2008) Global entrepreneurship monitor. Bericht 2007 zum Unternehmertum in der Schweiz und weltweit Zahra S, Dess G (2001) Entrepreneurship as a field of research: encouraging dialogue and debate. Acad Manag Rev 26(1):8–11

Chapter 2

Entrepreneurial Motivation

It’s often said that a person cannot win a game if he/she does not start the game. In entrepreneurship concepts, it is also stated that a person cannot succeed in a business if his/her willingness to become an entrepreneur is low. Entrepreneurial opportunities are an evolutionary but complex process in which individuals choose and examine a range of arbitrarily choices and then make a decision after the discovery of these opportunities (based on positive opportunities, available resources, and skills). Clearly, all of the decisions depend on the power of willingness of individuals prior to starting a business. The frequency of the opportunities for launching businesses depends on the culture of society, too. According to Sarfaraz (2017), there is no universal pattern for culture. Different historical and environmental factors shape varied cultures that these various cultures, in turn, create particular entrepreneurship ecosystems. In this chapter, we intend to state that motivation is one of the determining factors in perceiving opportunities for launching successful businesses. Motivation is an endeavor, driving force, willingness, and energy that manages a person to follow his/her objectives. Motivation is the main driving force for an individual’s actions, willingness, and objectives and also comes from within the individual which activates innate strengths. Because of the impact of culture, social norms, specific lifestyle, traditions, and so forth on the result of the business, so, the willingness of becoming an entrepreneur may also be either voluntary or mandatory. Ryan and Deci (2000a, b) stated that the motivation of a person determines the way of his/her behavior or, in other words, the motive of an individual is the reason for the repetition of a person’s behavior. Motivation is also a set of norms that play a fundamental role in a person’s future objectives. An individual’s motivation may be inspired by the approach of others that encourages the individual to keep up with his/her way or may be due to events happening spontaneously within the person (named intrinsic motivation). According to the research of Jodai and Zafarghandi (2013), motivation is one of the most influential factors that encourage an individual to move forward in direction © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_2

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of his/her dreams. Ericsson (2016) acknowledged that the infrastructure of motivation is due chiefly to the consistent interaction of both conscious and unconscious factors that happens within the individual. Additionally, notwithstanding the hardship of handling a profitable business, focusing on motivation and managing its right direction will be associated with positive results in ultimate outcomes and achievements. Aldrich (2000) mentioned that entrepreneurial research in recent years has focused mainly on the description of the environmental characteristics affecting firm foundings, whereas Christiansen (1997) talked about the properties of entrepreneurial opportunities. By the way, these investigations have strongly enhanced our insight over the entrepreneurship phenomenon; however it ignores the function of human capital. All in all, as a brief result, it is clear that the growth of entrepreneurship depends on the final decisions that will be made on this great process. Generally, by considering the motivation of individuals as a dynamic process, it can be said that uncertainties and disequilibria are ubiquitous. Thus, there is an urgent need for a comprehensive study on the entrepreneurship phenomenon by applying more robust and multi-parameter statistical methods. Shane et al. (2012) argued that the attributes of people who make decisions about the entrepreneurship process would influence the decisions that they make. Nonetheless, many scholars, like Aldrich and Zimmer (1986), and Carroll and Mosakowski (1987), have fiercely criticized many of the researches on the role of human motivation in entrepreneurship. In another way, Shane et al. (2012) have remarked that inadequate carrier experience does not negate the necessity of understanding the role of human motivation in the entrepreneurial process. Indeed, even sociologists, who strongly oppose future research in the field of entrepreneurship, acknowledge that the motivating factors are the key factors in the future state of entrepreneurial activities. In other words, the importance of entrepreneurial motivation is one of the most effective and undeniable components that has been strongly endorsed even by critics in the field of entrepreneurial behaviors. Specifically, the opportunistic properties of individuals in entrepreneurial activities and the understanding of the right and lucrative opportunities in the field of entrepreneurship is one of the most important sub-dimensions of entrepreneurial motivation that can prepare a person to venture into a business. Perceiving opportunities in the business market is one of the most important features of a clever entrepreneur that can lead to many successes and profits. Aldrich and Zimmer (1986) stated that entrepreneurial activity can be defined as a complex function of the interaction of factors like opportunity, motivation for business startups, and access to resources. Researchers also believe that sharing most of the criticisms have resulted in insufficient consideration of the role of the motivation in the entrepreneurial activities in recent entrepreneurship research. Consequently, this book tries to put aside the theories of entrepreneurship that do not consider entrepreneurial motivation as the main factor of business success. In

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fact, the motivation for launching a business is the underlying pivot of this study that we try to assert its importance. We believe that the deletion of the motivation parameter in entrepreneurshipbased researches will be problematic. As Baumol (1968) articulately argued, the study of entrepreneurship that does not explicitly consider entrepreneurs is like the analysis of in which “the Prince of Denmark has been expunged from the discussion of Hamlet.” Entrepreneurship, in turn, includes human capital also. The process of entrepreneurship occurs due mostly to the fact that people are looking for profitable opportunities. People are different in their willingness and abilities for perceiving the opportunities because they are different from each other. Therefore, the result of carrying out any entrepreneurial activity will also be different among people. In this regard, our main assumption in this study is that entrepreneurial attitudes and perceptions vary from society to society, too. Consequently, analyzing the existing difference among entrepreneurial outputs, even over countries that are similar in culture and are neighboring with each other, will be easier and justifiable herein. According to recent studies, like Shane et al. (2012), people are fully aware that there is a difference in the motivation and capability of individuals to set up a business. In other words, the fact that people’s aspirations, as well as their abilities for launching a business, have a significant effect on the success or failure of the business, has been completely accepted by the public. Majority of scholars believe that the willingness and desires of individuals to pursue entrepreneurial opportunities strongly depend on things like cost (Amit et al. 1995), their holdings and financial capital (Evans and Leighton 1989), their social communication to investors (Aldrich and Zimmer 1986), and their professional experience in business market (Carroll and Mosakowski 1987; Cooper et al. 1989). In general, it can be argued that environmental factors such as financial support and the existence of appropriate entrepreneurial infrastructure as well as individual factors such as skills, capability, risk-taking, opportunity understanding, and most importantly the motivation for business startups can together guarantee the success of a business. For example, Shane and Venkataraman (2000) expressed that the rate of understanding of opportunities and the courage of risk acceptance among entrepreneurs have a significant effect on entrepreneurial decisions. Unfortunately, the bad news is that people are different in risk-taking and do not want to spend their resources and money in businesses until they are not fully aware of them. In fact, people have a little spirit to follow up team works, and they prefer to run business plans personally. Thereby, the lack of awareness of the success or failure of business also has a fundamental effect on the level of risk acceptance and spending the resources required for running businesses (Palich and Bagby 1995). For this purpose, most people prefer to startup businesses that previously existed in their region and also try to run businesses that approximately are aware of their outcomes. For this reason, in most countries, especially in underdeveloped societies, innovative businesses with high-risk are strongly rejected by the people.

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Chances of having a thriving and successful business highly depend on comprehending the environment surrounding the business sector of the living region. Awareness of the entrepreneurial infrastructure status in an area and knowing the opportunities created therein and, most importantly, understanding the needs and demands of individuals in that area will greatly affect the odds of business success. Sometimes the dominant needs in a specific region reveal the principal opportunities to produce the products and services required in the area. As a matter of fact, identifying the needs of individuals in a community gives the entrepreneur an opportunity to launch a prosperous business.

2.1

Motivation as a Stochastic Process

In the information era, a very large and significant part of the information always depends on the time parameter in which, according to this parameter, justifiable and comprehensible changes have always been observed. These extensive data have always been measured in the most commonly used methods of statistics and mathematics. More importantly, in order to investigate the changes in a natural phenomenon (or even an artificial case), considering time factor as one of the most applicable variables will be so advantageous and may contribute researchers to reach a beneficial relationship between parameters making the phenomenon. The time parameter in these phenomena (herein entrepreneurship) acts as a determinant factor, and exploiting this factor not only helps researchers to find patterns of behavior bur will help to generate time-based equations to find out and estimate future reactions of a variable in question. The functions presented by researchers to predict the time-dependent data behavior are a set of stochastic processes that have a completely assessable and ultimately lead to predicting the behavior of that phenomenon in the future. In general, the only way to study a phenomenon dependent on the unknown parameter(s) (e.g., time) is to create a function dependent on that parameter. In order to accurately study the behavior of considered variable, the best-fitted function, which is named “the best model” in the statistics terminology, will be used. With all these explanations, what we intend to express is the emphasis on the entrepreneurial motivation index, which is heavily influenced by time, individual, and environmental factors. More precisely, this index, even within a person, has fluctuations that can undoubtedly affect the state of entrepreneurship. Hence, the study of entrepreneurial motivation requires precise modeling that incorporates many parameters including individual factors, environmental factors, and time and can be considered as a stochastic process. Information about the motivation of individuals in a community will cover a large-ranging data that is dependent on the numerous parameters that researchers may not have taken into consideration. In a comprehensive classification, if someone

2.1 Motivation as a Stochastic Process

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is going to divide the factors influencing people’s motivation into two main parts, both individual and environmental factors can firstly be mentioned.

2.1.1

Individual Factors

Individual factors are one of the most important and underlying components that affect individuals’ insights and also their performance. The motivation of individuals in a society leads to carrying out various activities, such as scientific research, sports, social studies, cultural actions, and business activities that are highly dependent on individual factors (Atkinson and George 1960; Atkinson and Feather 1974; Atkinson and Joel 1978). Individual factors are sometimes within the control of individuals, and, in some cases, they are beyond the persons’ control also. In the context of the motivation to start a business, individual factors that may strongly affect people’s motivation can be stratified as follows: • Knowing successful or unsuccessful people in entrepreneurship (named rate of role model) • Perceiving the opportunities for launching a business (named rate of perceiving entrepreneurship opportunity) • Perceiving capabilities and skills over entrepreneurial activities (named rate of perceiving entrepreneurship capabilities) • Risk acceptance or fear of failure about business startups (known as risk acceptance) • Intention to do entrepreneurial activities (named entrepreneurial intention index) As mentioned, the motivation of individuals to start up a business is mostly influenced by many individual factors that lead to the complexity of the motivation constitutive components. Therefore, to estimate and evaluate the behavior of entrepreneurial motivation at the level of a country, it is necessary to produce a function dependent on each of these individual sub-indicators (as referred to in foregoing items). On the other hand, these individual sub-indicators have a different impact on entrepreneurial motivation at different times. That is, the motivation of individuals in the past, present, and future is influenced by the experiences, skills, knowledge, and competencies. As a result, the entrepreneurial motivation index, which is influenced by individual factors, is a time-dependent stochastic process that is strongly influenced by the insights and characteristics of individuals in view of business approaches. Thereby, this multi-parameter phenomenon leads to the complexity of making a unique estimator function for studying the entrepreneurial motivation index.

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2 Entrepreneurial Motivation

Environmental Factors

To specify the impacts of entrepreneurial motivation on economic development, relevant factors that may hold a sensible effect on the entrepreneurial process and its outcomes need to be managed and controlled. One another distinct group of entrepreneurship variables effective on economic development is related to the external/ exogenous factor which is referred to as “environmental factor.” Environmental factor comprising the following: • Political factors (e.g., political stability, the situation of legislation enforcement, legal restrictions, and currency stability) • Market forces (e.g., the infrastructure of the industry, technology) • Structure, barriers to entry to business market, market size, and population requirements) • Resources (e.g., availability of stocks and financial capital, labor market including skill availability, transportation infrastructure, and complementary technology) Most of the scholars are explicitly or implicitly agree with this division which separates well the environmental factors affecting the process of entrepreneurial activity. They also believe that these factors need to be carefully monitored to measure the motivation of the entrepreneurial process. Thereby, entrepreneurial motivation in addition to being seriously influenced by individual factors will also be heavily under the effect of environmental factors. The environmental factors that often lead to business startups are always studied by researchers, and it has been frequently reaffirmed that environmental factors have a significant impact on the motivation of entrepreneurial activities. Some environmental sub-indicators such as government financial support, ease of launching the business in a community, extensive labor market, the possibility of enforcement the innovative ideas, entrepreneurship appropriate infrastructure, currency stability, etc. are the most important cases which strongly influence the entrepreneurial motivation, especially into the factor-driven economies. Generally, in entrepreneurship concepts, environmental factors are referred to as a set of factors that directly and indirectly have positive and sometimes negative effects on the motivation of individuals to create a business. For example, government support of individuals to launch a business may be one of the most important environmental factors affecting the business startups in the community. This is obvious that because of changes in government programs there will certainly be significant differences in the amount of government support in the past, present, and future, especially in the factor-driven economies. Hence, environmental factors also vary over time, and many indicators can also affect these environmental factors that are not measurable but understandable to the individual. As a remarkable result, entrepreneurial motivation is also highly dependent on environmental factors and time parameters, which lead to complicated calculations of the entrepreneurial motivation of individuals in society.

2.2 Motivation-Based Theories

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On the whole, the entrepreneurial motivation index is a very complex stochastic process that, in addition to the time, the place or community in which people live, individual factors, and environmental factors will also heavily influence it. Researchers are expected to provide an accurate assessment for all aspects that are needed to calculate this very complex and important factor in the entrepreneurial world. As a solution, the Global Entrepreneurship Monitor (GEM) in its project named Adult Population Survey (APS) for appraisement of entrepreneurship indicators has also provided questions for assessing the motivation index, albeit it requires a profound review to be applied. Most entrepreneurial motivation questions are related to the business opportunity (including greater financial independence, increasing personal incomes, or maintaining the former income). It should be noted that considering this information alone cannot indicate the true gauge of the motivation of entrepreneurship in society. All in all, in order to calculate this index, in addition to time (which has a great influence on the value of this index), it is necessary to measure all aspects of the individual and the environmental factors. Opportunities are aspects of the environmental factors that represent potentials for profit-making and often are beyond the individual control, and also because of the differences in individual insights, entrepreneurs may vary in how they are interpreting opportunities. Thereby, we argue that opportunities affect entrepreneurial behavior which comes out of society; in addition, we refrain from the claim that perceiving opportunities totally characterize entrepreneurial motivation and also the trend of entrepreneurship in its wake. By having the considered reasons given by the above statements, entrepreneurs are under the effect of their insights that result from their society and educations. Likewise, it is clear that individuals with various insights may make different decisions when encountering alike opportunities. In the next section, we have presented a comprehensive set of motivation-based theories which clarify the importance of this factor, especially in the term of entrepreneurship.

2.2

Motivation-Based Theories

There are various motivation theories that have been presented to explain the concept and interpretation of “motivation.” Motivation is the driving force that pushes a person to work in a certain direction. Additionally, motivation is an energy that forces people to try to achieve goals, even if the conditions are not in accordance with them. After the foundation of human organizations, people (including owners, holders, and managing directors) are trying to find an answer to what motivates an employee in one organization more than other factors. This created several “content theories” and “process theories” of motivation. The basic distinction between content theories and process theories is the different manner that considered in each one of them.

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Fig. 2.1 Motivation classifications. Source: Authors’ own figure (Gopalan et al. 2017)

• Content theories are working on “what” motivates individuals. • Process theories focus on “how” motivation occurs. Thereby, the theories of motivation can be classified into content and process theories. Figure 2.1 demonstrates a comprehensive category of both content and process approaches about the motivation issue. Maslow’s need hierarchy, Herzberg’s motivation–hygiene theory, McClelland’s needs theory, and Alderfer’s ERG theory are theories of motivation in a content perspective in which finding the answer to what motivates an individual and is concerned with individual needs and wants is the ultimate goal (Herzberg et al. 1959; Herzberg 1965; Alderfer 1969). In this category of motivational theories, researchers always seek to identify the components affecting individuals which motivate them to move forward. For example, if a researcher in the field of business startup (entrepreneurship) is looking for motivation factors, it is said that the researcher works in the field of content theories. In general, the investigation on the factors affecting the motivation of individuals is related to the content theories. On the other hand, Vroom’s expectancy theory, Adam’s equity theory, reinforcement theory, and carrot and stick approach to motivation are related to the process theories which deal with “how” the motivation occurs. In other words, as the theory suggests, these theories do not discuss the factors influencing people’s motivation but focus on the process of impact and how to create them. As a result, the study of factors affecting the motivation of individuals (for example in entrepreneurship) is carried out in the context of content theories that accuracy or inaccuracy of considered factors can be confirmed or rejected in this approach. But the causality of the effect of motivational factors can be examined in the field of process theories. The first studies have been conducted on the motivation to examine the needs of individuals. Specifically, in the early studies, researchers thought that an organization’s employees work hard only to satisfy their needs, or it was thought that the purpose of the employees was mostly to meet their needs. For example, an employee who is always walking around the office and talks to many people may need

References

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companionship; maybe this behavior will lead to the satisfaction of his/her needs. Researchers have developed theories to find the needs of individuals. Four theories in this division are classified as follows: • • • •

Maslow’s hierarchy of needs ERG theory Herzberg’s two-factor theory McClelland’s acquired-needs theory

In general, theories of motivation indicate the strong relationship between endogenous factors and exogenous factors that finally will lead to a phenomenon. Considering entrepreneurship as a phenomenon, one can claim that entrepreneurial motivation (which consists of individual and environmental factors) is one of the most influential factors in the growth of the business. These theories are expressed in order to find the motivating factors, as well as the process of the impact of these factors. These scientific theories prove that the interaction of some of the individual factors and environmental factors leads to the incitement of individuals toward a predetermined goal, which is called motivation index. The amount of motivation depends on the level of positive attitudes of people from their surrounding factors. Albeit the attitude is more positive in the initiation of a business, but the amount of motivation, which covers all attitudes also, will have a great impact on the fulfillment of goals.

References Alderfer CP (1969) An empirical test of a new theory of human needs. Organ Behav Hum Perform 4:142–175 Aldrich H (2000) Organizations evolving. Sage, Beverly Hills Aldrich H, Zimmer C (1986) Entrepreneurship through social networks. In: Sexton D, Smilor R (eds) The art and science of entrepreneurship. Ballinger, Cambridge, MA, pp 3–23 Amit R, Meuller E, Cockburn I (1995) Opportunity costs and entrepreneurial activity. J Bus Ventur 10(2):95–106. https://doi.org/10.1016/0883-9026(94)00017-O Atkinson J, Feather N (1974) A theory of achievement motivation, 6th edn. Krieger Publishing. ISBN 978-0-88275-166-5 Atkinson J, George HL (1960) Achievement motive and text anxiety conceived as motive to approach success and motive to avoid failure. Bobbs-Merrill Company Atkinson J, Joel OR (1978) Personality, motivation, and achievement. Hemisphere Pub. Corp. ISBN 978-0-470-99336-1 Baumol W (1968) Entrepreneurship in economic theory. Am Econ Rev Pap Proc 58:64–71 Carroll G, Mosakowski E (1987) The career dynamics of self-employment. Adm Sci Q 32:570–589 Christiansen C (1997) The innovators dilemma. Harvard Business School Press, Cambridge Cooper A, Woo C, Dunkleberg W (1989) Entrepreneurship and the initial size of firms. J Bus Ventur 3:97–108 Ericsson KA (2016) Peak: secrets from the new science of expertise. ISBN 9781531864880, OCLC961226136 Evans D, Leighton L (1989) Some empirical aspects of entrepreneurship. Am Econ Rev 79:519–535

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Gopalan V, Bakar AAJ, Zulkifli NA, Alwi A, Mat CR (2017) A review of the motivation theories in learning. AIP Conference Proceedings 1891:020043. https://doi.org/10.1063/1.5005376 Herzberg F (1965) The motivation to work among Finnish supervisors. Pers Psychol 18:393–402 Herzberg F, Mausner B, Snyderman B (1959) The motivation to work. Wiley, New York Jodai H, Zafarghandi AM (2013) Motivation, integrativeness, organizational influence, anxiety, and English achievement. Glottotheory 4(2) Palich LE, Bagby DR (1995) Using cognitive theory to explain entrepreneurial risk-taking: challenging conventional wisdom. J Bus Ventur 10:425–438 Ryan RM, Deci EL (2000a) Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp Educ Psychol 25(1):54–67. CiteSeerX 10.1.1.318.808 Ryan RM, Deci EL (2000b) Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol 55(1):68–78. CiteSeerX 10.1.1.529.4370 Sarfaraz L (2017) Women’s entrepreneurship in Iran. Springer, Switzerland Shane S, Locke EA, Collins CJ (2012) Entrepreneurial motivation. Hum Resour Manag Rev 13 (2):257–279 Shane S, Venkataraman S (2000) The promise of entrepreneurship as a field of research. Acad Manag Rev 25(1):217–226

Chapter 3

Entrepreneurship Viability

This chapter attempts to discuss the life span of entrepreneurial activities across the GEM member countries. The motivation, objectives, and background of this chapter will be addressed throughout the next subsections in detail. Additionally, the data used in this chapter is on the basis of the overall time (from start to failure) of entrepreneurial activities which has been elicited from the reliable GEM dataset. Stringent requirements (like the quality of products and services) and high expectations on businesses’ credit (e.g., viability or durability) by public demands encourage researchers to study the reliability of businesses. Doing so entails a creditworthy dataset that measures the lifetime of business activities. Additionally, it is necessary to apply statistical models to study the changes and trends of the lifetime of entrepreneurship across considered countries. Lifetime data analysis is commonly utilized to assess product reliability, too. The traditional lifetime data analysis methods offer several parametric lifetime distributions such as exponential, Weibull, and lognormal on the failure time data collected by whether “field operation” or “lifetime testing” ways. Maximum likelihood (ML) method and the Bayesian approach may be applied to estimate the unknown parameters to make reliable decisions. The parametric method follows a restrictive assumption. In some circumstances, it may be difficult to specify a precise parametric model for the failure time distribution. For example, modern complex systems usually result from multiple/ mixture failure models, so the use of simple lifetime distributions will be improper to model the failure times of such intricate systems. For products that need emerging of advanced technology, such as nanotechnology, we are unfamiliar with their failure mechanisms at the nano or atomic level. By considering entrepreneurship as a complex phenomenon that substantially influences the economic cycle of the country which is under the effect of the individual and environmental factors, it can claim that this phenomenon is a complex and unclear system that its impact on the life quality of communities is undeniable.

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_3

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Our goal in this chapter is to identify some of the idiosyncratic characteristics of entrepreneurial activities that will lead a country toward a more advanced situation and a sustainable economy. Likewise, our main approach in this chapter is the assessment of the lifetime of businesses that are launched in GEM member countries. Hence, this chapter encompasses the study of viable/durable businesses which may, from the view of policymakers and researchers, lead to economic growth. Accordingly, all of the goals considered in this chapter are to generate a lifetimebased indicator for entrepreneurial activities in the GEM member countries. At the end of this chapter, the readers will find some useful outcomes which would unfold new information about entrepreneurship situation across GEM member countries.

3.1

Background

Extensive and plentiful studies have been accomplished on the importance of entrepreneurial activities across the globe. These studies agree that entrepreneurial activities and business startups dramatically contribute to the sustainability of economic activity and thereby bring the quality of life. The problem that exists in entrepreneurship studies is the lack of precise, applied, mathematical, and analytical methods and their optimal modeling to make known the unclear behavior of entrepreneurship phenomena to researchers. Therefore, since this phenomenon is influenced by many events and parameters that originate from individuals or the environmental factors, it can be claimed that the reaction of the phenomenon of entrepreneurship in every society is almost unpredictable. Careful investigation and exact modeling of entrepreneurship at a community level will have significant results, especially for policymakers in the field of economic development. Entrepreneurial activities are accompanied by change and fluctuation. More importantly, after considering the entrepreneurship phenomenon as a dynamic process, clearly, uncertainties and disequilibrium are ubiquitous therein. Schumpeter (1934) acknowledged this phenomenon takes systems away from equilibrium or toward new ones (Hayek 1948). For this reason, this study will attempt to evaluate the entrepreneurship in the time framework, and the main purpose of this chapter is to study the phenomenon of entrepreneurship in the form of time, whereby the reliability of entrepreneurial activities would be examined along with timedependent variables. Authors consider dynamics (the time-dependent systems or phenomena) as the type of analysis in which the object is to trace and monitor the typical time paths of the components/variables (Chiang and Wainwright 2004). Time-dependent investigations are the analysis of sequences in time. When equilibrium in the process of entrepreneurial activity is lost due to some market disturbance, it will not be easy to achieve a quick product that its production is

3.1 Background

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affordable. In other words, getting to the former state of equilibrium would be somewhat problematic for the entrepreneur. More precisely, the process of creating a new product is not so practical, swift, and affordable until when the old theory of full competition made it out to be. Besides, Schumpeter (2003) stressed the possibility that the constant efforts to return a system back to a state of equilibrium may make it worse off from the former status and make it far away from the previous equilibrium. North (2006) and Schultz (1980) mentioned that in dynamic processes, unreliability and disequilibria are occurring everywhere. Therefore, a careful investigation on a time-dependent process (such as entrepreneurship) requires identifying its behavior as the study of a distribution function (e.g., cumulative distribution function (CDF), probability distribution function (PDF), and so on) which will provide all information of the variable in the form of a family. Studying the status of entrepreneurship at different time intervals will naturally provide adventurous information to the researchers. An issue that is essential in the field of entrepreneurship studies is to evaluate and investigate the lifetime of businesses in global research. Awareness of such information will allow researchers and also policymakers to have a convenient tool to maintain the economy of some external threats. As such, such things as the following question can be responded using the lifetime data of entrepreneurial activities: 1. 2. 3. 4.

What is the average lifetime of a business? How long will it take for a business to fail? How much is the reliability of entrepreneurial activities over time in a country? How much is the risk of launching a business in a country?

Answering such questions entails finding the lifetime (failure time) of the entrepreneurial activities in the country. The way that we handled to create this variable (lifetime) will be discussed in the next sections. Given the importance of entrepreneurial activity for economic growth (Wennekers and Thurik 1999; Acs and Storey 2004; Audretsch and Keilbach 2004; Stel et al. 2005; Wennekers et al. 2005), endeavors to study the entrepreneurship phenomenon have been highly increased. Economic and social development needs entrepreneurial agents; hence a growing interest by public authorities with the aim of encouraging entrepreneurship is needed. An important issue here is learning more about the phenomenon of entrepreneurship. In other words, it is offered to recognize the characteristics of this phenomenon that is one of the most favorite topics that researchers are trying to explore. Job creation is one of the main missions of entrepreneurship, and this behavior (entrepreneurship) is the first operational step before reaching new products/goods and services. According to Acs (2006), the emergence of new businesses will generate further jobs, stimulate competitions, and boost innovations. However, as it is effective in creating new firms/companies, it is even more important to ensure their continuation to guarantee the creation of the jobs and income generation in its wake. Therefore, identifying the elements which guarantee the survival of innovative and new

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businesses is crucial. Hence, we focus on the survival of such effective entrepreneurial activities in the countries. Entrepreneurship is a multi-dimensional phenomenon, and many definitions are presented to interpret it. For example, OECD Economic Survey in 1997 has defined entrepreneurship: • Entrepreneurship is a dynamic process that grows to determine the appropriate economic opportunities in society, as well as can be applied to the development of businesses, the sale and purchase of goods and services. If entrepreneurship is considered as a dynamic system, it has a life cycle same as all dynamic systems. Each business has five stages in its lifetime as follows: • Stage 1: is the stage before birth (which is called the seed). This is the very beginning of a business life span/lifecycle. It is the stage of idea formation and modeling. Often, this is the soul-searching step. It’s where the entrepreneurs can consider this phase as the feasibility of launching their own business idea. • Stage 2: This is the stage of the birth of a business. The businesses are being tested, and their ideas are satisfactory in terms of their profitability and applicability. It is time for entrepreneurs to make their business official at this stage. Most people believe that this is the riskiest phase of business lifetime. In fact, it is believed that mistakes made at this stage impact the company years down the line. The birth of new firms is often treated as one of the essential stages in the job creation process and also economic growth. It is believed that the birth of organizations will increase the competitiveness of an organizational community in the country and will require companies to become more efficient in light of the emerging competition that is emerging. Similarly, they stimulate innovation and facilitate the acceptance of new technologies, while helping to increase overall productivity in an economy. Looking at the rate of birth of businesses in the European Union (according to data in the 28 member states), the number of newly-born companies has declined as a percentage of the total number of active companies. While studying the birth rate of an organization provides useful information about economic dynamism, its impact on the labor market is an important aspect, as an index of job creation potential. The speed of a company’s birth is highly related to expected interests. If the main goal of the newly-born companies is profitability, so their birth will come about rapidly. It is worth noting that, according to Demographic Statistics Business report in 2017, among the lost activities, the death of firms and companies is relatively more common. • Stage 3: Is growth and development/establishment. • Stage 4: Is development and businesses at this stage often see rapid growth in cash flow as this plan has already now been established.

3.1 Background

23

• Stage 5: Is maturity and the possibility of exiting. Entrepreneurship should now have a healthy profit every year after the development phase of the business lifecycle. While some companies continue to grow the line up at the right speed, others try to enjoy the same high growth rates. It can be said that entrepreneurs here are faced with two options: to expand or exit the business. The lifetime of entrepreneurial activities not only affects the individual’s decisions and lifestyle but also affects the economic cycle of the country. A remarkable part of literature refers to the analysis of the impact of entrepreneurship on economic performance at the company level. On the other hand, entrepreneurship is considered as a key driver for raising the standard of living (Holcombe 2003; Schumpeter 1934; Volery et al. 2008). When a business starts up, investors and owners often believe that their company will work for a long period of time. However, because of the lack of adequate experience, during the first year, companies are at the highest risk of bankruptcy (named “lowest-reliability period” that would be discussed in the next sections). Most investors and stakeholders of a company usually do not invest in other parts and portions of the firm to increase their profits and advantages and also prefer to continue their business as they are. Therefore, in order to increase their profits, they do not try to change their other productive ideologies. For example, increasing the volume of products and reducing the quality of the products produced are the only options that are usually used by investors and business owners to save money and survive the pressures of possible bankruptcy. Meanwhile the development of other parts of the company can guarantee the viability/durability of their running business. In order to identify the long lifetime entrepreneurial activities and the reasons that caused such capabilities, Borjas (1986), Clark and Drinkwater (2000, 2010), Lofstrom (2002), Schuetze and Antecol (2006), and Fairlie and Lofstrom (2013) worked on the foreign workers and entrepreneurs. They mentioned that foreign workers are mostly entrepreneurs. This greater passion of foreigners to run businesses can be due to the difficulty of breaking into the job market or the desire to return, as soon as possible, to their own native country. It is even more important/ interesting when Lofstrom and Wang (2006), Fertala (2008), and Andersson (2010) acknowledged the possibility that in comparing the survival rate of businesses between foreigners and nationals, the literature generally showed that the rate for immigrant workers is lower than for native workers, albeit, maybe, so more effective businesses. On the other hand, educations and experiences gained from previous careers are the factors that may heighten the reliability and also the survival of the current business. In fact, human capital is a factor capable to be considered as a determinant factor of business survival. More importantly, human capital can be thought of as the amount of education of the business runners as well as the skill acquired through their previous work experience. According to Block and Sandner (2009) efforts, the effect of education on the probability of durability/viability in self-employment is not clear. To this end, the theory of human capital would indicate that education has a positive effect on the

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probability of survival/lifetime of firms. However, recent research has demonstrated that entrepreneurs with high education levels may have more opportunities for salaried employment than low-level entrepreneurs, and this may reduce their time spent in self-employment. This argument will also come true when studying the previous work experience of the owner–managers. Following results gained from previous studies, such as Haapanen and Tervo (2009), Block and Sandner (2009), Andersson (2010), and Millan et al. (2012), approved that education is a more effective variable in the viability of selfemployment. On the other hand, Conversely, Georgellis et al. (2007) showed that education is not a significant and relevant factor, and this is a neutral and useless factor. While Nafzige and Terrell (1996) researched in India, Nziramasanga and Lee (2002) researched in Zimbabwe and provided a negative relationship between the duration of self-employment and the education. Additionally, Faghih et al. (2019) stated that the positive entrepreneurial attitudes and perception will be associated with positive results in entrepreneurship that longaged companies may be one of its positive results. According to research on the impact of previous job experience on the viability and sustainability of a business several, like Taylor (1999), Georgellis et al. (2007), and Millan et al. (2014), it has been demonstrated that previous experience positively influences the duration of businesses. Although Brüderl et al. (1992) and Praag (2003) found no relationship between these variables, Haapanen and Tervo (2009) after a thorough study on the economy of Finland said that there is a negative relationship between the previous work experience and the durability of businesses, especially in self-employment. Roberts et al. (2013) stated that wide-ranging experiences, which gathered from a large number of organizations, have a negative effect on entrepreneurship. Note that it does not mean that working in different companies would increase the level of knowledge and capabilities of the workers. Munsasinghe and Sigman (2004) acknowledged a permanent change in the position of different businesses, and the shift in a variety of work will be accompanied by difficulties in adapting to the new job, and also a prerequisite critical for gaining abilities and understanding the problems of setting up an entrepreneurial activity can be very helpful before starting a business operation, and it can probably increase the productivity and profitability of entrepreneurship, whereby the durability of the business will increase. Another variable whereby authors would be able to find the reason why the businesses do not continue and are exiting from the labor market is the rate of exit from business. The data relating to the rate of exit from business is gathered by Global Entrepreneurship Monitor (GEM) from 1999 onward. Many studies have been conducted on the reason for exit from business, and many attempts have carried to introduce the effective factors in business survival also. Contrary to other studies on the survival of entrepreneurship, this chapter is studying on the survival of entrepreneurship (or the reliability of entrepreneurship) with the use of the lifetime of entrepreneurial activities, rather than the job creation or expectation of growth in a business. Generally, the matter that this paper focuses on is about the lifetime of the business at the country level. Although we know that some factors such as the size

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of business, growth expectation, size of employees, amount of exports, etc. have a striking effect on the viability of a business, this chapter only examines the age of businesses without any consideration of factors affecting their success.

3.2

Survival Data Analysis

In recent years, the development and application of statistical science in various science branches and disciplines have unfolded its capability as a powerful tool in the development of science and technology. Today it can be argued that the use of statistical techniques in the analysis of various phenomena is unavoidable. The use of statistics science in modeling and analyzing stochastic phenomena in different disciplines of engineering and industry is one of the most important areas for the application of this science. The advancement of technology and the rise in the quality of life of people through industrial achievements have created a tough competition between manufacturers and rendered products and services of higher quality. In the production of strategic products, if a company fails to present the quality and capability of its products on the basis of desirable standards, it will soon withdraw from the competition. Rapid advances in technology, the development of modern products, the intensification of global competition, and the growing expectations of customers have brought sever pressures on high-quality product makers. Customers expect to buy products that are reliable and safe. Systems, machines, and devices should be able to operate at high probability under standard conditions for a specified period of time. Reliability is usually defined as the probability that a system, machine, or device will work faultlessly under normal circumstances for a given period of time. Reinforcement and guarantee of reliability is an important stage of product quality improvement. Reliability improvement programs for the next generation of products entail numerical methods to predict and evaluate different aspects of the reliability of a product. In most cases, these programs involve collecting reliability data from studies such as conducted tests or accurate monitoring units. In this chapter, we first discuss some of the features of reliability data, and, since we need some functions to interpret these features and obtain some of the characteristics of these data, we introduce some of these functions in detail. Apart from political, religious, and cultural issues, what seems to have a significant impact on the quality of life of people in different societies in the present age is the influential economic activities of countries. According to recent research, entrepreneurship is treated as a phenomenon that its effect on the economy of societies is undeniable. Expecting that a complex entrepreneurial phenomenon can provide thriving economic activities in a community

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requires a careful examination of the factors affecting the growth and degradation of this phenomenon worldwide. Since this phenomenon is strongly influenced by individual and environmental factors (as comprehensively discussed throughout the previous chapter), changing the trend/behavior of entrepreneurial activities and economic fluctuations varies from society to society. So what requires to be strongly considered by the investors and policymakers of a society is promoting the culture of running effective businesses. There is no doubt that effective businesses are influenced by government policies and programs, so ease of access to viable businesses requires smooth plans made by state-run entities and investors. A necessary and sufficient circumstance for assessing the rate of effective entrepreneurship in economies can be reliability or, in general, the survival of the business which, based on business terminology, can be termed as entrepreneurial durability and viability. Therefore, measuring the viability of entrepreneurial activities in a society can spontaneously lead to the evaluation of a specific type of entrepreneurial activity that has a direct and positive impact on the economy of the society. In the information era, the daily growth of data relating to various entrepreneurial fields, including individual factors and environmental factors, and the complexity of the methods for accurate measurement of this phenomenon have led each scientist to choose a method to analyze the situation of entrepreneurship and factors affecting this phenomenon. What we are looking for in this chapter is the survival and reliability of entrepreneurship, which is referred to as entrepreneurship viability in management science. The reason why we chose this issue in this chapter is that the examination of this feature at the level of countries, in addition to providing sufficient information about the lifetime of the business, automatically leads to the extraction of information on the businesses that will have a positive impact on the economy cycle of the society. One way to measure and analyze the lifetime and reliability of various systems, like entrepreneurship, is to examine lifetime data on survival analysis. Survival analysis is a set of simple and complex statistical techniques that accurately examine all aspects of the variable studied in terms of lifetime. From another perspective, when businesses fail to continue, then the data of their lifetime is easily available. Based on the reliability studies’ viewpoint, the best circumstance to have the most accurate analysis of the business life span is when the real data of the lifetime is available. In fact, the lifetime data for a business is available when the exact time of start until the failure of that business is also available. In the analysis of survival, the term “failure time” of a phenomenon is sometimes used instead of “lifetime.” The term “survival time” refers to the length of time from the start of a business to its failure. In terms of entrepreneurship, the situations that the term “survival analysis” may be used are as follows: • Survival of entrepreneurial activities • Probability of failure of an entrepreneurial activity after a specific period of time

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• Hazard rate (hazard function) of entrepreneurship In lifetime data analysis, researchers try to predict the lifetime of products by applying a known family of distributions which is carried out by fitting a statistical distribution to the sample of units. The fitting of statistical distributions to lifetime data is the main method to in-depth probe into the characteristics of the data. These distributions are accompanied by unknown parameters. Prior to examining some of the characteristics of this data, including the probability of failure of the product at the specified time and the average lifetime of the product and so forth, the unknown parameters shall have to be estimated. To the fulfillment of survival data analysis, the following steps are needed to be applied: • Gathering lifetime data (complete or censored) • Selecting the best-fitted candidates of distribution function for fitting the lifetime data • Estimating the unknown parameter(s) of the considered distribution function prior to applying the model as the best-fitted one • Extracting the results, such as charts and tables, average lifetime, probability of the failure at the specified time, etc. Usually, in the studies of the lifetime, the T symbol is considered as a non-negative variable that indicates the waiting time for the failure of a product or, in other words, the waiting time for an occurrence of an event. For simplicity, we adopt the term “survival analysis” for a favorite event such as “death” and waiting time like “survival time,” but the techniques that are being studied are much wider applicability. This part of the science of statistics can be used in a wide range of applications, including death, marriage, fertility, life expectancy, and so on. An observant demographer should note that this section of statistics can include issues related to the fields of immigration, fertility, immortality, and so forth.

3.2.1

Failure Function

In statistical discussions, when the variable under consideration is the length of time for an event (e.g., death), a histogram diagram can be plotted to represent the number of events as a function of time. The curve fitted to the histogram is called the probability density function (PDF) that we name it, not so arbitrarily, death density function which is usually represented by the symbol f(t), as shown in Fig. 3.1. If the size of the area under the curve of the death density function set to be equal to 1, then for each given time, the area under the curve from the left until the given time t is equal to the proportion of the population that die at the time t. For instance, if the studied population is the lifetime of lamps produced in an electronics manufacturing company, then the density function of lamps lifetime

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Fig. 3.1 Graphical clarification of the concepts of the probability density function (PDF) and cumulative distribution function (CDF). Source: Authors’ own figure

indicates the distribution of the lifetime of the manufactured lamps by the company. The area below this curve until the specified time, for example, t ¼ 10,000 h, indicates what proportion of these lamps will last for over to 10,000 h. The proportion of products that have been broken can be shown by a function of t that, in statistics, is known as the cumulative death distribution function, and it would be notated by F(t). Depending on the objective, analyzing the lifetime of a phenomenon might be carried out in the form of continuous-time, whereas some of the phenomena also require a discrete time. To identify the distribution function of a discrete random variable, we can determine either of its probability mass function (PMF) or probability density function (PDF). While for the continuous random variables, the probability density function (PDF) and cumulative distribution function (CDF) are well-defined. Thereby, the choice of the CDF by researchers would present full information over the issue under study. Because in continuous random variable P(T ¼ t) ¼ 0 for all t E (0, +1), then the probability mass function (PMF) does not work for continuous random variables. For this purpose, scholars normally define the probability density function (PDF). The basic distinction between the PDF and the PMF is that the PDF is the density of probability whereas the PMD is probability mass although these terms are often used equivalently. It is worth noting that the concept of these functions is very similar to the concept of mass density in physics. To get a better understanding of the probability density function of a continuous random variable, T, the definition of the function fT(t), wherever the limit exists, is as follows:

3.2 Survival Data Analysis

29

f T ðt Þ ¼ lim

Δ!0

Pðt < T < t þ ΔÞ Δ

ð3:1Þ

The presented function fT(t) will give the probability density at the given point. This function is the limit of the fraction of the probability of T in the interval (t, t + Δ] divided by the length of this interval when the length of this interval goes down to zero. Take note that Pðt < T < t þ ΔÞ ¼ F T ðt þ ΔÞ  F T ðt Þ

ð3:2Þ

Therefore, we conclude that f T ðt Þ ¼ lim F T ðtþΔΔÞFT ðtÞ ¼ dFdtT ðtÞ ¼ F ´T ðt Þ, if FT(t) is differentiable at t Δ!0

3.2.2

Reliability Function

There are many definitions of quality, but the general consensus is that an unreliable product is not a quality product. Kromholtz and Condra (1993) emphasize that reliability is quality over time. A reliable product is a product that can maintain its quality well over time. Hence, the reliability of a product is a probability that products can work very well at least over a determined period of time and also can do their tasks properly over a specified period of time. Reliability is usually expressed in the form of probability and represents the proportion of products that will be able to demonstrate their best performance for a specified period of time. It can be mathematically expressed as Z

þ1

Rðt Þ ¼ PðT  t Þ ¼

f T ðt Þdt ¼ 1  PðT  t Þ ¼ 1  F ðt Þ

ð3:3Þ

t

where f(t) is the failure probability density function (PDF), T is a random variable referring to the failure time of product or system (like entrepreneurship herein), and R(t) is the notation of the reliability function. In the reliability analysis, the first step is to estimate the cumulative distribution function of the products’ lifetime using the given sample of units. There are many parametric and nonparametric methods for estimating the probability density function (PDF) or, equivalently, the Cumulative Distribution of products’ lifetime. What is required in the analyses of outcomes in the reliability field and the durability of entrepreneurship is the factors affecting the exit from business or, in other words, finding the reasons why businesses go broke. Business failure or wear-out may be caused by the lack of necessary skills, lack of understanding of opportunities, lack of stock and financial capital, inappropriate government programs, and government reverse policies that lead to the discontinuation of some businesses, and so forth.

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3 Entrepreneurship Viability

Perceiving the effective factors in business failure may contribute to the analysis of outcomes such as various charts and also will help to investigate the trend of fluctuations emerged during a business’s lifetime.

3.2.3

Hazard Function

The hazard function or hazard rate function is one of the most important criteria in reliability studies and longevity issues. Consider the random variable T with the cumulative distribution function F(t) and the probability density function f(t). The basic question that comes up here is this: • If we assume that the system is still active at time t, what is the probability of immediate failure exactly at the time t? In other words, in order to calculate this probability using math and statistics equations, suppose the small and positive amount of δ; now, we are interested in solve the following conditional probability. Pðt < T < t þ δjT > t Þ ¼

Pðt < T < t þ δÞ ; P ðT > t Þ

t  0, δ > 0

ð3:4Þ

Under this condition that P(T > t) is not equal to zero for any t  0. If the conditional probability (Eq. 3.4) is divided by δ and its limit is calculated when δ ! 0, then its result is a function that is called the hazard function. If the hazard function of the continuous random variable T is given by h(t), then it can be calculated as follows: hðt Þ ¼ lim

δ!0

Pðt < T < t þ δÞ Rðt Þ  Rðt þ δÞ f ðt Þ ¼ lim ¼ ; δ!0 δRðt Þ δRðt Þ Rðt Þ

t0

ð3:5Þ

The last equality results from the fact that f ðt Þ ¼ lim

δ!0

Rðt Þ  Rðt þ δÞ : δ

ð3:6Þ

Hazard rate can also be known as the force of mortality, which measures the amount of acceleration of the product in the direction of degradation which referred to as the instantaneous death rate or the failure rate. A casual example of an instantaneous hazard function is shown in Fig. 3.2. This chart shows the likelihood of foot-and-mouth illness weekly in two different types of farms in Cumbria (Great Britain) in 2001. The hazard rate function (also known as failure rate function in the reliability and survival data studies) plays a crucial role in most survival analyzes. It should be

3.2 Survival Data Analysis

31

Fig. 3.2 The hazard rate trend of weekly foot-and-mouth disease infection for cattle holdings (solid line) and other holdings (dashed line) in Cumbria (Great Britain) in 2001. Source: Authors’ own figure (Wilesmith et al. 2003)

noted that the hazard function is not the probability of a system’s failure (that is, it does not necessarily take between 0 and 1). Additionally, this quantity actually represents the rate of degradation of the system over time and is able to measure the failure rate of a system. This function may appear in five states: constant failure rate, increasing failure rate, decreasing failure rate, unimodal, and U-shape. The latter, as shown in Fig. 3.2, has been widely used in engineering reliability and can be applied for the interpretation of varied phenomena.

3.2.4

Mean Time to Failure

Mean time to failure (MTTF) is a basic scale of reliability in systems or processes that are non-repairable. This indicator represents the average time that a system (herein entrepreneurship) can last. It is a statistical index that is obtained using the probability density function of the variable under study and also calculates the average value of the lifetime of the system (e.g., the average of the lifetime of the businesses in a society) using a large number of samples. As a metric, MTTF is an estimated value of lifetime average indicating how much the product could be expected to work well and also survive under specific tests. It is important to note that the mean time to failure of products which is collected by running thousands of units over a specific period of time is often achievable.

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Meanwhile, there is another term in the statistics terminology called mean time between failures (MTBF). This is a statistical value that measures the expected time between two failure times (also called time-to-break) concerning repairable systems. To get a feeling for MTBF, assume that three repairable systems simultaneously start to work properly at time zero. This test will continue until all three systems fail. Suppose the first system stops after 50 h. The second system stops working after 70 h, and the third system broke down after 90 h. The MTBF of the system is the average waiting time for the failure of all three systems which is equal to 70 h. Note that if the system is non-repairable, then the MTTF will be equal to 70 h. Birolini (2013) has expressed that the MTBF is being defined through the arithmetic mean of the reliability function R(t), which can be considered as the expected value of the probability density function f(t) of failure time. See the following equation. Zþ1 MTBF ¼

Z Rðt Þdt ¼

0

3.3

þ1

tf ðt Þdt

ð3:7Þ

0

Empirical Distribution Function

In the field of nonparametric simulation methods, there is a wide range of schemes that allow researchers to move forward to have a better analysis. One of the highestfrequency methods which is capable to apply for the simulation of the cumulative distribution function (CDF) called the empirical cumulative distribution function (ECDF). Based on the nonparametric estimation of CDF, what appears to be important is counting the number of events before the specified time. For simplicity, suppose we intend to compute the ECDF of a sample of numbers between 1 and 10. Prior to the estimation of the ECDF for these vectors of numbers, we shall order the sample. For example, in the fifth step, we count the number of units before the fifth unit, which is 5. The ECDF of this point is calculated as a fraction of 5 dividing by 10 (the total number of the sample units), which is equal to 0.5. We begin this subsection with the mathematical definition of the empirical distribution function as follows. Definition 1 Suppose X1, . . ., Xn are independent and identically distributed (IID) random variables, with distribution function F(x) ¼ P(X1  x), for i ¼ 1, 2, . . .n. The empirical cumulative distribution function (ECDF), also known simply as the Empirical Distribution Function, can be defined as the following equation.

3.4 Bootstrap Sampling Method

33

F n ð xÞ ¼

1 Xn I fX i  x g i¼1 n

ð3:8Þ

where I represents the indicator function, in which I{Xi  x} is 1 if Xi  x and is equal to 0 otherwise. Note that the distribution function of a discrete random variable places mass 1/n in the points X1, . . ., Xn. Note also that one can also define the Empirical Probability Function (EPF) of any Borel-measurable set1 B as Pn ðBÞ ¼ Pn 1 i¼1 I fX i E Bg. Furthermore, it is worth mentioning that the order of the samples is n not important in the computation of Fn(X). For this purpose, the definition of the order statistics would facilitate the process of creating the ECDF. Definition 2 Let X1, . . ., Xn be a vector of random variables and let π : {1, . . ., n} ! {1, . . ., n} be a permutation operator such that Xπ(i)  Xπ( j ) if i < j. We define the order statistics X(i) ¼ Xπ(i) . Therefore X ð1Þ  X ð2Þ  . . .  X ðnÞ Have note that the order statistics are just a reordering of the data. Two of the most important and practical order statistics are the minimum statistic and the maximum statistic, which is defined by X(1) ¼ min Xi and X(n) ¼ Xi. The ECDF can be written as F n ð xÞ ¼

 1 Xn  I X ðiÞ  x i¼1 n

ð3:9Þ

It is easy to see that this function will be shaped by step-by-step like a stair function, in which the next steps are with the jumps of height 1/n that occurs at the points X(i). The estimated function (ECDF) grows at each upcoming stage, and it is continuous on the right side (right continuous) and covers values between 0 and 1. The ECDF appears to be a tangible estimator for the distribution function under study. This method of estimating the cumulative distribution function is used widely throughout this study, and several charts of the ECDF are drawn in the illustrative example section.

3.4

Bootstrap Sampling Method

Statistics is the science of learning. The learnings are mostly based on a set of experiences, especially those that are few and would only occur in a very short period of time. The use of information and experience gained during a short period of time

Based on the theory of probability, the Borel set (Borel sigma field) is one of the most important sets which are being used widely for many purposes including measurable spaces.

1

34

3 Entrepreneurship Viability

can be generalized to the whole society when the conditions met. The researchers in this field make attempts to present robust and varied methods with the least error to introduce the best models especially when only a small sample size is available. The most important objective of statistical theory is to accurate estimation of the unknown parameters of the population. The accuracy of these estimates increases when the methods’ error is low. In other words, what seems to be substantial in the statistical inference, in addition to the choice of appropriate method, is the minimum error of estimation which is very essential. In all processes of statistical data analysis, three phases should be accomplished step by step with high precision. Generally, statistical theory tries to answer the following three questions: 1. Data collection: What is the best way to collect data? 2. Summary: What is the best method to summarize and analyze the collected data? 3. Statistical inference: How accurate are the applied methods for the data analysis? There are numerous nonparametric methods for simulating data in the science of statistics. Despite their widespread use, they have very simple basic principles. Some statistical theories are based on “urn schemes.” A lot of books and articles have been published on urn schemes that detail all the ideas that have emerged from these basic methods. One of these newly developed models is the “Bootstrap” method. The Bootstrap method is generally a newly developed method whose main application is in case of a low sample size. The basic idea behind Bootstrapping is to increase the sample size using the resampling from the previous sample. At first glance, it may seem that the repetition of the previous sample may not give new information to the researcher for analyzing and estimating the unknown parameter(s) of the population, but it is important to note that one of the most important goals of statistical science is to reduce the error of sampling and thus reduce the error of model selection. Replication of the sample using the Bootstrap method may not provide new information to researchers but will reduce the estimation error by increasing the sample size. The Bootstrap method introduced by Efron (1979) is a very general resampling method for estimating the unknown distributions of a sample on the basis of independent evidence. The Bootstrap method has been shown to be more applicable and beneficial than other manners. However, some counterexamples have shown that the Bootstrap method may generate a wrong solution and, depending on the type of data, it may present inconsistent and erratic estimators. Suppose the problem is to estimate the location parameter using the Bootstrap method. Let the observations x1, . . ., xn be the sample units that are coming from a common cumulative distribution function, which is referred to as independent and identically distributed (IID) based on the statistics terminology. It is merit to probe into the distribution appropriate for the sample in hand. This will come about if there were be proper candidates for the distribution of the data.

3.4 Bootstrap Sampling Method

35

Suppose b θ is the estimator of parameter θ. Note that b θ is a function of the X1, . . ., Xn; hence this estimator has a probability distribution (sampling distribution), which is shown by n and F. In the path of understanding the specifications of this sampling distribution, we would face two problems: • First: we are unaware of F. • Second: even if we find the characteristics of F then b θ can be as a complex function concerning X1, . . ., Xn that finding its distribution would need a wide range of analytical methods that probability is out of the researcher’s abilities. On the whole, the Bootstrap method in statistics refers to all methods that are highly dependent on random sampling with replacement method. According to Efron and Tibshirani (1993) and Efron (2003), the Bootstrapping method allows the researcher to be able to control the estimation and analysis errors in all the methods in the statistics, including such things as skewness, kurtosis, variance, quantile, confidence intervals, quartile, prediction error, or another such measure. On the other hand, Varian (2005) has stated that this technique authorizes individuals to estimate the sampling distribution function by using random sampling methods with replacement. In general, this method falls in the wide-ranging class of resampling schemes. For more details, it is necessary to take note that Bootstrapping is a procedure of predicting the values of an estimator (like mean value). Empirical distribution function, which described thoroughly in the previous subsection, is one of the reliable nonparametric methods for approximating distribution function using observed data. Approximating the cumulative distribution function can be calculated using the empirical cumulative distribution function method. To increase the accuracy of the estimated distribution function, the sample size can be increased by using the Bootstrap method, and then the structure of the empirical cumulative distribution function can be computed by exploiting the simulated data. Repeating the Bootstrap resampling method and the empirical cumulative distribution function manner will reduce the error of predicted distribution. After getting information about the characteristics of the statistical sample obtained by the Bootstrap method, such as mean, variance, kurtosis, skewness, median, and other information, researchers seek to expand and generalize the sample information to the population. It is important to note that information extracted from the sample will not be generalized to the population unless its transferability as well as the authority of extension be proved using the methods available in the inferential statistics and also statistical theories. The only way in the inferential statistics to prove the generalizability of sample information on the population, or, in other words, the only way in which the accuracy of the received information from a sample is authorized to generalize to population, is the hypothesis testing method. Depending on the condition of the sample, the hypothesis testing methods may be carried out using either parametric or nonparametric manners. The main condition in

36

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which the parametric hypothesis needs to be applied is the normal condition of sample units. In addition to the simple and straightforward use of the Bootstrap method, generating estimates of population parameters such as variance, confidence interval, mean, and so on, the most important advantage of the Bootstrap method in its applicability in ease of access to a huge number of sample for estimating distribution function. One of the major concerns that statisticians always face is the way they deal with a complex issue, especially when the data distribution function does not have a closed form as well as when a complex parameter is evaluated. In this situation, the Bootstrap method can simply generate arbitrary size from the sample and can then be used to determine the exact methods for estimating complex parameters. Monte Carlo method is one of the varied methods that will have a very accurate application in parameter estimation. Details of this method have been discussed in many papers and books. As a result, it has been acknowledged by many scholars, like DiCiccio and Efron (1996), that the Bootstrap method is a way to control and understanding the real results. Along with all the benefits of the Bootstrap method, there exist some disadvantages as well. For example, the generation of infinite sample leads to an increase in the skewness of the sample that, in turn, leads to the use of a nonparametric hypothesis testing method. Furthermore, resampling an independent sample from the existed sample is a little meaningless. It is not sensible to generate an independent sample from a finite sample which its units had been known ever before. Notwithstanding the disadvantages of the Bootstrapping algorithm, the existence of its much wider applicability, specifically in the choice of arbitrary sample size and decreasing of estimators’ error, we have applied this method in this chapter. Because of the inadequate size of the GEM member countries, to prove the accuracy of results raised from the GEM dataset, we attempted to increase artificially the size of the sample and retest the result on the big data.

3.5

Weibull Distribution

What we are looking for in this chapter is the analysis of the lifetime of entrepreneurial activities based on the GEM dataset. After collecting data on business life in each country, our goal is to assess the lifetime of the business in the societies. Lifetime measurement and modeling can be done using survival data analysis and or reliability analysis, which is widely used in statistics and mathematics. One of the most important and influential methods used by statisticians in analyzing product lifetime is the fitting of a well-known statistical model, which usually takes place within the framework of the probability distribution function.

3.5 Weibull Distribution

37

Since the method used to fit the lifetime distribution function in this book is nonparametric, it is necessary to determine some appropriate candidates for fitting the best model. Today, products manufactured by the business owners hold high quality (considering a business as a product it can be said that the businesses have been viable and are resilient when facing the economic downturn, etc.), and since the durability of the products are different it required to fit a model in which all variations are considered. One of the most important probability density functions, that is very flexible, is the Weibull distribution function. This distribution, among other probability functions related to longevity, is one of the most reliable density functions that any statistician tries to use this coherent and well-balanced distribution to reach the most realistic estimation (Hallinan 1993). Weibull models are beneficial to perceive the varied types of positive-domain variables and phenomena. As mentioned, the Weibull models are widely used in reliability assessment and also their great advantage is in their flexibility when encountering different phenomena. Meanwhile, in addition to the simple two-parameter and, also, three-parameter Weibull distributions in the statistics literature, there exist various new-developed Weibull-related distributions that are easily available. Many generalizations of the basic Weibull distributions are widely used, and various applications of this distribution in the field of reliability context have been developed, and also many software for the purpose of reliability analysis through data modeling with the use of Weibull distribution have been produced. The R package (a statistical programing language) is the software that we have exploited it widely during this book. There exist various types of the Weibull distribution structure, albeit the results of all models are the same and unique. One of the well-liked structures of the probability density function (PDF) of this family, which has made it easy to work with, is as follows: t α α f α,β ðt Þ ¼ t α1 eðβÞ , β

t > 0,

α, β > 0

ð3:10Þ

Note that this probability density function is defined when t E (0, +1). Because of the positive range of Weibull distribution, the selection of this distribution as the appropriate candidate for the lifetime seems to be right. The lack of flexibility of traditional forms of probability distribution functions has led researchers to provide more generalized models. The developed models of probability density functions, including the Weibull density function, in addition to the further flexibility in estimating the best model, have much wider applicability for accurate prediction of all the changes in the data. In order to increase the flexibility of the density functions, scholars usually add new unknown parameters to the model in different ways (shape or scale parameter).

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3 Entrepreneurship Viability

Fig. 3.3 The probability density function (PDF) of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5, 1, 1.5, 5). Source: Authors’ own figure

The plenty number of parameters within a model will guarantee its wider capability of precise estimation. The PDF of the Weibull distribution, with different values of the shape parameter, is shown in Fig. 3.3. The programming code of this chart is written using the R programing language in Appendix C. As Fig. 3.3 demonstrates, the different situations of this PDF highly depend on the values of the shape parameter (α). For α < 1,this density function is decreasing, for α > 1 its kurtosis will increase. In addition, the Weibull cumulative distribution function is as follows: (for more details, see Fig. 3.4, and in order to find the programming codes of Fig. 3.4, see Appendix C). t α

F ð t Þ ¼ 1  e ð β Þ ,

t > 0,

α, β > 0

ð3:11Þ

Figure 3.4 shows that the gradient of the Cumulative Distribution Function increases with an increase in the shape parameter. This is more pronounced, for instance, when comparing the shape parameter values below and above unity, i.e., the gradient increases sharply for values of the shape parameter higher than unity.

3.5 Weibull Distribution

39

Fig. 3.4 The cumulative distribution function (CDF) of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5,1,1.5,5). Source: Authors’ own figure

This means that as much as the shape parameter is increasing, the likelihood of the longer lifetime of a product (here entrepreneurship) also increases. The reliability function is the issue that we will be addressing as the next most important function in the field of survival analysis. The reliability function of the Weibull distribution is summarized as follows: t α

Rðt Þ ¼ 1  F ðt Þ ¼ eðβÞ ,

t > 0,

α, β > 0

ð3:12Þ

Note that this function is the complement of the cumulative distribution function (CDF). Thus, clearly, the sum of the reliability function and the CDF at a specified point (specific time herein) is equal to 1. Furthermore, the hazard function can be calculated as below: hð t Þ ¼

f ðt Þ α α1 ¼ t , Rðt Þ β

t > 0,

α, β > 0

ð3:13Þ

As regards the presented functions (Eq. 3.13), the probability density calculates the probability of failure at any given time.

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3 Entrepreneurship Viability

Fig. 3.5 The hazard function of Weibull distribution for constant scale parameter (β ¼ 1) and different values of the shape parameter (α ¼ 0.5,1,5). Source: Authors’ own figure

For example, consider the lifetime of an electronic machine in a factory. Obviously, each machine has a certain lifecycle function, which ultimately will be broken down due to the excessive operation or electrical shock, or due to stress and pressure related to temperature, humidity, etc. While a machine works continuously, after lasting a specific period of time, it has a greater chance of being broken down, because the failure rate is being computed by dividing the number of units that exist in a particular time interval by the number of total units. With these interpretations, if the goal is to calculate the probability of a machine failure, it can be calculated using the machine age division by the maximum age that the machine potentially is expected to last. Undoubtedly, this value would increase each year. With passing time, the probability of a machine failure would be higher. For example, a 10-year machine will go broke early than an aged 7 machine. Namely, the machine aged 10 would have a greater probability of failure than the younger one, because the 7-year machine has many years of its life which haven’t passed. Figure 3.5 presents the three states of Weibull’s hazard function. The programming codes of this figure are written in Appendix C.

3.5 Weibull Distribution

41

As this figure shows, the behavior of this hazard function varies by different amounts of the shape parameter. Among the five situations of the hazard function (including constant failure ratio, increasing failure ratio, decreasing failure ratio, and the unimodal failure ratio), the hazard function of the Weibull distribution includes three states. The increasing failure ratio occurs when the shape parameter is more than 1, decreasing failure ratio occurs when the shape parameter is less than 1; moreover, the constant situation of this function will occur when this parameter is equal to 1. In addition, the cumulative hazard function can be calculated as H ðt Þ ¼  log ð1  F ðt ÞÞ

ð3:14Þ

where H ðt Þ ¼

 α t β

According to the probability theory and statistics, to obtain some characteristics of the cumulative distribution function and the probability density function of a random variable, indirect use of a function called the “moment-generating function” can be used instead of direct use of said functions. The structure of the momentgenerating function for the Weibull distribution is as follows. Note that in this equation, X is a random variable.  E et

log X



¼ βt Γ



t þ1 α



ð3:15Þ

***where Γ denotes the gamma function. According to the probability theory and statistics, the characteristic function defines the probability distribution of any real random variable. If a random variable accepts a probability density function, then the Fourier transform of its probability density function is the characteristic function. Thus, the characteristic function is introduced as an indirect and alternative method to analytic results in comparison with the direct method in which the probability density functions or distribution functions shall be examined. Similarly, the characteristic function of log(X) is given by  E eit

log X



¼ βit Γ

 it þ1 α

In particular, the n-th raw moment of X is given by

ð3:16Þ

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3 Entrepreneurship Viability

 n mn ¼ β n Γ þ 1 α

ð3:17Þ

Therefore, the mean value and variance of the Weibull random variable can be calculated by using Eqs. (3.18) and (3.19).  1 EðX Þ ¼ βΓ þ 1 α

   2  2 1 VarðX Þ ¼ β2 Γ þ 1  Γ þ 1 α α

3.6

ð3:18Þ ð3:19Þ

Maximum Likelihood Estimator

In mathematics, a parameter is something in an equation that is often considered as the main component of that equation, and researchers often identify the behavior of the equation based on parameter changes. The parameter may be known and or unknown. In the case of the unknown parameter, there are many statistical methods that can be applied to estimate the parameter. In other words, by having the considered methods given by statisticians, a parameter is considered to be a random/stochastic variable, which may take any possible value from its range of variations (domain). The reason why the parameters have been considered important values is because of the extensive information that they carry about the population under study. Contrary to the statistic, which renders a small amount of information about a small part of society (i.e., the sample), a parameter gives much more information over the population under study. Therefore, getting detailed information on parameter values is one of the most important objectives of the statisticians that help them to provide more accurate approaches. The reason for the diversification of these methods is because of the varied datasets. Of course, the development of some traditional methods has increased the accuracy and alternatively reduced the error of estimation. The parameter never changes, but because it is unknown, so researchers develop different methods for its accurate estimation. The difference in the estimated value of the unknown parameter is due to the difference in its estimation methods, not because of parameter variations. Thus, any method that can get the close value of the actual value of the parameter with the lowest error is considered as the best method. To get a feeling for the basic distinction between parameter and statistic, let’s say you wanted to know the average age of everyone in your grade or year. If you gain the information about the age of those who are in your class, then the average age calculated from this data gives you the statistic, whereas calculation of

3.6 Maximum Likelihood Estimator

43

the average age in a large quantity of population, like all students in your country, will describe the average age parameter in the population under study. On the whole, unlike the parameter, the statistic is a variable that is constantly changing. In other words, by changing a sample, the value of the statistic will also be different. Thereby, the main difference between the statistic and the parameter is that the statistic is an approximation of a parameter that is calculated in a sample of society, while the parameter is the unknown but constant of an indicator in society. Since the statistical sample is random, then the changes in the sample will also change the amount of statistic. Parameters are usually indicated in Greek or uppercase letters, while statistics are usually represented by lowercase or Roman letters. On the other hand, the accuracy of estimating demonstrates how close your statistic is to the real specific parameter of the population. Suppose a researcher wants to study the average growth in interest rates over the past 2 months. If the factual amount of this average is 2000 units and the sample mean is around 2005, then the accuracy of this statistic is worth reporting. In general, statistics are variables that are also associated with errors but are currently the best tools we can approximate the unknown parameters of the population using them. As a confession, clearly, statistics are not entirely correct, but some of them are useful. Based on the inferential statistics approach, in which the goal is to generalize the information from the sample to the population, there are predetermined models but with unknown parameters that can be fitted using sample data. Some of these models are expressed in the form of probability distribution functions. After selecting the best candidates, the unknown parameters are estimated based on the existing data, and then the best model will be specified. The parameters in the distribution functions may appear in different modes. In general, there are three types of parameters: scale, location, and shape parameters. As mentioned, there is a wide range of methods for estimating the unobservable values, which act as parameters in the probability density functions, that their priority of choice is based on their application and also their advantageousness. One well-liked method that works only on the basis of the present evidence (collected data) is the maximum likelihood. For more details about this estimator, read the following introduction. Let’s consider X1, X2, . . ., Xn as a random sample that its elements are independent and also identically distributed (IID) and their distribution depends on some unknown parameter θ. In accordance with the inferential statistics, suppose the primary goal here is to specify a point estimator of θ that we show it with notation u(X1, X2, . . ., Xn); in addition, u(x1, x2, . . ., xn) denotes a “good” point estimate of the mentioned parameter, where x1, x2, . . ., xn are the observed data of the random sample under study. For example, if we intend to generate a random sample X1, X2, . . ., Xn in which the Xi (for i ¼ 1, 2, . . ., n) are assumed to come from a normal distribution with mean μ and variance σ 2, thereby, the primary goal is to find the estimators for either of μ and σ 2, by using the present data x1, x2, . . ., xn which gained from the random sample.

44

3 Entrepreneurship Viability

As stated, the unknown parameter estimator is accurate when being close enough to the actual value of the parameter. One way to measure the accuracy of an estimator is to use loss functions, which one of the most well-known of loss functions is the mean squares errors (MSE). It is reasonable to assume that as much as the data obtained from a statistical sample is similar to the actual data of the population, then the estimated value of the unknown parameter of the population will be closer to its actual value. On the other hand, the sample size is usually a very small percentage of the total population. Therefore, in order to have a random sample that is very similar to population’ units, it is necessary to select units from the sample population that are of the highest frequency units. This sampling method allows the estimated parameter value to be similar to the parameter value in the population. It is important to note that in a random sampling method, in a variety of ways including the Bootstrap method, the units of the population that are of most repetitive ones are more likely to be in the sample as well. Therefore, accurate random sampling will lead to creating a similar sample of the population of interest. Generally, in order to increase the precision of an estimator, it is best to use a sample that its members are of high incidence. Or, in other words, the use of a sample that the probability of its occurrence is greater than other samples is offered. This suggests that the most accurate estimator for an unknown parameter is obtained by an estimator which leads to the maximization of the probability density function. In short, the idea behind the maximum likelihood estimator is based on this attitude. An estimator is called the maximum likelihood estimator if and only if the probability density function gets its own most maximum amount by replacing the given estimator with the parameter. For the implementation of this idea, suppose a random sample X1, X2, . . ., Xn that are independent and identically distributed (IID) and probability density function of each Xi is f(xi; θ), and then, suppose the joint probability density function of X1, X2, . . ., Xn that we, not so arbitrarily, name L(θ) is as follows: LðθÞ ¼ PðX 1 ¼ x1 , X 2 ¼ x2 , . . . , X n ¼ xn Þ ¼ f ðx1 ; θÞ:f ðx2 ; θÞ . . . :f ðxn ; θÞ Yn ¼ f ð xi ; θ Þ ð3:20Þ i¼1 This equation arises from the basic definition of the joint probability density function. Equation (3.20) results from the fact that when a vector of independent sample exists, the joint density function of its units can be rewritten as a product of the density functions of each variable. Consequently, given the main idea of the MLE, we must treat the function L(θ) as a function of the unknown parameter θ and then determine the value of this parameter in such a way that the value of L(θ) became maximum. See the following example. Example Consider the random sample X1, X2, . . ., Xn where

3.6 Maximum Likelihood Estimator

( Xi ¼

45

0; if the randomly selected ball is red 1; if the randomly selcetd ball is blue

Suppose that the Xi (for i ¼ 1, 2, . . ., n) is independent and comes from a Bernoulli2 distribution with unknown parameter p. Try to find the maximum likelihood estimator of p, which represents the proportion of the balls with red color. In light of the basic structure of the maximum likelihood function [L(θ)], the steps of calculation of the MLE of parameter p in this example can be carried out as follows. First of all, the probability density function of the Bernoulli distribution is f ðxi ; pÞ ¼ pxi ð1  pÞ1xi ,

xi ¼ 0 or 1 and 0 < p < 1

ð3:21Þ

Therefore, the likelihood function, L( p), is LðpÞ ¼ px1 ð1  pÞ1x1  . . .  pxn ð1  pÞ1xn ¼

Yn i¼1

pxi ð1  pÞ1xi

ð3:22Þ

Now, to proceed with the MLE method, we shall find the p which maximizes the maximum likelihood function L( p). Because of the complex structure of the likelihood function, scientists often use a trick! Since the natural logarithm is an increasing function with respect to its variable, so we apply natural logarithm to both left and right sides of the likelihood function referred to in Eq. (3.22). That means if a value of p which is able to maximize the natural logarithm of the likelihood function ln(L( p)) is estimated, so no doubt this value of p can also maximize the likelihood function L( p). Taking the first derivative of ln(L( p)) is the next step of this process. Note that we shall take the first derivative of ln(L( p)) with respect to p rather than taking the first derivative of L( p). As a trick, doing so somewhat facilitates differentiation. Note that we will use either ln(L( p)) or Log(L( p)) equivalently throughout the remainder of this study. In this example, the natural logarithm of the likelihood function is LðpÞ ¼ px1 ð1  pÞ1x1  . . .  pxn ð1  pÞ1xn ¼

Yn i¼1

pxi ð1  pÞ1xi

ð3:23Þ

2 Based on statistics and probability theory, the Bernoulli distribution is a discrete probability distribution of a random variable which takes the value 1 with probability p (which is called the probability of success) and the value 0 with probability q ¼ 1  p (which is known to be as the probability of failure).

46

3 Entrepreneurship Viability

LogðLðpÞÞ ¼

X  X xi LogðpÞ þ n  xi ðLogð1  pÞÞ

ð3:24Þ

After taking the first derivative of both sides in the Eq. (3.24), and also setting it equal to 0, the next step is to solve the equation with respect to p. A traditional notation of the maximum likelihood estimator is a hat sign (“^”) that often is put on the estimator of the relevant parameter (herein p). In this case, the ML estimator is as follows: n P

xi

b p ¼ i¼1 n

ð3:25Þ

Accordingly, the ML estimator of p is n P

Xi b p ¼ i¼1 n

ð3:26Þ

Finally, in order to technically examine and also accurate evaluation of the forgoing issue, it is necessary to take the second derivative of the maximal likelihood function (Eq. 3.24). If this function is negative with respect to the parameter p, it shows that the designated estimator minimizes the maximum likelihood function.

3.7

Entrepreneurship Reliability Assessment

As discussed in the previous sections, in order to verify the reliability of a system, the availability of data related to the system’s lifetime is required. Lifetime data is the amount of time a system runs, and then it meets failure and eventually destroys. The system that we are considering in this research is a complex phenomenon that must be known and also needs to be well studied. Furthermore, wide-ranging experiences and knowledge of the endogenous and exogenous factors affecting this phenomenon should be constantly updated. This system, which has a direct and significant relationship with economic growth, is entrepreneurship. Since this research has been based on Global Entrepreneurship Monitor (GEM) dataset, therefore we attempted to identify the data which give us information about the lifetime of the businesses among all variable of the questionnaire in this survey. After a comprehensive investigation into the GEM dataset, we found no variables that introduce to us the life span of entrepreneurial activities among the Global Entrepreneurship Monitor (GEM) questionnaires up to 2019. Hence, in order to study this issue, we had to create our lifetime-based variable.

3.7 Entrepreneurship Reliability Assessment

3.7.1

47

Entrepreneurship Life Span

In this section, we intend to introduce a new index to measure the viability or durability of entrepreneurship among the GEM member countries. In the next section, using this index, we will evaluate entrepreneurship reliability (based on the viability of entrepreneurship) among countries that are members of the Global Entrepreneurship Monitor (GEM). In order to clarify the idea used in constructing the Entrepreneurship Viability Index, consider the following example. Example Suppose a car manufacturing center is in trouble of getting the raw material necessary to its productions and also it is no longer capable of producing without this material. On the other hand, suppose, in the depot of the factory there exist 5000 cars that were stored and previously not sold. Given the gathered information about this factory, about 400 cars per year are being sold customarily. The questions that arise here are as follows: • Question 1: Has this factory gone bankrupt? • Question 2: How long will this factory take to stop working? Since, because of the lack of material, this factory will no longer be capable of producing, so, it can be said that the company has stopped working at least until receiving the news of material. But since the managing director of this company has stored 5000 cars in the storehouse and has not yet been on the market, so it can be said that this car manufacturing is still making a profit and also the business market is influenced by the sales of this factory. After selling all products of this factory, the factory will go bankrupt and officially will no longer proceed into any activity afterward. Hence, the durability or, in other words, the remaining lifetime of the company from the present time can be calculated as follows: Viability of the company ¼

Total remaining products 5000 ¼ ¼ 12:5 Sales per year 400

ð3:27Þ

Therefore, it takes 12.5 years that this car manufacturer company to remove from the competition in the market. Note that there is a main restrictive assumption in calculating the lifetime of this company: • We suppose that no new products will be produced in the company as of the calculation time. In fact, the remaining lifetime (also named durability) of this company is calculated from the date that the company is no longer able to produce the products. In other words, this index presents the remaining lifetime of the activities. By considering this example, it is feasible to calculate the remaining lifetime of entrepreneurial activities (or entrepreneurship viability) at the country level. According to the reports of the Global Entrepreneurship Monitor (GEM), the rate

48

3 Entrepreneurship Viability

of total entrepreneurial activities for member countries is available annually. With respect to the GEM’s definition of the Total early-stage Entrepreneurial Activities (TEA), which reflects “all entrepreneurial activities that initiated along the past 42 months,” the value of this index is a quantitative measure that reflects the percentage of the TEA at the country level. In different circumstances, another type of entrepreneurial activity that is evaluated in this research is called the Established Businesses Ownership (EB). According to the Global Entrepreneurship Monitor definition, any entrepreneurial activities that have been launched from at least 42 months ago are called the Established Businesses. Given these definitions, the variable that we consider in this research is “the sum of all the entrepreneurial activities that have already been launched in the community and are currently active.” Besides, according to the Global Entrepreneurship Monitor dataset, another indicator that is very substantial for scrutinizing the business viability in a community is the Rate of Exit from Business. The main reason why we choose this indicator for a precise appraisal of the viability concept is its full information about the percentage of entrepreneurial activities that have been failed during the 12 preceding months (1 year). The rate of exit from business measures the ratio of entrepreneurs in a country who have abandoned their own business, for any reason whatsoever, during the past 12 months.

3.7.2

Illustrative Example

This section tries to create the Entrepreneurship Viability Index (EVI) and also assesses the reliability of this index which is made based on the Global Entrepreneurship Monitor dataset gathered in 2018. Note that only 49 countries have participated in this survey in the year 2018. That would be noticed that the Entrepreneurship Viability Index (EVI) is able to classify countries on the basis of their durability and effectivity on the economic cycle. Prior to calculation of the Entrepreneurship Viability Index (EVI) among the member countries, we sorted the countries with respect to the rate of “any entrepreneurial activity [either nascent (SU), baby (BB) or established (EB)].” Table 3.1 has provided overall details of the status of member countries in terms of the total entrepreneurship activity rate. In general, this table summarizes the indicator which has been calculated based on the sum of the entrepreneurial activities, either nascent (SU), baby (BB), or established (EB) rates for the 49 countries that have participated in the GEM project in 2018. In short, as Table 3.1 presents, the rate of entrepreneurial activity in Angola, Lebanon, Madagascar, and Thailand is highest as compared to the other countries.

Country Angola Lebanon Madagascar Thailand Brazil Guatemala Chile Sudan Peru Colombia Korea Canada Indonesia

Source: GEM-2018

Rank 1 2 3 4 5 6 7 8 9 10 11 12 13

SU+BB +EB 55.0663 44.1360 42.1393 38.2635 37.9947 37.2094 32.2098 31.4321 29.8211 27.2514 26.6695 25.0771 24.9040

Rank 14 15 16 17 18 19 20 21 22 23 24 25 26

Country Netherlands Taiwan USA Turkey Iran Uruguay Panama Switzerland Poland Argentina India Austria Greece

SU+BB +EB 23.5829 22.9882 22.5753 22.3939 21.4034 20.7838 20.1198 18.4170 18.1875 18.1031 17.9550 16.9775 16.7605 Rank 27 28 29 30 31 32 33 34 35 36 37 38 39

Country Slovakia Ireland Saudi Arabia United Kingdom Egypt Bulgaria Luxembourg Croatia China Puerto Rico United Arab Emirates Slovenia Qatar

Table 3.1 Rate of entrepreneurial activities: either nascent (SU), baby (BB), or established (EB) SU+BB +EB 16.3249 15.8929 15.1047 14.3350 14.3309 14.0461 13.6176 13.4666 13.3721 13.1782 13.1157 13.0738 12.6265 Rank 40 41 42 43 44 45 46 47 48 49

Country Spain Germany Sweden Japan Morocco Russia Italy Cyprus Israel France

SU+BB +EB 12.3923 12.2537 11.9484 11.3116 10.7570 10.4112 10.3783 9.6996 9.5340 8.4879

3.7 Entrepreneurship Reliability Assessment 49

50

3 Entrepreneurship Viability

Angola might have the highest rates of business in the 49 countries, but because of adverse business market circumstances3 in this country, the holders of these businesses will not be able to maintain their own businesses. More precisely, by the data gathered from at least 49 countries that are associated with wide-ranging experiences and investigations, the high gauge of entrepreneurial activities does not guarantee high-quality entrepreneurship. In fact, the rate of exit from business in such countries will reduce the reliability of the business market. Additionally, the great percentage of this rate includes the entrepreneurial activities which may destroy soon without any impact on the economic cycle. This means if a country has a high entrepreneurial activity rate but its rate of exit from the business is high too, this goes true especially in factor-driven economies. It can be said that launching businesses in factor-driven countries are unsustainable, and, in probability, it will soon be loosed out. For more clarification, imagine a country with a high rate of entrepreneurship and a low amount of rate of exit from business. Evidently, launching businesses in this country is very reliable and probably lucrative, and, as such, the businesses will be last for a long time in this country. Additionally, the reliability of entrepreneurial activities in such countries will grow. According to this table (Table 3.1), the rate of entrepreneurial activity in France, Israel, and Cyprus is lower than in other countries. Although the rate of entrepreneurial activities is low in these countries, the capability of entrepreneurs to maintain their running business in these societies is striking. As a result, the proportion of entrepreneurial activities to the rate of exit from business can be considered a determinant component that refers to the viability of the entrepreneurial activities across countries. In the following tables (Table 3.2), we will show that the high/low rate of entrepreneurial activity in a community does not guarantee good/bad conditions for the business sector. What specifies the credibility and the value-adding of a business in the community is the capability to the maintenance of the business. And, the lifetime/viability of a business denotes its strong effectivity on the economic cycle. This index which is introduced as the Entrepreneurship Viability Index (EVI) will be calculated in the following sections. Before computing the EVI, Table 3.2 shows the rate of exit from business based on the Global Entrepreneurship Monitor dataset in 2018 among 49 countries. As shown in this table, Angola, Sudan, and Peru have the highest rate of exit from business compared to other countries. In the comparing of the Tables 3.1 and 3.2 (rate of entrepreneurial activities and rate of exit from business), it is clear, Angola, despite having the highest rate of entrepreneurial activities, has the highest rate of exit from the business, too.

3 Based on the GEM’s report, the high exit from the business in this country shows that the business market in such countries is not so appropriate.

Peru Egypt Morocco Thailand

Chile Saudi Arabia Guatemala

Uruguay Canada

Lebanon Iran

3 4 5 6

7 8

9

10 11

12 13

Source: GEM-2018

Country Angola Sudan

Rank 1 2

4.5485 4.0633

4.9211 4.6479

5.1017

5.3443 5.3240

6.5792 6.0942 6.0637 5.4698

Rate of exited businesses 18.8170 7.7934

25 26

23 24

22

20 21

16 17 18 19

Rank 14 15

Sweden United States Greece Argentina

Panama

Madagascar Colombia Brazil United Arab Emirates Turkey Austria

Country India Israel

2.7744 2.5165

2.8238 2.8205

3.0546

3.2114 3.1158

3.3482 3.2920 3.2378 3.2327

Rate of exited businesses 3.7931 3.6008

Table 3.2 Rate of Exit from Business (between 2017 and 2018)

38 39

36 37

35

33 34

29 30 31 32

Rank 27 28

Cyprus Slovenia

United Kingdom France China

Netherlands Ireland

Croatia Puerto Rico Qatar Luxembourg

Country Slovakia Taiwan

1.6783 1.6498

1.8777 1.7163

1.8842

1.9203 1.8951

2.1796 2.1651 1.9386 1.9289

Rate of exited businesses 2.4755 2.2253

49

48

46 47

42 43 44 45

Rank 40 41

Indonesia

Japan

Italy Switzerland

Country Bulgaria South Korea Russia Poland Spain Germany

.8078

.9288

1.0981 .9899

1.4603 1.3514 1.1742 1.1015

Rate of Exited Businesses 1.5763 1.4652

3.7 Entrepreneurship Reliability Assessment 51

52

3 Entrepreneurship Viability

Table 3.3 Analysis of variance (ANOVA) for comparing the mean values of the rate of exit from business and the rate of entrepreneurial activities into three groups Variable Rate of exit from businesses

Rate of entrepreneurial activity

Between groups Within groups Total Between groups Within groups Total

Sum of squares 103.204

dfa 2

277.301 380.505 1372.801

46 48 2

3820.120 5192.921

46 48

Mean square 51.602

Fb 8.560

Sig.c .001

8.265

.001

6.028 686.400 83.046

Source: Authors’ own table Refers to the degree of freedom b Refers to Fisher statistic c Refers to significance level or, equivalently, probability value ( p-value) a

Observation of information collected in these two tables may be a bit confusing for readers. Namely, why in countries with a high rate of entrepreneurial activities, the rate of exit from businesses is high, too? This is one of the questions that the reader will find the answer at the end of this book. With the use of Tables 3.1 and 3.2 and as well the GEM reports, in summary, it can be said that low-income countries have high rates of entrepreneurship. For most cases, the underdeveloped countries (low-income or factor-driven economies) seem to have the top ranks in the term of entrepreneurial activities rate and also the rate of exit from the business. To accept or reject this claim that factor-driven economies have a greater entrepreneurial activity rate and rate of exit from business as compared to innovationdriven economies, we have analyzed this hypothesis thought statistical methods. Because of the small sample size, we tried to use a robust nonparametric method called the Kruskal–Wallis test for mean comparison. The average rate of entrepreneurial activities and the rate of exit from business of the three economy groups (low-income, middle-income, and high-income) were tested, and the results were presented in Table 3.3. In general, this table displays that there is a significant difference between the rate of entrepreneurial activities between three classes, and also the rate of exit from business profoundly varies among the three economies (low-income, middleincome, and high-income) at the level of 95%. Thereby, separately, entrepreneurial activities rate and the rate of exit from businesses in developed (innovation-driven) and non-developed countries (factor-driven) are significantly different. Table 3.4 summarizes the mean value of the rate of entrepreneurial activity and the rate of exit from business based on the triple economic groups. This table shows that the rate of entrepreneurial activities and the rate of exit from businesses in high-income countries are lower than in countries with lower earnings. Consequently, when there is a country with a high rate of entrepreneurship (like

3.7 Entrepreneurship Reliability Assessment

53

Table 3.4 Sample average of the rate of entrepreneurial activities and the rate of exited businesses in triple groups Income group Low Middle High Total

Entrepreneurial Activity 28.083514 26.936618 16.425603 20.450635

Exit from Business 6.673914 3.659691 2.454965 3.328120

Source: Authors’ own table Table 3.5 Regression-based models for the rate of entrepreneurial activities and rate of exit from business Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .234 32.181 .340 24.240 .194 11.337 .263 15.978 .277 10.709 .294 19.599 .296 19.730 .187 10.800 .294 19.599 .294 19.599 .294 19.599

df1 1 1 1 2 3 1 1 1 1 1 1

df2 47 47 47 46 45 47 47 47 47 47 47

Sig. .000 .000 .002 .000 .000 .000 .000 .002 .000 .000 .000

Parameter estimates Constant b1 12.612 2.355 11.030 9.564 28.092 17.073 11.456 2.883 8.066 5.611 13.713 1.092 12.502 .390 3.238 .733 2.618 .088 13.713 .088 .073 .916

b2

b3

.032 .532

.019

Source: Authors’ own table

Angola in this study), it does not mean that the entrepreneurship viability/durability and other entrepreneurial situations in this country are good because this country might be also ranked in terms of rate of exit from business at the top position. Furthermore, it would be better, those who set up a business in these countries (such as Angola), not to expect to have a protected and safe business. Based on the reports released by GEM on high business rates in such countries, ease of access to running low-efficient businesses can lead to initiation of non reliable businesses. On the other hand, the rate of exit from business happens too fast. So, it can be said that business startups in underdeveloped countries are not necessarily reliable and may soon fail and go bankrupt. Identifying and selecting a model with the least of mean squared error (MSE) and also the proper usage of the best estimator are prevalent problems among the statisticians especially when they acknowledge that the use of such accurate models will have substantial results in future researches. Thus, first of all, we normally used linear and nonlinear regression models to determine the most precise relation between entrepreneurial activities rate and the rate of exit from business. The outcomes of these regression-based tests are sorted in Table 3.5.

54

3 Entrepreneurship Viability

In these regression models, the rate of exit from business is considered as the independent variable, and the rate of entrepreneurial activities is considered as the dependent variable. Table 3.5 shows all the 114 linear and nonlinear fitted models. Although all models are significant at the 95% level, the logarithmic model has the highest determination coefficient.5 The magnitude of the R-square (as a numerical measure for distinguishing best model among all other models) of the logarithmic model is 34%. This means that the rate of exit from business is able to predict approximately 34% of the variations of the rate of entrepreneurial activities across member countries. See the chart below (Fig. 3.6). The equation of this model is estimated as follows. Rate of Entrepreneurial Activities ¼ 11:030 þ 9:564 LnðExit from BusinessÞ

ð3:28Þ

where Ln is the natural logarithm function. This fitted model suggests that with the increase in the rate of exit from business, the rate of entrepreneurial activities will also increase. Moreover, this formula confirms that in societies with high incomes and a low rate of exited businesses, the rate of entrepreneurial activities is low as well. This reflects the fact that the viability of entrepreneurship in different societies may not be mostly related to their rate of entrepreneurial activity. Since the rate of entrepreneurship in developed countries is much lower than this indicator in undeveloped countries, the lower amount of exit from business in developed countries confirms this fact that entrepreneurs in these countries are struggling to keep their business rather than venturing into new business. As a result, regardless of the manner of initiation of businesses, the preservation of business from failure is more important than business startups.

4 Linear, logarithmic, quadratic, cubic, inverse, S, exponential, power, growth, logistic, and component 5 In statistical analysis, often in the regression-based estimations, the coefficient of determination is a suitable scale for measuring the “goodness of fit” of one or more independent variables to the dependent variable. On the other hand, in regression estimates, the dependent variable is beyond the control of the researcher, but the independent variable is achievable. To this end, researchers use the independent variable to predict the changes in a dependent variable. The coefficient of determination index is also used to measure the accuracy of prediction. This indicator, commonly known as “R-square,” takes a value between 0 and 1. Typically, the closer the indicator to one indicates who the model is accurate in the estimation of dependent variable fluctuations, and also the closeness of the determination coefficient to zero demonstrates that fit is inappropriate. Additionally, for a model with more than one independent variables, the adjusted R-square is the best coefficient for determining the best-fitted model. Generally, the coefficient of determination (/or adjusted Rsquare) indicates whether one or more independent variables are capable of predicting the level of variation in the dependent variable or not.

3.7 Entrepreneurship Reliability Assessment

55

Fig. 3.6 Scatter plot of the rate of entrepreneurial activities versus the rate of exit from business. Source: Authors’ own figure

3.7.3

Computation of Entrepreneurship Viability Index

After having the considered outcomes given by the appraisements in the previous subsections, this section attempts to generate an index that can be used to assess the entrepreneurship reliability. Since there is no variable about the business life in the Global Entrepreneurship Monitor (GEM) dataset, thus, by using the total entrepreneurial activity rate and the rate of exit from business, we will try to create an alternative for the lifetime of entrepreneurial activities at the country level. Let’s stick with our running example, the car manufacturing company, again. As you studied, 5000 cars have been stored in the storehouse of the factory, and also 400 cars are being sold annually in this factory. Thereby, if this factory fails to produce any car from now, its products will be finished after about 12.5 years. This means that the company will go bankrupt after about 12.5 years. With this argument, and given the rate of entrepreneurial activities and also the rate of exit from business which have been sorted in Tables 3.1 and 3.2, for example, the rate of entrepreneurial activities in Angola in 2018 is 55.0663%. This means that the probability of being an entrepreneur in Angola is about 55.06%. If we assume that no new entrepreneurial activities are carried out in this

56

3 Entrepreneurship Viability

country since 2018, how long will it take these remaining entrepreneurial activities (55.06%) to stop? To respond to this question, we must have the rate of exit from the business in the country. As the rate of exit for business in Angola during the past 12 months (1 year) is 18.81%, therefore 18.81% out of 55.06% of entrepreneurial activities in this country will fail per year. On the whole, the Entrepreneurship Viability Index (EVI) in Angola can be calculated as follows: Rate of Entrepreneurial Activities 55:06 ¼ Rate of Exit from Business 18:81 ¼ 2:9271

Entrepreneurship Viability ¼

There is a need to emphasize again that the computation of this index has been carried out subject to important restrictive assumptions set forth below: 1. Imagine no new business starts up in the country after the year 2018. 2. The viability of entrepreneurship in each country has been calculated after the year 2018 [this is a quasi-lifetime6 variable that we name it the Entrepreneurship Viability Index (EVI)]. In general, we calculate the Entrepreneurship Viability Index (EVI) across GEM member countries by applying the following equation. EAI j : Entrepreneurial Activities Index of jth country EB j : Rate of Exit from Business of jth country Therefore, using these two definitions, we calculate the Entrepreneurship Viability Index as follows: EVI j ¼

EAI j EB j

ð3:29Þ

where EVIj denotes the Entrepreneurship Viability Index of jth country. For more details, consider the other example below. Suppose the rate of entrepreneurship in a country is 40% and the rate of exit from business during the past 12 months is 10%. Now, imagine no new business launches in this country, and so the viability of entrepreneurship in this country is 4 years. That is, entrepreneurship is expected to last for a maximum of 4 years in the country under study. See Fig. 3.7.

6

Quasi-lifetime, pseudo-lifetime, or semi-lifetime may be used equivalently throughout this book.

3.7 Entrepreneurship Reliability Assessment

57

Fig. 3.7 Entrepreneurial Activities as a parallel system. Source: Authors’ own figure

This figure illustrates a conceptual model of the relationship between entrepreneurial activities, exit from business and entrepreneurship viability. As shown in Fig. 3.7, the index of entrepreneurship at the beginning of the first year is 40%, at the beginning of the second year is 30%, at the beginning of the third year is 20%, and at the beginning of the fourth year is 10%. Finally, in the fourth year, this 10% will end, and eventually at the beginning of the fifth year, there will be no entrepreneur (entrepreneurial activity) in this community. The process of exit from the business in this country is done with a parallel series algorithm. This means that with the loss of 10% of entrepreneurs in the first year, the economy of this community will remain active with the remainder of entrepreneurial activities (30%). So, with the loss of 10% of entrepreneurial activities, it will continue with 30% along the next 3 years, and the entrepreneurship system will remain active with 30%. At the end of the fourth year, the last 10% will end, and the entire entrepreneurial system in this community will be destroyed. Finally, the total lifetime/viability of entrepreneurship in this country is 4 years. In Table 3.6, we ranked the member countries according to the Entrepreneurship Viability Index (EVI). Note that this classification is only on the basis of the quantity of the entrepreneurial activities rather than the quality of business which takes a positive effect on the economic (the rate of efficient entrepreneurial activities which have a substantial effect on the economic growth have been discussed in the next chapters). With respect to this table, Indonesia, Switzerland, and South Korea have the highest rates of entrepreneurship viability. Indonesia, with more than 30 years of Entrepreneurship Viability Index (EVI), is ranked in the first position among the 49 member countries of the Global Entrepreneurship Monitor (GEM) in the year 2018, while Angola, despite the fact that it has the highest rates of entrepreneurial activities, has gained the 45th position. Morocco, Egypt, and Israel also have the lowest amount in terms of the Entrepreneurship Viability Index. Therefore, the entrepreneurship in these countries will last for less than 3 years.

Netherlands Japan

6 7

11.73487 11.12485 10.55405 10.33033 9.703312 9.450895

12.28092 12.17838

12.58568

13.45875

30.82845 18.604 18.20197

Entrepreneurship Viability Index

Source: Authors’ own table

8 9 10 11 12 13

Brazil Germany Spain Taiwan Lebanon Italy

Madagascar

5

4

Indonesia Switzerland South Korea Poland

Country

1 2 3

Rank

21 22 23 24 25 26

19 20

18

17

14 15 16

Rank

China United Kingdom Guatemala Argentina Russia Luxembourg Thailand Turkey

United States Slovenia

Bulgaria Ireland Colombia

Country

Table 3.6 Entrepreneurship Viability Index (EVI)

7.293456 7.193914 7.129704 7.059676 6.995402 6.973248

7.791408 7.60807

7.924414

8.004045

8.910628 8.386501 8.278036

Entrepreneurship Viability Index

34 35 36 37 38 39

32 33

31

30

27 28 29

Rank

Cyprus Austria Canada Iran India Peru

Puerto Rico Greece Chile

Croatia

Slovakia Panama Qatar

Country

5.779547 5.4489 5.395402 5.267556 4.733618 4.532648

6.041174 6.026919

6.086657

6.178411

6.594529 6.586767 6.513121

Entrepreneurship Viability Index

47 48 49

45 46

44

43

40 41 42

Rank

Angola Saudi Arabia Israel Egypt Morocco

United Arab Emirates Sudan

France Sweden Uruguay

Country

2.647753 2.351543 1.773998

2.926409 2.837079

4.033144

4.057261

4.520283 4.231303 4.223421

Entrepreneurship Viability Index

58 3 Entrepreneurship Viability

3.7 Entrepreneurship Reliability Assessment

59

Table 3.7 The ANOVA for comparing mean values of Entrepreneurship Viability Index (EVI) into three economic groups Between groups Within groups Total

Sum of squares 76.354 242.122 318.476

df 2 40 42

Mean square 38.177 6.053

F 6.307

Sig. .004

Source: Authors’ own table Table 3.8 Mean value of Entrepreneurship Viability Index into triple economies

Economy Low-income Middle-income High-income Total

Mean of Entrepreneurship Viability Index 4.734065 7.691843 7.920300 6.782069

Source: Authors’ own table

No doubt, the high value of EVI does not signify the goodness and excellence of the country in the field of entrepreneurial activities. This index only refers to the lifetime of entire entrepreneurial activities all over member countries. Additionally, this index never indicates the quality of entrepreneurship. To observe and ensure the existence of a difference between the mean values of the Entrepreneurship Viability Index (EVI) in different economic classifications, there is a need for examining this variable using some hypothesis testing methods. On the one hand, the missing values and on the other hand the lack of appropriate sample size are the factors that hold back us to test parametric hypothesis testing methods to appraisal the quantity of this index among triple economies. Although, because of the lack of data to prove claims, doing some statistical hypotheses tests is not allowed, there are several nonparametric methods that researchers are authorized to use them in such cases. The following results (Table 3.7) have been obtained using the Tukey hypothesis testing method. The results of one-way analysis of variance (ANOVA) are as follows: This analysis of variance shows that there is a significant difference between the averages of different economic groups at the 95% level. In fact, business lifetime (entrepreneurship viability) has a direct impact on the development of countries. Notwithstanding the rate of entrepreneurship in high-income countries is low, but due to the low rate of exit from the business, the index of entrepreneurship viability in these countries is high. See Table 3.8.

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3 Entrepreneurship Viability

Evaluating the Entrepreneurship Reliability

In this section, we aim to examine entrepreneurship reliability based on the data of the Global Entrepreneurship Monitor dataset in 2018. As mentioned in the previous sections, there is an essential need of complete (or censored) lifetime data to check the reliability of a system (herein entrepreneurship). Since among the various variables studied by the GEM, none of these variables indicate the lifetime of entrepreneurship. Thus using the inference outlined in the previous section, we calculated the Entrepreneurship Viability Index among 49 countries in 2018. Finally, the created index can be used to assess the entrepreneurship lifetime. As the first use of this index, we are going to apply it in the reliability analysis. Generally, all things described in the previous sections (ranging from the functions related to the reliability) will be applied in the next sections to clarify the applicability of this index with different methods of entrepreneurship analysis. Our ultimate goal in this section is to answer some critical questions about entrepreneurship reliability. Some of the remarkable questions which will play a fundamental role in future researches are summarized, inter alia, as follows. Moreover, at the end of this subsection, we will try to give a precise response to such questions. • Can a density function be fitted to the entrepreneurship lifetime data? • What is the probability density function (PDF), cumulative distribution function (CDF), hazard function, and the reliability function of the Entrepreneurship Viability Index? • What is the average age of entrepreneurship around the globe (across member countries)? • Will entrepreneurship reliability increase from 2018 onward? • How will the hazard function of Entrepreneurship Viability Index (EVI) change after the year 2018? And so forth. These questions are some of the important points that scholars try to find their response. Besides, on the whole, all estimates, fittings, graphs, and data simulations are derived from the Bootstrap method which is extracted by the R programming language. The following sections will theoretically present the probability density function (PDF), cumulative distribution function (CDF), entrepreneurship reliability function, and entrepreneurship hazard function calculated using a nonparametric manner throughout the following section.

3.8 Evaluating the Entrepreneurship Reliability

3.8.1

61

Statistical Functions of EVI

In this section, we intend to introduce the best-fitted probability density function onto entrepreneurial durability data. Hence, using probability theory and statistical methods, we first plot the empirical probability density function graph for proposing the best candidates for estimating the probability density function of the data under study. The first step in estimating the probability density function for a set of data (including survival data) is to observe its empirical probability density function. The estimated nonparametric graph is based on the histogram chart, and, by observing this nonparametric graph, researchers will be able to select the best candidates among other probability density functions for fitting on the existing data. As you studied, in the equations of the empirical probability density probability, this function can be calculated without any limiting assumption (refers to a fully nonparametric method), and the nonparametric methods have been fully utilized in its calculation (see Fig. 3.8.). This function is empirically obtained, and there is no limiting assumption in the plot of the probability density function of the Entrepreneurship Viability Index. In order to use the programming codes of the empirical probability density function plots, see Appendix D.

Fig. 3.8 Empirical probability density function (PDF) of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure

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3 Entrepreneurship Viability

This chart illustrates the PDF of the entrepreneurship viability which has empirically plotted by R programing language. As this plot demonstrates, the range of changes in this variable is in the range from zero through infinity, and its maximum value is at the beginning of the interval (zero to 10 years) of the horizontal axis. In other words, the maximum lifetime of entrepreneurship in most countries is less than 10 years. In addition, some of the data are in the range of 15–20 years, and a little group lasts for over a period of 30 years. It seems that with the increase in the sample size (with the increase in the number of participating countries in the Global Entrepreneurship Monitor surveys), this chart has three modes. This means that the average age of businesses in most countries (probably underdeveloped countries) is less than 10 years and in some of the countries (efficient-driven economies) is between 15 and 20 years and for a small number of countries (innovative-driven) is more than 30 years. Perhaps with applying a big sample size, it can be proven that business age may be determined by the breakdown of different regions of the countries. For example, in underdeveloped countries, the life span of businesses is much lower than in developed countries. In this case, using nonparametric (or parametric) statistical methods, we can use a mixture of probability distribution functions so that the probability density of these data can come up from the combination of three different probability densities. Following the estimated probability density function and with the use of the statistical terms and methods, these data are skewed to the left, and, additionally, the well-known functions that can be considered as candidates for fitting to the lifetime data of entrepreneurship are: • • • • • •

The exponential probability density function The gamma probability density function The truncated normal probability density function from the left at the zero The Weibull probability density function The truncated lognormal probability density function from the left at the zero And so on

These density functions can be deemed as candidates for fitting to entrepreneurial durability data. It should be noted that selecting an appropriate probability density function to the data requires some statistical analysis to perform the best estimation with the least possible error. On the other hand, in order to prevent the provision of non-specialized content, an empirical method for selecting the best candidate is generally proposed by statisticians. Hence, selecting the best probability density function for the entrepreneurship viability data will be empirically and intuitive.

3.8 Evaluating the Entrepreneurship Reliability

3.8.2

63

Empirical Functions of EVI

0.8 0.6 0.4 0.2 0.0

Empirical Cumulative Distribution Function

1.0

In order to more accurate estimation and to propose a more proportional probability density function, in some cases, in addition to the empirical probability density function, researchers need to draw the cumulative distribution function. In this regard, what you will encounter in this subsection is the analysis of the empirical cumulative distribution function of the entrepreneurship viability data. In summary, we have evaluated and drawn the cumulative distribution function (CDF) of entrepreneurship lifetime data. Using the empirical cumulative distribution function method, we have estimated the empirical cumulative distribution function (ECDF) that is mentioned in the previous sections. Generally, considering the Entrepreneurship Viability Index as the lifetime variable, the graph of its ECDF is as follows (the programming codes of this figure is written using R programing language, see Appendix E) (Fig. 3.9). As the graph displays, the plotted empirical cumulative distribution function represents that this data is divided into almost three different clusters (groups): first, a sub-society whose entrepreneurship lifetime within that is less than 10 years (see the first top point of stair function plot); second, a group whose lifetime between about 12 and 18 years (find the second top point of the curve); and the third

0

5

10

15

20

25

30

Time (year)

Fig. 3.9 The empirical cumulative distribution function (ECDF) of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure

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is related to a society whose lifetime more than about 20 years (observe the third top point of the said plot). One of the problems we meet in this study is the low sample size. If the volume of countries in this study was more than 100 countries, we could accurately interpret the entrepreneurship lifetime by economic segmentation. So one of the solutions that can help in these situations is using the simulation method. Both the empirical probability density function and the empirical cumulative distribution function are distinguishing three different groups in the entrepreneurship viability dataset. In another word, because the size of data is not large enough, so in order to avoid the estimation massive error in process of the probability density function assessment, we first want to increase the amount of data using a simulation method. After simulating the data, we will estimate the probability density function of the estimated data. Finally, using this simulated probability density function, we will able to propose the best candidate for entrepreneurship viability data. Doing so not only applies a fewer error into the model but leads to adopting the best-fitted family of distribution onto the data (herein the EVI). In doing the simulation process, with the consideration of the main sample, a new sample with a large size shall be generated that has the characteristics of the main sample as well. Among a variety of simulation methods, including the Monte Carlo Markov Chain (MCMC), Bootstrap, Metropolis, etc., we will use the Bootstrap method which has been extensively introduced in the previous sections. In this section, we will simulate a sample of 285 using the Bootstrap method on the basis of the original sample. The probability density function and the cumulative distribution function of entrepreneurship lifetime (entrepreneurship viability) of 49 countries have been plotted, and additionally the simulated sample, by the Bootstrap method, has plotted for 285 samples in Figs. 3.10 and 3.11. The programming codes are in Appendices I and J. Figure 3.10 refers to the probability density function of real entrepreneurship viability among 49 countries of the GEM’s members in 2018 (blue solid line). In addition, it demonstrates the probability density function of the simulated data using the Bootstrap method with the size is 285 (the dashed red line). As you observe, although the size of the simulated entrepreneurship viability sample is more than five times greater than the real sample, the probability density functions of these two samples are approximately fitted onto each other. Additionally, the cumulative distribution function of both simulated and real sample is as follows (the programming codes is in Appendix J). The cumulative distribution function of entrepreneurship viability data from 49 countries (the real data) is drawn with blue color, and the cumulative distribution function of generated data of viability of entrepreneurship by the Bootstrap method is drawn by the red line. As is illustrated, this graph also proves the high accuracy of the simulated sample by the Bootstrap method. In the following sections, we will estimate the well-fitted probability density function for entrepreneurship viability data. The

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65

Fig. 3.10 The probability density function (PDF) of the real Entrepreneurship Viability Index (EVI) and the probability density function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure

entrepreneurship reliability function and the entrepreneurship hazard function are also plotted in Figs. 3.12 and 3.13. The programming codes of this figure are written using R programming language (see Appendix F). Figure 3.12 refers to the real entrepreneurship reliability function for 49 countries (blue graph), and also it displays the entrepreneurship reliability function using Bootstrap simulated data (red charts). The reliability function in this graph shows that businesses with a lifetime of fewer than 10 years have the highest reliability worldwide. When their age is more than 10 years, their reliability declines sharply. In other words, it can be said that in their first years of life, businesses have the highest profitability for entrepreneurs, and over time (especially from year 10 onward), this credibility in business durability/ viability drops sharply. Note that this does not mean that countries with low EVI have the most reliable business (and vice versa). This only refers to the increase in the degradation of businesses by passing time that shows businesses will lose their reliability by passing time. Obviously, both graphs (Figs. 3.12 and 3.13) are almost identical and show high accuracy of the Bootstrap simulation method. The reliability function is a complement function of cumulative distribution. This diagram shows that entrepreneurship reliability (without the initiation of any new business) will decline over the next 30 years. This conclusion implies that the entrepreneurship hazard function will

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Fig. 3.11 The empirical cumulative distribution function (ECDF) of the real Entrepreneurship Viability Index and the empirical cumulative distribution function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure

Fig. 3.12 The real reliability function of Entrepreneurship Viability Index (EVI) and the reliability function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure

67

0.3 0.1

0.2

Hazard

0.4

0.5

3.8 Evaluating the Entrepreneurship Reliability

5

10

15

20

25

30

Time (year)

Fig. 3.13 The hazard function (hazard rate) of the real Entrepreneurship Viability Index and the hazard function of the simulated EVI by applying the Bootstrap method. Source: Authors’ own figure

increase over time (see Fig. 3.13). (The programming codes of this figure are in Appendix F.) The hazard function which represents the level of risk and shows the rate of factors threatening the health of entrepreneurship over time (in other words, this function calculates the rate of failure) is drawn up for real entrepreneurship viability data of 49 countries and simulated entrepreneurship viability. Consequently, the graphs of a probability density function, the cumulative distribution function, the reliability function, and the entrepreneurship hazard function indicate that in order for a precise estimation of entrepreneurship reliability and to fit a density function on these lifetime data, the Bootstrap method can be used, instead of the real data of 49 member countries. To avoid the growth of the data analysis error, we will use real data throughout this chapter. Our purpose of data simulation was to select the best candidate for the probability density function for entrepreneurship viability data. Therefore, after obtaining the best probability density function for the Entrepreneurship Viability Index, in order to verify the characteristics of the real data, we will only use the data of 49 member countries. In the following subsections, we will try to fit the best probability density function to the Entrepreneurship Viability Index (EVI).

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3 Entrepreneurship Viability

The PDF of Entrepreneurship Viability Index

Analyzing data is one of the ways to gain insight and uncover the new information about the dataset under study. Examining and analyzing the characteristics of a sample (herein the lifetime of entrepreneurship) can be done with the use of the nonparametric or parametric statistics methods. In the nonparametric statistics method, researchers consider the probability density function of a variable as an unknown function, and, in this regard, they will endeavor to estimate the density function using relevant methods. In the nonparametric method, the probability density function has not a closed form. Additionally, because of lack of a distinct formula, most of its properties are not assessable by a mathematical equation; and also in most cases, in order to estimate some unknown parameters (such as mean, variance, etc.) through nonparametric method, there is an urgent need for the usage of computer and statistical software. In contrast, in the parametric method, it is assumed that the data (herein the business lifetime) has a probability density function but with unknown parameters. After finding the unknown parameters of this predetermined probability density function, other features of this function can be calculated using mathematical formulas without any need for software and computer. Therefore, the basic distinction between parametric and nonparametric methods is the existence or lack of a specified probability density function. All in all, in the parametric methods, scholars estimate the parameters of a known density function, which is derived from a well-known family, whereas in the nonparametric methods the probability density function, which is unknown, and also the study of this probability density function requires software and complex equations that can only be measured by a computer. Although in the nonparametric methods it is so hard to work with an unknown and intricate mode, the consequences of such analysis will be along with the most realistic results in the field of interest. As mentioned in the previous sections, the Weibull distribution function is one of the best possible functions that widely used in lifetime topics, and its capability of accurate analysis has been documented by a large number of authors. Further, in continue, you will observe the goodness of fit of the Weibull distribution function. On the other hand, due to diverse and unlimited behaviors that exist in the Weibull hazard function, this distribution function can predict the reliability of entrepreneurial lifetime with more flexibility. To this end, because of the unique characteristics and capabilities of the Weibull family, we have used this distribution function as a candidate for fitting onto the entrepreneurship lifetime data (Entrepreneurship Viability Index). As you studied in the previous section, the equation for the Weibull probability density function is as follows:

3.8 Evaluating the Entrepreneurship Reliability

69

t α α f α,β ðt Þ ¼ t α1 eðβÞ , β

t > 0,

α, β > 0

Moreover, the cumulative distribution function and its reliability function are written respectively as t α

F ð t Þ ¼ 1  e ð β Þ ,

t > 0,

α, β > 0

And t α

Rðt Þ ¼ 1  F ðt Þ ¼ eðβÞ ,

t > 0,

α, β > 0

Finally, the hazard function of this distribution is hð t Þ ¼

f ðt Þ α α1 ¼ t , Rðt Þ β

t > 0,

α, β > 0

As you noticed in this probability density function, there are two unknown parameters that can be estimated using several methods. The two most important methods which are being used in estimating parameters are itemized below. • Maximum likelihood estimator (MLE) • The Bayesian method estimator The maximum likelihood method is a frequency-based method that is acceptable to many statisticians. On the other hand, because of considering the prior data in parameter estimation by the Bayesian method, the Bayesian method is a more logical approach. In the case of parameters estimation, although Bayesian methods are much more accurate than the maximum likelihood method, but since this method requires a large amount of information7 and additionally needs to apply an intricate methodology, we will try to use the maximum likelihood estimator (MLE) method to estimate the unknown parameters of the Weibull distribution.

3.8.3.1

Estimation of the Weibull Parameters

In the section of estimating by the maximum likelihood method, the details of this method are fully described. Therefore, we will only apply this manner in this section. The steps of applying the maximum likelihood method for the unknown parameters of the Weibull distribution are as follows:

7

Because the sample size of entrepreneurship viability is fewer than the required size, the Bayes’ law cannot be applied herein.

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The overall model of a joint probability density function of X1, X2, . . ., Xn that we named it L(θ), not so arbitrarily, is LðθÞ ¼ PðX 1 ¼ x1 , X 2 ¼ x2 , . . . , X n ¼ xn Þ ¼ f ðx1 ; θÞ:f ðx2 ; θÞ . . . :f ðxn ; θÞ Yn f ðxi ; θÞ: ¼ i¼1 where θ represents a parameter vector and f(x; θ) denotes the density function of variable X. Specifically, we replace (α, β) with θ and Weibull probability density function with f(x; θ). So t1 α tn α α α LðθÞ ¼ t α1 eð β Þ  . . .  t nα1 eð β Þ ¼ 1 β β

 n Y ti α n α t α1 e ð β Þ i i¼1 β

ð3:30Þ

Since the logarithm is an ascending function, we apply the logarithm function to the two sides of the equation, according to the method studied.   n n  α X X α ti þ ð α  1Þ ln ðt i Þ  ιðα, βÞ ¼ LnðLðα, βÞÞ ¼ n ln β β i¼1 i¼1

ð3:31Þ

In order to achieve the maximum likelihood estimators of the α and β parameters, we need to take the first derivative with respect to the unknown parameter α, and then we put it equal to zero and will solve the equation. Moreover, in the next step, we repeat the said process by considering β as the unknown parameter and set it equal to zero. Since the first derivative of this equation is not solvable, this equation cannot be solved by the mathematical method. On the whole, by using numerical analysis methods, it will be possible to calculate the estimators of these parameters and then with the coding in R the estimated values of the unknown parameter would be achievable. Numerical analysis methods are used to calculate the approximate value of unknown parameters in the complex models. In addition to the advantages, many models of numerical analysis that are presented by scientists crept a significant error into the model. One of the most commonly used methods for estimating the unknown parameters, in which the amount of error is negligible, is the Newton–Raphson8 method. Given the high accuracy of this method as well as its applicability, we will also use the Newton–Raphson method to estimate two unknown parameters in the above equation.

8

In numerical analysis, Newton’s method (also known as the Newton–Raphson method) is called in the honor of the Newton and Joseph Raphson, which is a method for finding a better approximation of a real function based on its roots.

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71

The result of the maximum likelihood estimation of the unknown parameters (α and β) in the Weibull distribution have been calculated using the Newton-Raphson method by the programming language R. Note that the entrepreneurship lifetime data from 49 member countries in 2018 have been considered as the primary data. The code for programming of these maximum likelihood estimations is in the Appendix section (see Appendix G). Maximum likelihood estimation Newton–Raphson maximization, 8 iterations Return code 2: successive function values within tolerance limit Log-likelihood: 138.698 2 free parameters Estimates: Estimate, Std. error, t value, Pr(> t) Alpha 1.7791 0.1748 10.18 0

1:7791

,

t>0

ð3:33Þ

ð3:34Þ

Based on statistics and probability theory, one of the metric instruments to reject or accept the claim in the hypothesis tests is the p-value. When this value is more than error type one (which is indicated by α), the claim is accepted and will reject otherwise.

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Finally, the hazard function of entrepreneurship viability data is estimated as follows: hðt Þ ¼

f ðt Þ ¼ 0:1979 t 0:7791 , Rðt Þ

t>0

ð3:35Þ

Moreover, the average time to failure (average value of entrepreneurship viability) is calculated here: Zþ1 MTBF ¼

Z

þ1

Rðt Þdt ¼ 0

Z

þ1

tf ðt Þdt ¼

0

t  0:1979 t 0:7791 eð8:9876Þ t

1:7791

dt

0

¼ 7:997254 It is obvious that the average life span of businesses in different countries is different, and, with having data on the longevity of businesses in each country, their average lifetime can also be calculated individually. What has been calculated in this equation shows that the average lifetime (mean time to failure) of businesses in the global community is approximately equal to 8 years.

3.8.3.2

Goodness of Fit

In mathematical and statistical analyses, usually after predicting the model of the given data, parametric and nonparametric tests are carried out to confirm or reject the model accuracy at the given error level. The Kolmogorov–Smirnov method is one of the most common nonparametric methods used by researchers in such circumstances. This method is usually used to test the hypotheses of normal distribution and the “goodness of fit” test. To test the goodness of fit of the Weibull distribution function with estimated parameters for the lifetime of entrepreneurship, we used the Kolmogorov–Smirnov’s hypothesis test with the Weibull kernel instead of Normal kernel. The programming codes of this test are in the Appendix section (see Appendix H). One-sample Kolmogorov–Smirnov test Data: Entrepreneurship viability D ¼ 0.1274, p-value ¼ 0.3729 Alternative hypothesis: two-sided These results are significant at the level of 95%. Because the p-value is more than 0.05 (which is 0.3729), so there is no evidence to reject the claim of this hypothesis (that referred to the accuracy of Weibull distribution). Therefore, this result indicates that the entrepreneurship viability for 49 countries in the year 2018 has a Weibull distribution with estimated parameters by the maximum likelihood method.

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Fig. 3.14 The QQ plot of the Entrepreneurship Viability Index (EVI). Source: Authors’ own figure

In other words, the Kolmogorov–Smirnov hypothesis test shows that there is no sensible reason to ignore the Weibull distribution as the most proper distribution of the Entrepreneurship Viability Index. Therefore, from the statistics view, with 95% of confidence, it can be said that entrepreneurship viability data have been extracted from a Weibull family. After having considered particular evidence given by these hypothesis tests, specifications of entrepreneurship viability can be easily distinguished using the characteristics of the Weibull distribution function with estimated parameters. For example, the average time to failure for the entrepreneurship viability across the 49 participated countries has been calculated equal to 7.99 years. As a result, using the inferential statistics in which the findings from a sample can be generalized to the population, the average time to failure of entrepreneurship viability is about 8 years across the globe. Obviously, the factors that lead to a longer life span of a business depend heavily on the needs of the community. If the products produced in business are in line with the needs of a community (i.e., innovation and durability of products are standard), then the viability of this business goes beyond the average life span of businesses worldwide. The QQ plot figure of entrepreneurial lifetime data is also proving that the results above are true. For programming codes, see Appendix H (Fig. 3.14). As this QQ plot diagram demonstrates, because entrepreneurship lifetime data are around the line y ¼ x, therefore the accuracy of the Kolmogorov–Smirnov test in the

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above diagram can also be verified. It can be said that the selection of the Weibull distribution as a distribution of the lifetime of entrepreneurial activities for member countries of GEM in 2018 is acceptable. Now, with the confirmation of these outcomes, it is possible to do careful investigation on the entrepreneurship lifetime using the estimated functions (density function, cumulative distribution function, hazard function).

3.9

Evaluating the Fitted Distribution Functions

After fitting the Weibull distribution function onto the entrepreneurship viability data, we are going to compare the simulated functions with the actual functions of entrepreneurship viability data. In a different circumstance, with regard to plotted graphs, we intend to evaluate the goodness of fit of Weibull distribution for entrepreneurship viability data.

3.9.1

Simulation of the Density Function

As stated in the previous sections, we conclude that entrepreneurship viability data has a Weibull distribution with known parameters (which were estimated by the maximum likelihood method). The graph of the empirical probability density (factual function) and the estimated density function (Weibull distribution) for entrepreneurship viability data are presented in Fig. 3.15. The programming codes of this figure are in Appendix I. According to Fig. 3.15, evidently, the estimated probability density function (Weibull probability density function) is approximately laid on the empirical probability density function. If the outliers are eliminated from this sample set, both density functions will fit perfectly onto each other. Meantime, due to the small sample size, some units of the sample are not predictable. In the cases of high sample size, the useful method for estimating all variation of the sample is usually based on the mixture of density functions in a nonparametric manner. As a brief explanation, to estimate the density functions in the multi-modal forms (as shown in Fig. 3.15, the empirical density function has three modes), the method of mixed density functions is used. For example, to have an accurate estimation of the density function of data under study, we can use the mixture of three Weibull density functions with different parameters and weights. In future research, we will try to obtain a precise estimate of the mixture density function by increasing the actual sample size (rather than Bootstrap simulation approach). The estimated Weibull probability density function, which has been fitted to entrepreneurship viability for 49 countries (in 2018), is acceptable at the level of

3.9 Evaluating the Fitted Distribution Functions

75

Fig. 3.15 The real probability density function (PDF) and the simulated PDF (Bootstrap-based Weibull probability density function with shape parameter ¼ 1.7791 and scale parameter ¼ 8.9876). Source: Authors’ own figure

95%, and this density function is generalizable for analyzing the entrepreneurship viability in the coming years.

3.9.2

Simulation of the CDF

In order to view the accuracy of this estimation for the cumulative distribution function, we plot the following graph (Fig. 3.16) which includes the empirical cumulative distribution function (for real data) and the estimated cumulative distribution function (the Weibull CDF). The programming codes are in Appendix J. According to the Kolmogorov–Smirnov test results with a null hypothesis of the Weibull distribution, these graphs show that the behavior of the distribution function estimated for entrepreneurship viability data is approximately similar to the empirical cumulative distribution function. Therefore, in order to predict the behavior of entrepreneurship viability, we can use the Weibull cumulative distribution with estimated parameters. It should be noted that by changing the data in the years of the future or past, new parameters can be updated using the maximum likelihood method. Also, the cumulative distribution function is one of the most important statistical-based functions of the data that contains many characteristics of the data under study.

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Fig. 3.16 The real empirical cumulative distribution function (ECDF) and the simulated CDF [Bootstrap-based Weibull Distribution Function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876)]. Source: Authors’ own figure

The Weibull cumulative distribution function as a precise estimator for entrepreneurship viability in the world can have broad applications in the field of business lifetime investigations.

3.9.3

Simulation of Reliability and Hazard Functions

The reliability function and the hazard function of entrepreneurship viability, as the important criteria and beneficial to lifetime assessments, in this section are presented simultaneously (see Fig. 3.17). The reliability function is the complement of the cumulative distribution function. Hence, the variation of the CDF will have an inverse result in reliability function. Since the reliability function refers to the proportion of products whose lifetime exceeds a certain value (t), it is obvious that as the time (t) increases, the ratio of the products will also decrease. Hence, decreasing the reliability function is a normal event in the issues related to the lifetime. What is important here is the risk that is threatening entrepreneurial activities. In order to investigate the hazard rate that measures the number of unforeseen dangers,

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77

Fig. 3.17 Upper figure: the real reliability function and the simulated reliability function with the use of the Weibull probability density function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876). Lower figure: the real hazard function and the simulated hazard function with the use of Weibull probability density function with known parameters (shape ¼ 1.7791, scale ¼ 8.9876). Source: Authors’ own figure

which menace entrepreneurs in the world, we also examined the hazard function of entrepreneurship viability as referred to in Fig. 3.17. As this chart shows, the hazard function of setting up businesses is growing worldwide which can be a reason to increase the rate of exit from business. With having adequate data from each country, we will be able to estimate the survival/ reliability function for entrepreneurial activities thereto. In this case, the hazard function in underdeveloped countries (factor-driven economies) may be incremental but consider that the degree of hazard function in developed countries (innovationdriven economies) may often diminish. All in all, this study is preliminary research on the life span of business worldwide which is expected with the growth of data sources, providing more accurate analyses across regions or even into different countries become feasible. Clearly, according to Fig. 3.17, the reliability function and hazard function of entrepreneurship viability are similar to their real empirical curves. This also reflects the fact that the Weibull distribution and the hazard rate of entrepreneurship viability are acceptable to examine the status of businesses’ lifetime across the globe. Eventually, with the availability of these four functions in analyzing the survival of entrepreneurship, a large portion of the questions of scholars can be answered accurately.

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Conclusion and Assessment

3.10.1 Overview As stated, our main objective in this chapter was to fit a probability density function to the entrepreneurship viability. Since there is no variable about the lifetime of businesses in the questionnaire of the Global Entrepreneurship Monitor (GEM), hence in order to achieve a precise answer to the analysis of entrepreneurship lifetime, we attempted to initially find a variable as an alternative for the entrepreneurship lifetime. As indicated in the methodology section, we generated a new index using the total entrepreneurial activities rate and the rate of exit from business. We also ranked the GEM member countries based on this index, and finally we named that, not so arbitrarily, the Entrepreneurship Viability Index (EVI). In the next steps, we proved that entrepreneurship viability is significantly different in various economic groups. This means that the average value of the entrepreneurship viability among high-income countries is higher than that of low-income countries. On the other hand, given that the testing of statistical hypotheses by applying small size samples is not scientific, so we decided to increase the sample size using the Bootstrap simulation method. Eventually, using parametric methods, we could fit the Weibull distribution function to the entrepreneurship viability index. In continuation, using the Kolmogorov–Smirnov test and the QQ plot method, it has been shown that entrepreneurship viability data extract from a Weibull distribution function with the estimated parameters. In summary, it is worth noting that finding the probability density function of entrepreneurship viability data provides researchers the opportunity to assess the characteristics of entrepreneurship viability easily and accurately around the globe.

3.10.2 Future Research As you observed during this chapter, the shortfall of a time-dependent variable induced us to create the life span data of businesses by applying a challenging method. In fact, this shortcoming forced us to develop a new index that is necessary for future research named the Entrepreneurship Viability Index (EVI). Undoubtedly, the existence of accurate information about the start and end date of entrepreneurial activities will allow us to conduct comprehensive and accurate studies on the lifetime of entrepreneurship at the country level. Some of these questions, which will be of further assistance to researchers in the analysis of entrepreneurship viability, are brought in Table 3.9.

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Table 3.9 Suggested questions for gathering more information about entrepreneurship life span for doing more accurate survival analysis Subject Rate of failed business until now

Question Have you ever launched a business that has completely failed?

Date of starting a failed business

What is the initiation date of your failed business?

Date of business failure

On what date your said business is broke?

Answer Yes ¼ 1 No ¼ 0 Don’t know ¼ 1 Refused ¼ 2 Date Don’t know ¼ 1 Refused ¼ 2 Date Don’t know ¼ 1 Refused ¼ 2

Source: Authors’ own table

References Acs Z (2006) How is entrepreneurship good for economic growth? Innov Technol Governance Global 1(1):97–107 Acs Z, Storey D (2004) Introduction: entrepreneurship and economic development. Reg Stud 38 (8):871–877 Andersson P (2010) Exits from self-employment: is there a native-immigrant difference in Sweden? Int Migr Rev 44(3):539–559 Audretsch DB, Keilbach M (2004) Does entrepreneurship capital matter? Enterp Theory Pract 28 (5):419–429 Birolini A (2013) Reliability engineering: theory and practice. Springer Science & Business Media Block J, Sandner P (2009) Necessity and opportunity entrepreneurs and their duration in selfemployment: evidence from German micro data. J Ind Compet Trade 9:117–137 Borjas G (1986) The self-employment experience of immigrants. J Hum Resour 21:487–506 Brüderl J, Preisendörfer P, Ziegler R (1992) Survival chances of newly founded business organizations. Am Sociol Rev 57(2):227–242 Chiang AC, Wainwright K (2004) Fundamental Methods of Mathematical Economics, 4th edn. McGraw-Hill/Irwin, New York Clark K, Drinkwater S (2000) Pushed out or pulled in? Self-employment among ethnic minorities in England and Wales. Labour Econ 7:603–628 Clark K, Drinkwater S (2010) Patterns of ethnic self-employment in time and space: evidence from British census microdata. Small Bus Econ 34(3):323–338 DiCiccio TJ, Efron B (1996) Bootstrap confidence intervals (with Discussion). Stat Sci 11:189–228 Efron B (1979) Bootstrap methods: another look at the Jackknife. Ann Stat 7:1–26 Efron B (2003) Second thoughts on the bootstrap. Inst Mathemat Stat 18(2):135–140 Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman & Hall/CRC, Boca Raton, FL. ISBN 0-412-04231-2 Faghih N, Bonyadi E, Sarreshtehdari L (2019) Global entrepreneurship capacity and entrepreneurial attitude indexing based on the global entrepreneurship monitor (GEM) dataset. Global Dev 13–55 Fairlie RW, Lofstrom M (2013) Immigration and entrepreneurship. IZA Discussion Paper no. 7669 Fertala N (2008) The shadow of death: do regional differences matter for firm survival across native and immigrant entrepreneurs? Empirical 35:59–80

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Georgellis Y, Sessions J, Tsitsianis N (2007) Pecuniary and non-pecuniary aspects of selfemployment survival. Q Rev Econ Financ 47:94–112 Haapanen M, Tervo H (2009) Self-employment in urban and rural locations. Appl Econ 41 (19):2449–2461 Hallinan AJ Jr (1993) A review of the Weibull distribution. J Qual Technol 25(2):85–93 Hayek FA (1948) Individualism and economic order. University of Chicago Press, Chicago, IL Holcombe RG (2003) Progress and entrepreneurship. Q J Austrian Econ 6(3):3–26 Kromholtz GA, Condra LW (1993) A new approach to reliability of commercial and military aerospace products: beyond military quality/reliability standards. Qual Reliab Eng Int 9 (3):211–215 Lofstrom M (2002) Labor market assimilation and the self-employment decision of immigrant entrepreneurs. J Popul Econ 15(1):83–114 Lofstrom M, Wang C (2006) Hispanic self-employment: a dynamic analysis of business ownership. IZA discussion paper no. 2101 Millan JM, Congregado E, Román C (2012) Determinants of self-employment survival in Europe. Small Bus Econ 38:231–258 Millan JM, Congregado E, Román C (2014) Entrepreneurship persistence with and without personnel: the role of human capital and previous unemployment. Int Entrep Manag J 10 (1):187–206 Munsasinghe L, Sigman K (2004) A hobo syndrome? Mobility, wages, and job turnover. Labour Econ 11(2):191–218 Nafzige EW, Terrell D (1996) Entrepreneurial human capital and the long-run survival of firms in India. World Dev 24(4):689–696 North DC (2006) Understanding the process of economic change. Princeton University Press, Princeton, NJ Nziramasanga M, Lee M (2002) On the duration of self-employment: the impact of macroeconomic conditions. J Dev Stud 39(1):46–73 Praag CM (2003) Business survival and success of young small business owners. Small Bus Econ 21(1):1–17 Roberts PW, Negro G, Swaminathan A (2013) Balancing the skill sets of founders: implications for the quality of organizational outputs. Strateg Organ 11(1):35–55 Schuetze HJ, Antecol H (2006) “Immigration, entrepreneurship and the venture start-up process”, the life cycle of entrepreneurial ventures. In: Parker S (ed) International Handbook Series on Entrepreneurship, 3. Springer, New York, pp 107–135 Schultz TW (1980) Investment in entrepreneurial ability. Scand J Econ 82(4):437–448 Schumpeter JA (1934) The theory of economic development: an inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press, Cambridge, MA Schumpeter JA (2003) Capitalism, socialism, and democracy. Routledge, London Stel AV, Carree M, Thurik R (2005) The effect of entrepreneurial activity on national economic growth. Small Bus Econ 24(3):311–321 Taylor MP (1999) Survival of the fittest? An analysis of self-employment duration in Britain. Econ J 109:140–155 Varian H (2005) Bootstrap tutorial. Mathemat J 9:768–775 Volery T, Bergmann H, Gruber M, Haour G, Leleux B (2008) Global entrepreneurship monitor. Bericht 2007 zum Unternehmertum in der Schweiz und weltweit Wennekers S, Stel AV, Thurik R, Reynolds P (2005) Nascent entrepreneurship and the level of economic development. Small Bus Econ 24(3):293–309 Wennekers S, Thurik R (1999) Linking entrepreneurship and economic growth. Small Bus Econ 13 (1):27–55 Wilesmith J, Stevenson M, King C, Morris R (2003) Spatio-temporal epidemiology of foot-andmouth disease in two counties of Great Britain in 2001. Prev Vet Med 61(3):157–170

Chapter 4

Entrepreneurial Capability Index

People of a county might be extremely capable to start a business but probably, because of some adverse environmental conditions, they will not be able to launch an appropriate business. Hence, it seems that considering both individual and environmental factors together will be helpful to present a guideline for people to run an efficient business. To this end and with respect to either individual or environmental factors, in order to measure the concept of “entrepreneurial capability,” there is a need for generating another index. That is, finding an entrepreneurship-based indicator relating to attitudes of individuals living in a country that results from the interactions of the individual and environmental factors, is our ultimate goal during this chapter. Measuring endogenous factors that come from within individuals is one of the most difficult issues that baffle scientists for a long period of time. Furthermore, allocating a score and numeric value to the reaction of a person when an event is happening is an underlying method that has been used for many years for the purpose of measuring individual behavior. A great deal of academic researches on individual attitudes has emerged rapidly with many academic articles published worldwide in recent years. Concurrent with the publication of such studies, the authors have tried to talk about the importance of individual and environmental factors along. In general, there are many remarkable endeavors behind studies relating to behavior, in which the authors have applied many methods to obtain and analyze data. According to Faghih et al. (2019), assessment of many entrepreneurship indexes entails mastering the individual factors. Entrepreneurship Capacity Index, that does not seem to be in connection with individual factors, was one of the indexes created in their recent work. In the concepts of entrepreneurship, several sub-dimensions of individual factors, which greatly influence the results of the entrepreneurial activities and annually used by Global Entrepreneurship Monitor (GEM), are as follows:

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_4

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Role model Perceived opportunities Perceived capabilities and skill Fear of failure (or risk acceptance) The intention of the business startup The motivation of the business startup.

The capability to do business is a particular ability of a person that results from due mostly to the interaction of the individual sub-indicators set above. Having these characteristics signifies the eligibility of the person in the business startup. Generally, these individual factors have been considered as the most important properties that inspire a person to move forward. As a preconception, we assume that the interaction of these six sub-indicators within a person will be associated with a positive result in entrepreneurship. The objective of this chapter is to achieve a new behavior-based index which is able to measure the capability of entrepreneurial activities across GEM member countries.

4.1

Background

The status of entrepreneurial activities reflects the ability of a community to support the business-based ideas of individuals. In other words, since entrepreneurship is a complex phenomenon that highly depends on some innate capabilities (i.e., individual factors) and also environmental factors, therefore the creativity, risk-taking, innovation, and thereafter the planning power to achieve entrepreneurial goals cannot be denied (European Commission 2006). Additionally, as a large group of scholars has acknowledged, the capability to turn ideas into action, which is the base of the entrepreneurship, is necessary to promote the innovativeness, competitiveness, and also the economic growth in its wake (European Commission 2012). Individual factors, which are referred to as one of the two high influential factors, require sustainability and the strengthening of the entrepreneurial spirit that will lead to an increase in entrepreneurial thinking and capabilities in a society, which will increase business growth and economic durability. Therefore, the growth of entrepreneurial abilities requires, foremost, an increase in the entrepreneurial spirit. However, regardless of whether a person can succeed or not in the future, entrepreneurship skills and abilities will generally provide profits (Schoof 2006). Perhaps this profitability, although it may disappear too soon, is due to the entrepreneurial appropriate responsibility, innovation, creativity, capability, and adequate financial backing, but what seems to be more important than other factors is the spirit of entrepreneurship which is very important in the early period of a business so will finally lead to profitability (Cooney 2012; European Commission 2013). Reducing the entrepreneurial spirit, over the remaining period of an individual’s entrepreneurial activity, is one of the reasons that reduce the business/businesses profitability.

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Launching a new business is an intricate and idiosyncratic process that requires an in-depth scrutinizing about all circumstances, and it starts with aspiration by the entrepreneur and encompasses bringing together supplies that the entrepreneur does not necessarily control to follow up an opportunity (Venkataraman and Sarasvathy 2001; Stevenson and Jarillo 1990). Because of the instability of entrepreneurial spirit, to maintain the business, as well as in order to meet a rise in the rate of business’s profitability, there is a vital need for rising in the capability and skills of business management. To this end, entrepreneurs thoroughly need to truly support, skill and knowledge of own business, availability of relevant required resources and create enough obligation from organizational shareholders to turn the idea from vision to reality (Shane and Venkataraman 2000; Hannan and Freeman 1984; Hill and Levenhagen 1995). New firms (businesses) are often created during the entrepreneurial activities of one or more organizations in a short time or even after many years. These activities may take place in a predetermined plan in order to attract employees and/or attract investors to increase the durability/viability of the business/businesses and even operationalize ideas (Aldrich 2000; Shane and Venkataraman 2000; Gartner 1988). These intentionally systematic activities will cause the agglomeration of resources, the extending of organizational borders, and the initiation of deal with other business executors, and, no doubt, thereby these items will result in bringing forth the emerge of a new firm (Edelman et al. 2008). According to the literature on entrepreneurship, there are two contradictory theories, as follows: • First is about the discovery of entrepreneurship: It is more related to the business environment and designs its question in such a way that it only inquires about environmental factors, which is does the environment provide enough appropriate opportunities for people in a community to launching businesses? • Second is the creation perception which is in front of the “discovery” and is related to entrepreneurial perceptions. This theory emphasizes the creation of the ability to perceive the entrepreneurial opportunity and which finally will result in the initiation of startups. It can be said that the second theory is the complement of the first one so that it will occur if and only if the “discovery” takes place. Empirical studies have individually accentuated the essential use of either individual or environmental perceptions. Few efforts have also been made to increase insights and understanding of both theories, but the relationship between “discovery theory” and the “theory of creation” has not widely examined on the business environment and individual perceptions in terms of entrepreneurial opportunity, while the ability of entrepreneurs to launch a business (or businesses) has been investigated by many authors. The individual factors (e.g., perceived opportunities, perceived capabilities, risk acceptance, entrepreneurship intention) and environmental components (such as government programs, economic condition, entrepreneurship infrastructures,

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government support, etc.) are the most determinant components affecting the economic cycle and also will guarantee the improvement of life–health in countries.

4.2

Entrepreneurial Attitude

Entrepreneurship is not considered as a new topic in management and social sciences. The concept of entrepreneurship has been rapidly and carefully studied from various perspectives so that a wide range of perspectives, including the field of psychology, economics, mathematics, and so on, have scrutinized this phenomenon, complementarily. Classical and neoclassical theorists are trying to give a precise and comprehensive definition of entrepreneurship, but there is no single definition of entrepreneurship. Some researchers look at entrepreneurship from the viewpoint of economics, sociology, and psychology, and others see it from the viewpoint of management, while some others look at it from a social perspective. In an overall view, what seems to be somewhat commonly perceived among scholars is that entrepreneurship is a multidimensional concept that can be considered as an individual-environmental complex phenomenon (Bula 2012). After accepting the existence of all differences among scientists in the field of entrepreneurship, the main common question that arises here is why some people venture into business, whereas others do not have any focus on doing entrepreneurial activities even after several decades? A great deal of research has investigated the reasons for the creation of new enterprises and the entrepreneurial characteristics of those individuals who are liable for the emergence of new firms. In a different circumstance, another important question is why some individuals decide to follow their entrepreneurial activities while others do not? Audretsch and Keilbach (2004) and Hofstede et al. (2004) acknowledged that there exist documents showing that possible causes behind this behavior arise from the attitude of the individuals as well as the environmental conditions, economic, and other factors. More precisely, in the field of entrepreneurial endeavors, the separation of the determinant components which take effect on the economic cycle has been accomplished by an inordinate number of investigators. On one hand, the individual attitude of entrepreneurial activities and, accordingly, the environmental factors will produce together a novel intricate process which if policymakers and business actors of society pay no heed on it, this negligence will have grave consequences on the life–health of people in future. According to Faghih et al. (2019), the Entrepreneurial Attitude Index varies from society to society. They statistically proved that the amount of this index in factordriven economies is higher than innovation-driven economies. According to their study, it was concluded that some sub-indicators making the Entrepreneurial Attitude Index like risk acceptance was the main reason for this difference, whereas

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some knowledge-based indicators like perceiving entrepreneurship skills or entrepreneurial motivation may inverse the result. To this end, after a careful investigation into the GEM’s reports, we noticed the perceptions play a critical role in the fulfillment of entrepreneurship. If a person has a positive perception/attitude about entrepreneurship, it is likely that the person will venture into a business soon. An individual’s perception of entrepreneurship will be shaped by factors endogenous and exogenous. Endogenous factors are those that come from within one’s and are duly within individual control and relate to issues such as character. In contrast, the exogenous factors are beyond a person’s control and relate to environmental issues such as government programs and economic inflation. All of these factors can fully affect everything such as entrepreneurship status. For example, a thorough understanding of entrepreneurship will make the limitations of business startups disappear (Moy et al. 2003). Likewise, an individual’s attitude of self-capabilities and environmental sub-dimensions appoints the objects the individual specifies for himself. Kabui and Maalu (2012) conveyed that the perception of opportunity, alongside propensity, motivation, and access to means to trace the opportunity, is seen as a prerequisite essential to any entrepreneurial behavior. Meanwhile, the perception of entrepreneurs has been identified as an important component of the fulfillment of a successful business. Those who take up entrepreneurship perception will face beneficial opportunities, where others do not distinguish them. Following the endeavors of Palich and Bagby (1995), it can be said that entrepreneurs are also seen to perceive less risk in positions, rather than focus on the disadvantages and threats; they look for the advantages and opportunities, that we name (arbitrarily) the set of the capability of individuals to perceiving opportunity, the entrepreneurial capability (EC).

4.2.1

Theory of Planned Behavior

As the first definition of entrepreneurial capability, it is important to note that environmental factors are usually beyond the control of individuals, but individual factors are within the individual’s control. Hence, the capability of launching business/businesses, which focuses more on personality aspects and perceived capabilities of individuals, will be carefully discussed in this chapter. If we take entrepreneurship as a planned behavior (Drucker 2014), then we need to check three aspects as follows: 1. Attitude 2. Perceived behavioral control 3. Subjective norms These factors predict the tendency to do an individual’s optimal behavior (Ajzen 1991).

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Wedayanti and Giantari (2016) stated that subjective norms are perspectives that go out of the minds of individuals and usually only serve to advise others to do or not do something. In this regard, the consideration of the motive for doing or not doing business is also usually considered by the individual. Based on the research of Utami (2017), subjective or social norms refer to the individual’s perspective of people about how people are motivated to achieve their goals. Additionally, subjective norms are personal actions that appear more than the conscious and within the individual (Sumaryono and Sukanti 2016). The subjective norm relates to the understanding of individuals about the social environment affecting behavior. Moreover, the magnitude of subjective norms will get remarkable until when individuals notice that there are more resources surrounding them (Ajzen 1985; Hartwick and Barki 1994; Lee and Kozar 2005). Consequently, in Utami’s research in 2017, it was mentioned that attitude and subjective norms are known as the most important factors for the fulfillment of a behavior. Attitude is a specifying factor that demonstrates how much is the willingness of a person to act the idea (Ajzen 1991). Subjective norms can be recognized as the best predictor of behavior that social pressures significantly affect them. In general, the subjective norms finally specify whether a person does or abandon a specified. As Ajzen (1991) has mentioned in the theory of planned behavior, a person’s attitude to the habits or performance of others can be considered a predictor of behavior. Additionally, attitudes and beliefs are in a strong relationship with perceived behaviors and contribute to behavior or deterrence (Cruz et al. 2015). Also, the behavior is not only a function of individuals’ experiences, values, perceptions, and abilities but also reflects mental and behavioral situations. Everyone, first, begins to understand his surroundings and then he/she makes an effective decision to answer the questions created and ultimately acts. Perceptions can be defined as a process by which individuals will be able to identify environmental stimuli (such as motivating or even deterrent factors). What makes human understanding and perception more interesting is its inability to respond to all the stimuli that span its environment. Therefore, one’s perception of the environment may not be fully right, and everything depends on his abilities to perceive the environment. Hence, in the business sector, the business environment may not be well understood, and all the knowledge and perceptions of individuals may not be entirely correct. Therefore, a person’s view may differ from the entrepreneur’s perception of the business environment, although the existence of common perceptions is undeniable. In the concepts of entrepreneurship, according to the GEM research, the rate of perceived capability and opportunity to startup a new business, as well as the entrepreneurial intention, role model, and fear of failure in a population, is considered as the substructures of the entrepreneurial perceptions and attitudes. These sub-indicators mainly result from subjective norms and individual attitudes of entrepreneurship so viewed as behavior. The improvement and enhancement of the state of entrepreneurial perceptions in a community are feasible through making

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culture and training of entrepreneurial principles that require careful study and the regular implementation of correct programs. Krueger (1993) and Kolvereid and Isaksen (2006) claim that intent can determine the intensity of perceptions. The more you intend to do a job, the higher the level of understanding to do it. For example, in the field of entrepreneurship, if a person’s intention to set up a business is weak, it indicates that he does not have full awareness of the business, regardless of whether this information can be true or false. What seems to be correct is that the intention may include the propensity and motivation of individuals, too. Meanwhile, the intention is a behavior that has not been implemented yet (Tshikovhi and Shambare 2015). Nevertheless, the intention that is caused by attitude and perception can be considered as a behavior that promotes an individual up to the threshold of the act (the step before businesses’ birth).

4.2.2

Entrepreneurial Motivation

Although the effectivity of motivation has been discussed in Chap. 2 comprehensively, in this subsection we briefly clarify the relationship between entrepreneurship and motivation issue. The entrepreneurial motivation is the process of transforming a normal person into a powerful entrepreneur who can create opportunities and maximize wealth and contribute to economic development, at least in his country. To meet the needs, even at the highest possible level, motivation will be only stimuli. That is, motivation is a driving force that can help a person to achieve the most inaccessible needs. As mentioned in Chap. 2, Maslow’s need hierarchy theory, Hertzberg’s two-factor theory, and David Mc Clelland’s acquired needs theory presented that motivation can bring energy, enthusiasm, creativity, and efficiencies in fulfillment of the desired objectives. There are different reasons to activate the entrepreneurial motivation for launching a business. Indeed, the lack of existence of research identifying the various motivations for launching a business is tangible. According to Fairlie (2017), we found that that the basic distinction between entrepreneurs is that some entrepreneurs create a business when they see opportunities, while other entrepreneurs are forced to start a business because of the lack of other options in the labor market. Individuals decide to undertake entrepreneurial activities with different motives. Generally, Shapero and Sokol (1982) and Gilad and Levine (1986) mentioned that there is a conspicuous distinction between positive factors, that pull people into the entrepreneurship, and negative factors, that push people out of entrepreneurship. When entrepreneurial motivation is discussed, there is often a dichotomy between policymakers and researchers in topics of understanding the opportunities and the need for businesses. From the perspective of the researchers, a person will launch a

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business if he/she sees a good opportunity. While from policymakers’ point of view, a person starts a business whenever he/she has to (Stephan et al. 2015). Entrepreneurial motivations can be classified as follows: • • • • • •

Financial independence Better business Maintenance of income and financial capital Lack of other option Family job Interest and challenge and so on

Businesses can continue well regardless of their initiation procedure. Based on reports released by the GEM, since a business initiation is subject to opportunistic or necessity viewpoint, the subject of job creation is the least advantage arising from such viewpoint, and probably the motivation-based businesses lead to innovation and even exports, which are of characteristics of best-run businesses. Most of the questions about entrepreneurship phenomenon, which are also discussed by the Global Entrepreneurship Monitor, are related to the rate of entrepreneurial activities, innovations, exports, business creation, business discontinuation, environmental factors, etc. The Global Entrepreneurship Monitor has paid a lot of attention to the type of motivation behind business startups from 2001 onwards. Hence, according to research conducted at the GEM, motivation is divided into two sections: first, opportunity-driven entrepreneurship, and second, necessitydriven1 entrepreneurship. Since 2005 extensive and advanced research on opportunistic entrepreneurship has been conducted at the Global Entrepreneurship Monitor. According to the released researches, an opportunity-driven entrepreneur refers to an individual who is pulled into entrepreneurship by opportunity because he/she desires more independence or wants to increase the current income, whereas the latter group includes those who have launched businesses to maintain their previous income or who are venturing into a business, out of necessity, because of lack of other options. The questions of GEM’s questionnaire whereby the data of the Entrepreneurial Motivation Index is gathered are as follows: • Are you involved in this startup to take advantage of a business opportunity or because you have no better choices for work? • Which one of the following (itemized in the questionnaire), do you feel, is the most important motive for pursuing this opportunity? In order to answer questions like “do the different types of motivation have a different effect on economic development?” economists have focused on the impact of entrepreneurship on economic output such as the GDP, productivity, and employment (Naudé 2011).

1

Note that both necessity and mandatory terms may be used equivalently henceforth in this book.

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The literature review on entrepreneurial motivation has revealed that motivation matters to the performance of firms as well as for strategic decisions that entrepreneurs make them. There is evidence that shows entrepreneurial motivation leads to the satisfaction of entrepreneurs about their business situation. Likewise, investors pay attraction to the production of quality products and innovation in service delivery, which will fulfill mostly due to differences in entrepreneurial motivation. The current research presents evidence in which the findings show that entrepreneurial motivation is important from the point of view of both policymakers and researchers.

4.3

Entrepreneurial Motivation and Economic Growth

The indirect connection between entrepreneurial motivation and economic development is what will be addressed in this subsection. In general, the classification of individuals’ motivation on the basis of the goals intended by entrepreneurs may be a multilateral process, but what is being discussed in this book is the motive that drives a person toward becoming an entrepreneur. Since entrepreneurial motivation has a significant impact on business success, so considering this factor as the most influential factor in economic development will not be far from logic. On the other hand, the fact that entrepreneurial activities are a driving force for economic development is a well-accepted issue by researchers and policymakers. Therefore, it is logical to claim that entrepreneurial motivation will have a very effective impact on economic growth. Entrepreneurial activities will become effective if, and only if, the economydependent objectives including job creation, industrial development, and countries’ economic growth activate. Additionally, based on Ahmad (2010), the road map of markets and economies has been changed by emerging entrepreneurship and entrepreneurs. Hisrich et al. (2009) have argued that entrepreneurial activities have a very determinant role in business growth, and employment generation, as well as entrepreneurial activities of a country, will be associated with a positive result in economic development. On the other hand, entrepreneurship has also been viewed as a revolution in the twentieth century, which many scholars such as Kuratko and Welsch (2003) consider the entrepreneurial revolution as the mainstay of the Industrial Revolution in this period. Hence, economic development and development in international commerce are also due to the growth of entrepreneurial activities. With the establishment of business entities, entrepreneurs invest their resources and investors commence to support their plans, and people are allowed to work for acquiring revenues from the newly flourished businesses, and that doing so finally will lead to the creation and sharing of wealth. Because of the importance of entrepreneurship in employment generation and growth of the GDP, academic activities present evidence to justify the governmental

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entities to encourage people to make money with launching businesses (Ahmad 2010). Depending on the motivation of individuals to startup businesses, these activities drive innovation as well as turn new knowledge into products and services, by which economic development will happen. By considering all arguments stated here, entrepreneurship leads to innovations that bring new technology into society, and this technology leads to job creation and, ultimately, will be along with the growth of social wealth. What seems to be interesting in the business market is that entrepreneurs often do not try to solve social problems, but in the direction of their profits, they are always looking for creative and innovative ways to answer needs, whereby the ambitious willingness of entrepreneurs will contribute to communities to get welfare. This is reasonable to claim that without sustainable businesses, the economies which depended on weak businesses will meet a downturn soon (Zimmerer and Scarborough 1996). Global Entrepreneurship Monitor (GEM) dataset comprising any types of entrepreneurial activities such as the rate of businesses started with high motivation or mandatory approaches. According to the GEM dataset, only the businesses which have started with high motivation (with greater independence or more income) lead to economic development in a society. Based on the GEM dataset, because the rate of entrepreneurship including any types of entrepreneurial activities [either nascent (SU), baby (BB), or established (EB)], so, clearly, it is reasonable that the rate of entrepreneurship has a negative effect on economy indicators (like GDP). Figure 4.1 demonstrates that the rate of entrepreneurship not only has no positive effect on GDP growth but also decreases the GDP rate. This figure shows a negative relationship between entrepreneurial activities and the GDP per capita that seems to be a contradiction. In the next sections, this fact that low motivation of entrepreneurs in launching businesses has made such strange results will be illustrated methodically. Because of the high motivations behind the businesses started in developed countries, many studies have shown that entrepreneurial activities in developed societies have always a positive and strong impact on economic development (Tang and Koveos 2004; Stel et al. 2005; Thurik et al. 2008; Wennekers et al. 2005; Acs and Amorós 2008). Global Entrepreneurship Monitor (GEM) survey has shown that the rate of high opportunity-driven entrepreneurial activities in developed (or high-income) countries is more than in other countries. This means that entrepreneurship in developed countries is often based on high-opportunity and motive-based rather than necessity. As a result, what affects a society’s economy is entrepreneurship launched on the basis of high opportunity. Therefore, one of the reasons for the development of highincome countries is that the startup of businesses in those societies is mainly based on opportunity, rather than the necessity.

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Fig. 4.1 The rate of entrepreneurial activities [either nascent (SU), baby (BB), or established (EB)] vs. GDP per capita. Source: Authors’ own figure

4.4

Assessment of Entrepreneurial Capability

As pointed out in the literature section of this study, entrepreneurship is a stimuli force that accelerates the flourishing process of the economic cycle across societies. With the growth of quality entrepreneurship, the economic cycle of societies works well. In fact, the fulfillment of this matter will lead to an increase in income, life expectancy, health, social welfare, education, etc. If we consider the Human Development Index (HDI) as an indicator of society’s health, according to the HDI equation, it can be said that the health index will increase when all of its three sub-dimensions (income index, education index, and life expectancy) enhance concurrently. On the other hand, since entrepreneurship is an effective factor in the economy of a country, with the increase in the quality of entrepreneurship (namely, entrepreneurship affecting the economy), the rate of Human Development Index (as a value for the society health) will also increase. Meanwhile, only some entrepreneurial activities can bring the health of communities which have been started with the intention of increasing the quality of productions, innovations, exports, employment generation, etc.

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Fig. 4.2 Scatter plot of the rate of entrepreneurial activities vs. the Human Development Index (HDI). Source: Authors’ own figure

It is important to note that any type of entrepreneurship is not able to increase the health of communities. For example, the following dispersion chart (Fig. 4.2) shows the relationship between the Human Development Index and the rate of entrepreneurial activity based on the dataset of the Global Entrepreneurship Monitor (GEM) in the year 2015. Among the 11 linear and nonlinear models, the Quadratic regression model was selected to clarify the relationship between the Human Development Index (as the dependent variable) and the Entrepreneurial Activity Index (as the independent). The determination coefficient in this estimated regression model is 38.7%. This indicates that the Entrepreneurial Activity Index can predict about 38.7% of the variations and fluctuations of the Human Development Index. The equation of the fitted model (Quadratic equation) is as follows. HDI ¼ 0:804 þ 4:47E  3  x  1:8E  4x2  error

ð4:1Þ

in which E  a denotes 10a, and x represents the rate of entrepreneurial activities.

4.4 Assessment of Entrepreneurial Capability

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Generally, based on Fig. 4.2, notwithstanding the increase of entrepreneurial activity rate among the 60 countries which had participated in the Global Entrepreneurship Monitor survey in 2015, the Human Development Index is decreasing. At the first look, this is not a logical result. Because entrepreneurial activities contribute to the economic development of societies and, additionally, because the economic index is one of the sub-dimensions of the Human Development Index, thereby, it is expected that a rise in entrepreneurial activities will have a positive result in the growth of the Human Development Index. More precisely, in light of this outcome, evidently, the amount of the Human Development Index in the innovation-driven economies (high-income) is the greatest whereas in the factor-driven economies (low-income countries) the Human Development Index is the lowest. In contrast, the value of the Entrepreneurial Activities Index for the factor-driven countries is much higher than of the innovation-driven ones. It seems the main reason causing this contradiction is the lack of consideration of the quality level of the entrepreneurial activities that can benefit humans and also may hasten economic development. Although the amount of entrepreneurial activities in the innovation-driven countries is much lower than the factor-driven countries, the health of life (like income, life expectancy, education, and so on) in these societies is much better than of low-income countries. In order to get more details about such contradictions, see Fig. 4.3 which demonstrates the nonlinear relationship between HDI (as the dependent variable) and the rate of exit from business (as the independent variable). This diagram displays the correlation between the Human Development Index and the rate of exit from business. According to the overall linear and nonlinear regression methods, the logarithmic regression model is selected as the best-fitted model for estimating the relationship between these two variables. The coefficient of detection of this model is more than 33% which means the rate of exit from business may predict more than 33% of the variations of the HDI. And, with a view to Fig. 4.3, the rate of exit from business has a negative relation with the Human Development Index. Clearly, the HDI decreases when the rate of exit from business starts to increase. Therefore, the constant reduction of the HDI is associated with a fact that entrepreneurial activities, no doubt, have a positive effect on the HDI. This dispersion chart (Fig. 4.3) has also separately drawn for different economic groups. According to this chart, the rate of exit from business for factor-driven countries is much higher than the innovation-driven economies. This illustrates that most of the entrepreneurial activities, that have launched in the factor-driven economies, does not have even the minimum quality for influencing the economic cycle of the society under study. Moreover, due to the nonexistence of efficient businesses (which they might be launched on a mandatory basis), the owners of these businesses have no interest to maintain their businesses. All in all, creating a business in the factor-driven (low-income) countries is much easier than innovation-driven ones and, alternatively, in probably the fail of businesses in the factor-driven economies is more likely than innovation-driven countries. The equation of the fitted model (the Logarithmic model) is as follow:

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4 Entrepreneurial Capability Index

Fig. 4.3 Scatter plot of the rate of exit from business vs. the Human Development Index (HDI). Source: Authors’ own figure

HDI ¼ 0:894  0:105  log ðxÞ  error,

0 < x < 100

ð4:2Þ

where x is the rate of exit from business. In the innovation-driven countries, the rate of entrepreneurial activities is low and the amount of exit from business in these countries is much lower than the factordriven ones. This suggests that entrepreneurs in innovation-driven countries can easily continue their own businesses without fear of endogenous and exogenous threats that reduce their business capabilities, whereas this fact is inverse in the factor-driven economies. In the innovation-driven countries, entrepreneurs seem to have launched their own businesses on a voluntary basis whereas in factor-driven and low-income countries most businesses have been started on a mandatory basis. As well as, in low-income economies, because of the inability of individuals or the lack of profitability of businesses, these entrepreneurial activities will be abandoned after a short period of time. See Fig. 4.4. This figure shows a nonlinear relationship between the rate of Total early-stage Entrepreneurial Activities (TEA), which are started with the necessity/mandatory motive, and the rate of total entrepreneurial activities. The quadratic model for these

4.4 Assessment of Entrepreneurial Capability

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Fig. 4.4 Scatter plot of the rate of TEA based on necessity motivation versus the rate of total Entrepreneurial Activities. Source: Authors’ own figure

two variables is considered as the best-fitted model. On the other hand, the chart shows that in most factor-driven countries, a great number of businesses are started mandatory and maybe this is why the exit from the business in such countries is much larger than in developed countries, while the maximum amount of necessity entrepreneurial activities in innovation-driven economies is just 3.2%. Another reason that may induce entrepreneurs in low-income countries to discontinue their own business is the nonfulfillment of their needs throughout their business. When people venture into a business out of necessity and a lot of needs, clearly, they expect to get the right results very soon to meet their needs. But since entrepreneurship profits somewhat entail a long time, then those who startup their business/businesses would abandon it/them very soon. This not only does not lead people to their needs but also causes them to lose some of the assets and capabilities that they already had. Additionally, there is a fact that some people, especially in the factor-driven countries, hope to earn their financial-based objectives without having any suitable career experiences or adequate skills and capabilities of business startups.

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4 Entrepreneurial Capability Index

The determination coefficient of this quadratic model is more than 66%. This means the rate of necessity motive TEA is able to predict the variations of the rate of any entrepreneurial activities more than 66%. Its estimated reference line (the quadratic model) is: Rate of any Entrepreneurial Activities ¼ 9:23 þ 3:53x  0:01x2  error

ð4:3Þ

where x denotes the rate of TEA based on necessity motive. In summary, the linear and nonlinear regression models that have been fitted for this relationship show the rate of total entrepreneurial activities in the factor-driven economies is more than the innovation-driven (developed) countries, and, furthermore, the rate of exit from the business in the low-income countries is much more than the high-income communities. The notable issue, in this case, is about the amount of the Human Development Index (HDI) in different economies. As a result, it is evident that the HDI in the factor-driven economies is lower than the innovation-driven economies, whereas the rate of entrepreneurial activities in factor-driven countries is significantly greater than in the innovation-driven economies. With regard to the low percentage of entrepreneurial activities in innovationdriven countries than the factor-driven ones, the question that arises here is, • Which kind of entrepreneurial activities positively influence economic status? In response to the points raised in this study, and for clarifying the contradictions raised here, we provided the following sections. Because of the lack of supports supplied by investors and policymakers, people of low-income countries do not seem to appreciate the magnitude of the effectiveness of entrepreneurship on the economy. Another reason that may cause this adverse outcome could be because of the difficulty of businesses startup in these countries. On the other hand, it sounds in the factor-driven economies, people are forced to start a business because of the lack of other option in the labor market. Further questions that arise here are as follows: • How this low TEA helps innovation-driven economies to reach such great welfare? • Why factor-driven countries, in spite of having a high rate of entrepreneurial activities, are less developed? To respond to the questions raised here, there is a need of following three stages: 1. Individual factors 2. Environmental factors 3. Entrepreneurship driving force As Fig. 4.4 demonstrates, the approach of people in the innovation-driven (highincome) economies for starting a business is chiefly based on a powerful motivation.

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Fig. 4.5 Scatter plot of the rate of TEA based on opportunity approach vs. the rate of Total earlystage Entrepreneurial Activities. Source: Authors’ own figure

For careful scrutiny, we attempted to have a succinct discussion on the motivation-based entrepreneurial efforts. To this end, the regression relationship between the rate of Total early-stage Entrepreneurial Activities (TEA) based on opportunity motivation and the rate of total entrepreneurial activity has been accomplished. See the results in the following figure (Fig. 4.5). As a result, among 11 linear and nonlinear models, the cubic model has been appointed as the best-fitted model for these variables. The detection coefficient of this model is more than 84%. The meaning of this value refers to the reliability of the independent variable (here in the rate of TEA based on the opportunity motivation) which is able to predict the variation of the dependent variable (the rate of any entrepreneurial activity). Moreover, this curve shows that the opportunity-driven activities in innovation-driven economies are less than of the factor-driven countries. The estimated function of the reference line (the cubic model) is as follow:

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4 Entrepreneurial Capability Index

TEA ¼ 5:54 þ 1:27  x þ 0:04  x2  8:22E  4  x3  error, 0 < x < 100

ð4:4Þ

in which x represents the rate of TEA based on opportunity motivation. In summary, the components which have been studied until here are as follows: • • • •

The rate of entrepreneurial activity The rate of exit from business The rate of TEA based on necessity motivation The rate of TEA based on opportunity motivation

The rate of entrepreneurial activities and the rate of TEA based on opportunitydriven in the innovation-driven economies are low compared to factor-driven ones, and what seems to be interesting is that these little amounts of entrepreneurial activities in the innovation-driven economies lead them toward a high HDI. Meanwhile, although the innovation-driven economies have the least amount of entrepreneurial activities, so What leads to high-quality businesses in the innovation-driven economies? As stated, for the appraisal of a system (herein entrepreneurship), it is necessary for all exogenous (environmental) and endogenous (individual) factors to be assessed. For this purpose, we focus on the implementation of the three steps outlined above.

4.5

Entrepreneurial Capability Indexing Procedure

Indexing the entrepreneurial capability requires the fulfillment of three steps below.

4.5.1

Stage One: Individual Factors Dimension

This subsection has been provided in order to achieve an appropriate index for evaluating the individual factors for categorizing the Global Entrepreneurship Monitor (GEM) member countries based on the dataset in 2015. The questionnaire of surveys in the Global Entrepreneurship Monitor presents five variables to measure the value of the individual factors. The related questions are listed as follow: • The Role Model Index: Do you know someone personally, who started a business in the past 2 years? Description Anyone the respondent knows personally by name would qualify— relative, family member, neighbor, work colleague, schoolmate, and the like. Any

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99

kind of economic activity, even part-time self-employment, will qualify. The business need not currently be in operation, but the startup effort should have occurred in the past 2 years. The effort should have involved more than just talk; some time or resources should have been deployed to get the business going. • The Perceived Opportunity Index: In the next 6 months, will there be good opportunities for starting a business in the area where you live? Description The item focuses on the respondent’s personal evaluation of business opportunities; the geographic region may be vague, but community, neighborhood, commune, or towns are all acceptable concepts. The focus is on the location of their immediate experience, not major regional or national economic conditions • The Perceived Capability Index: Do you have the knowledge, skill, and experience required to start a new business? Description The focus is on their capacity to start a business, NOT their motivation or interest. They may have the skill and capacity and (1) not be interested or (2) may not consider those suitable opportunities exist. • Rate of Fear of Failure: Would the fear of failure prevent you from starting a business? Description The ENTIRE emphasis is on the presence of a “fear of business failure.” Reasons for the fear are not the focus, even if such reasons are given, such as high economic risk, strong cultural or social sanctions for failure, and lack of personal capacity to start a business. Note that, in order to calculate the rate of risk-taking, we reversed the answers of respondents in the question related to the rate of fear of failure. • The Intention Index: Are you, alone or with others, expecting to start a new business, including any type of self-employment, within the next 3 years? Description The question is a focus on the expectation that a new business entity will be formed. Any type of business entity would quality, even part-time selfemployment. Any reasonable expectation would qualify, but preferably it would be more than a 50% chance, and some effort would be made to start the new business. Using the mixture of Role Model Index and the Risk-Taking Index, a new index that is able to measure the personality properties can be created. In addition, the mixture of both perceived opportunity and perceived capability will give a new index known as entrepreneurship skills. Finally, by using the Personality Properties Index and the entrepreneurship skills, a new index can be provided that we name it entrepreneurial competency. Eventually, by adding the Entrepreneurial Intention Index to the four existing indicators, an individual factor that clarifies the attitudes of peoples toward the entrepreneurship phenomenon can be estimated. The path diagram for calculating

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4 Entrepreneurial Capability Index

Fig. 4.6 The path diagram for calculating the individual factor. Source: Authors’ own figure

the individual factor, as one of the sub-dimensions essential for the calculation of the Entrepreneurial Capability Index, that we have used in this study is displayed in Fig. 4.6. The formula that we applied for the calculation of the indexes in this chapter is based on the interaction effect. For understanding the function of this estimator, study the following sections.

4.5.1.1

Arithmetic Mean

The highest frequency type of average which is well-liked is known as the arithmetic mean (AM). If n numbers are given and each number denotes by xi (herein we use the notation Indi), where i ¼ 1, 2, . . ., n, then the arithmetic mean is a fraction of the sum of total units divided by the number of total units (n) or: n P

Individual Factor of j country ¼ th

Indij

i¼1

n

ð4:5Þ

where n is the number of sample units in the jth country. The use of this estimator function with the same weights (1/n) seems to be unreasonable. Therefore, the use of a function with different weights is more justifiable, and estimating the unknown weights of this function also requires a nonparametric methodology. The weighted average function, with equal weights, in the second stage, is recommended instead of the arithmetic mean function (see the function below): Individual Factor of the jth country ¼

n X

θij Indij

i¼1

where θi denotes the unknown weights that need to be estimated. Note that:

ð4:6Þ

4.5 Entrepreneurial Capability Indexing Procedure

101

0  θij  1, n X

θij ¼ 1

i¼1

Although the use of a weighted average is more justifiable than the arithmetic means and the use of this function will lead to lower error, in general, but the estimated value of the individual factor will be higher than the real value. Meanwhile, the usage of these kinds of estimators might be easy, but due to their bias behavior, they are likely to cause a large error in the estimation of values. A more precise method that includes the interaction effects of other indicators and also is commonly used for calculation of subsequent indices is geometric mean (GM). For the purpose of scrutinizing this estimator (geometric mean), the following subsection has been presented.

4.5.1.2

Geometric Mean

The arithmetic mean is only a simple linear average of all the indices, while sometimes we encounter a phenomenon that does not result from the linear effect of other factors. For example, entrepreneurship does not come about by a simple combination of environmental and individual factors, but it happens because of the effect that environmental factors put on the capabilities and perceptions of individuals. Therefore, the use of geometric meanings is suggested in calculating entrepreneurial indices. The equation of this mean value is as follows: " Individual Factor of the j country ¼ th

n Y

#1n Indij

ð4:7Þ

i¼1

4.5.1.3

Harmonic Mean

The third type of average function is the harmonic mean (HM) whose equation is as follows. This method is used when the researcher intends to achieve the average amount of differences between different rates: Individual Factor of the jth country ¼

n P

n

i¼1

1 Indij

ð4:8Þ

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Furthermore, among the arithmetic, geometric, and harmonic mean functions, there is a proven inequality relationship that shows the advantages and disadvantages of each of these equations, regardless of the application of each of them: " n P i¼1

n 1 GEIi



n Y i¼1

n P

#1n GEIi



GEIi

i¼1

n

ð4:9Þ

or, HM  GM  AM This inequality specifies that the average of the arithmetic is constantly greater than the geometric mean, and the geometric mean steadily is more than the harmonic mean. In accordance with this inequality, it is clear that the harmonic average is always lower than the real value (in fact, it has a tendency of underestimation) and the average of the arithmetic is always higher than the real value (overestimated). While the geometric mean is an average value close to the actual value of a parameter. On the other hand, the geometric mean can calculate the average of the interactions of indices rather than using linear relationships. In general, to calculate the individual factor of any 60 countries that have taken part in the GEM’s survey, we will use the geometric mean function. According to the characteristics of the geometric mean function, the individual factor in a country is high if and only if all five sub-dimensions are enough large. Therefore, for increasing the individual factors of a community, the simultaneous growth of any five sub-dimension is required. Given the GEM data in 2015, we calculated the Individual Factors Index across 60 countries. Table 4.1 has ranked GEM member countries on the basis of this index. According to the outcomes of this table, Senegal, Burkina Faso, Botswana, and Cameroon have the largest amounts in terms of entrepreneurial attitudes (individual factors) among 60 countries, whereas Greece, South Korea, Bulgaria, and Italy have obtained the least amount of individual entrepreneurial attitudes, respectively. Now, the question that comes up here is do entrepreneurial attitudes (individual factor) alone develop the economic situation? To have an in-depth probe into this index, it is better to know that only the entrepreneurial attitudes which have a positive impact on the economy can influence the value of the Human Development Index, and also only such entrepreneurial efforts are able to enhance the life quality. Figure 4.7 has represented a nonlinear model for the effect of individual entrepreneurial attitudes (individual factors) on the Human Development Index. According to Table 4.1, underdeveloped countries often have the highest individual index, while developed countries typically account for a small percentage of this index. Since the startup of businesses in some underdeveloped countries is often

Burkina Faso Botswana Cameroon Chile Lebanon Ecuador Peru Philippines Colombia Indonesia Barbados Tunisia Iran Vietnam

Country Senegal

63.0408 58.2114 56.4038 55.7500 53.9364 53.6230 52.6527 49.6656 49.5038 47.9345 47.4304 47.3964 46.7453

64.4740

Individual factor 73.8859

Source: Authors’ own table

3 4 5 6 7 8 9 10 11 12 13 14 15

2

Rank 1

18 19 20 21 22 23 24 25 26 27 28 29 30

17

Rank 16

Brazil Panama Uruguay Kazakhstan Israel Macedonia Morocco Mexico Egypt Estonia Romania Latvia Poland

Argentina

Country Guatemala

43.6236 43.0184 42.5483 42.3044 41.8881 39.3710 39.1768 38.9681 38.6108 37.9370 37.4500 37.1830 36.7402

45.2989

Individual factor 46.3825

33 34 35 36 37 38 39 40 41 42 43 44 45

32

Rank 31

Table 4.1 The rate of individual factors affecting the entrepreneurial activities

Canada Luxembourg China Finland Ireland Thailand Sweden South Africa Slovakia Taiwan Netherlands Portugal India

Country United States Australia 35.5358 35.2622 35.1153 35.1070 34.7643 34.5552 34.5280 34.3272 34.1588 33.4449 32.6328 32.1062 32.0904

36.4404

Individual factor 36.5851

48 49 50 51 52 53 54 55 56 57 58 59 60

47

Rank 46

Croatia Switzerland Puerto Rico Norway Slovenia Spain Germany Belgium Malaysia Italy Bulgaria South Korea Greece

Country United Kingdom Hungary

30.1224 29.8332 29.8129 29.3446 28.9336 26.3012 26.2684 26.0227 25.6788 23.2906 23.2302 22.7341 22.2285

30.2417

Individual factor 30.7758

4.5 Entrepreneurial Capability Indexing Procedure 103

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4 Entrepreneurial Capability Index

Fig. 4.7 Scatter plot of the rate of individual factor vs. the Human Development Index (HDI). Source: Authors’ own figure

compulsory, some sub-indicators (such as risk acceptance) are very high in underdeveloped countries, and this has led that the individual index in these countries often become more than developed countries. According to Table 4.1, Fig. 4.7 can be reasonably justified. With an increase in the individual index, the number of non-developed countries will increase. Namely, countries with a low Human Development Index are high in individual index, so a decrease in the Human Development Index is a natural trend when the individual index is increasing. The equation fitted to this relationship is based on a quadratic (third-order) formula. The coefficient of detection of this nonlinear regression relation is more than 47%. This means the individual factor is capable to estimate the fluctuations/ variations of the Human Development Index by more than 47%. Apart from this baffling outcome, as Fig. 4.7 demonstrates, the individual index cannot alone estimate the group of entrepreneurial activities that have a positive impact on the economic development or health of a community in its wake. Since entrepreneurial attitudes (individual factor) in low-income countries (factor-driven) is higher than in countries with high incomes (innovation-driven), therefore it can be said that components such as risk-taking, entrepreneurial intention, role model, perceived capability, and perceived opportunity (because these are

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105

only a notion and attitude of entrepreneurship and also may be incorrect criteria) have a negative effect on the Human Development Index. As a result, the reason why the individual index is associated with a negative result in the life quality is that large percentage of the population who live in factordriven economies have to start any business because of the lack of other option in the labor market; and also they have no better choice unless take a risk for making money that, naturally, does not guarantee successful businesses. The estimated equation of this model (reference line) is as follows: HDI ¼ 0:86 þ 3:1E  3  x  1:14E  4  x2  error, < x < 100

0 ð4:10Þ

in which x shows the Individual Factors Index. More precisely, if we consider the individual factor as one of the three indicators of “entrepreneurial capability,” it can be said that this index alone cannot calculate the accurate amount of “entrepreneurial capability.” In order to calculate the Entrepreneurial Capability Index, it is necessary to calculate the environmental factors that affect the attitude of individuals in terms of entrepreneurship. Thereby, these outcomes assert that individual attitudes cannot alone undertake the appraisement of the entrepreneurial activities which affect the economic cycle (or communities’ life–health). In fact, there is a need to apply another entrepreneurial affecting component to introduce the Entrepreneurial Capability Index (ECI). In addition, because of the significant impact of the exogenous components on entrepreneurial activities, close attention to the environmental factors is vital for calculating indicators necessary for measuring the rates relating to entrepreneurship (e.g., Entrepreneurial Capability Index).

4.5.2

Stage Two: Environmental Factors Dimension

In the assessment of entrepreneurship (as an intricate phenomenon), in addition to individual factors, environmental factors play an essential role also that shall be heeded. Thereby, in this regard, we try to use at least one of the environmental factors (herein income index) to calculate the “Entrepreneurial Capability” Index. By considering the income index as an environmental factor affecting the launch of a business, that take a positive effect on the economic cycle in any society, the continuation of this chapter would be carried out as follows. The calculation of the Entrepreneurial Capability Index has been accomplished using the geometric mean of individual index and income index. It should be noted that the computation of the Entrepreneurial Capability Index at this stage is experimental and other studies are needed to complete the fulfillment of this new index. The final adjustments for creating the Entrepreneurial Capability Index have been followed out in stage three.

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As the second step to move forward, the table below (Table 4.2) represents the primary results (pre-index) of the interaction of individual attitude index and income index. As shown, this pre-index, which will be used to calculate the main index of “entrepreneurial capability,” has been ranked for the countries under study. Have note that the income index for Macedonia, Taiwan, and Puerto Rico has not been released in 2015. Therefore, we have not calculated this index for these three countries. With reference to Table 4.2, Bosnia, Chile, Lebanon, and Peru have the highest levels in this pre-index of entrepreneurial capability. It seems that we will not be able to get real results if we only use the individual factors and an environmental factor (like income index). According to this table, Bulgaria, Greece, and India have the least value of the pre-index. In order to hold a deep appraisal as regards this calculated pre-index, we intend to process a logical relationship between the Human Development Index and this pre-index. Accordingly, using a set of linear and nonlinear regression models, we will estimate a suitable model for this relationship. The following table (Table 4.3) summarizes the 11 renowned models which have estimated the relationship between these two variables. In these models, the primary index of entrepreneurial capability is considered as the independent variable (effective variable), and the Human Development Index is treated as the dependent (response) variable. As shown, none of the 11 proposed models is significant at the level of 95%, and also there is no meaningful relationship between the pre-index of entrepreneurial capability and the Human Development Index (HDI). The dispersion diagram below confirms the validity of this claim (see Fig. 4.8). As displayed in Fig. 4.8, there is no logical connection between this index (pre-index of entrepreneurial capability) and the Human Development Index. The regression-based modeling is carried out for three different economy groups (factordriven, efficiency-driven, and innovation economies), and the result of these regression estimates is presented in Appendix A. The results of these tests showed that no significant association was found in any of the economic groups. Consequently, creating an indicator that only derives from individual and environmental interactions cannot measure Entrepreneurial Capability Index. What seems to be omitted from the sub-indicator set is a sub-indicator that can give an impetus to individual attitudes and environmental factors, but many researchers have ignored it. More precisely, only consideration of the interaction between individual factors and environmental factors for providing a criterion for measuring an index that demonstrates the rate of reliable entrepreneurial activities, which influences the economy of society, will go wrong.

Colombia Argentina Philippines Kazakhstan United States Senegal Indonesia

9 10 11 12 13

5.8866 5.8698

6.0336 5.9746 5.9306 5.9006 5.8954

Pre-index of EC 6.9127 6.7759 6.4100 6.1963 6.1621 6.0490 6.0397 6.0368

Source: Authors’ own table Asterisks indicate unavailable data

14 15

Country Botswana Chile Lebanon Peru Ecuador Iran Barbados Israel

Rank 1 2 3 4 5 6 7 8

29 30

24 25 26 27 28

Rank 16 17 18 19 20 21 22 23

Poland Netherlands

Estonia Sweden Finland Cameroon Latvia

Country Luxembourg Uruguay Panama Australia Tunisia Brazil Canada Ireland

5.5222 5.5001

5.6652 5.6575 5.6273 5.5335 5.5251

Pre-index of EC 5.8575 5.8160 5.8148 5.7775 5.7579 5.7199 5.6991 5.6982

Table 4.2 The primary index (pre-index) of entrepreneurial capability

44 45

39 40 41 42 43

Rank 31 32 33 34 35 36 37 38

Morocco Hungary

Country Mexico Romania Guatemala Slovakia Norway Switzerland Vietnam United Kingdom Portugal Egypt Burkina Faso China Thailand 5.0268 4.9980

5.1901 5.1765 5.1540 5.1010 5.0942

Pre-index of EC 5.4848 5.4770 5.4654 5.3884 5.3708 5.3572 5.2915 5.2571

59 60

54 55 56 57 58

Rank 46 47 48 49 50 51 52 53

Taiwan Puerto Rico

South Korea India Greece Bulgaria Macedonia

Country South Africa Slovenia Croatia Germany Belgium Spain Malaysia Italy

** **

4.4779 4.4244 4.2927 4.2376 **

Pre-index of EC 4.9853 4.9679 4.9304 4.9213 4.8690 4.7890 4.6166 4.5298

4.5 Entrepreneurial Capability Indexing Procedure 107

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4 Entrepreneurial Capability Index

Table 4.3 Regression-based models for the primary index (pre-index) of entrepreneurial capability and the Human Development Index (HDI) Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .006 .320 .005 .298 .005 .277 .007 .196 .008 .203 .003 .178 .003 .174 .003 .170 .003 .178 .003 .178 .003 .178

df1 1 1 1 2 2 1 1 1 1 1 1

df2 54 54 54 53 53 54 54 54 54 54 54

Sig. .574 .587 .601 .822 .817 .675 .678 .681 .675 .675 .675

Parameter estimates Constant b1 b2 .882 .016 .934 .081 .720 .414 .593 .092 .010 .751 .000 .008 .862 .984 .914 .089 .326 .465 .149 .017 .862 .017 1.160 1.017

b3

.001

Source: Authors’ own table

Fig. 4.8 Scatter plot of the pre-index of Entrepreneurial Capability vs. the Human Development Index (HDI). Source: Authors’ own figure

Given the definition of the Global Entrepreneurship Monitor from entrepreneurship,2 this newly created index (pre-index of entrepreneurial capability) is expected 2 Based on the definition of entrepreneurship by the Global Entrepreneurship Monitor (GEM), any entrepreneurial activity, regardless of its impact on the economy, is called entrepreneurship.

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109

Table 4.4 Regression-based models for the pre-index of entrepreneurial capability and the rate of entrepreneurial activities Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .261 19.456 .252 18.570 .241 17.482 .271 10.053 .271 10.026 .343 28.739 .336 27.802 .325 26.436 .343 28.739 .343 28.739 .343 28.739

df1 1 1 1 2 2 1 1 1 1 1 1

df2 55 55 55 54 54 55 55 55 55 55 55

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 35.553 10.416 71.995 55.156 74.303 286.003 40.032 17.579 13.094 3.097 1.137 1.670 .183 2.734 5.571 14.260 .128 .513 1.137 .513 .879 .599

b2

b3

2.564 .000

.150

Source: Authors’ own table

to estimate only the changes in the entrepreneurial activity index. Since the rate of entrepreneurial activities in societies may include entrepreneurial activities without any positive impact on the economy (especially in factor-driven or low-income countries), a relationship between this pre-index and the rate of entrepreneurial activities in societies can be expected to exist. To have an in-depth investigation, the regression-based modeling is applied again. Table 4.4 offers the fitting of linear and nonlinear regression models to the calculated pre-index (as an independent variable) and the rate of entrepreneurial activity (as a dependent variable). Among the 11 estimated models, the compound, growth, exponential, and logistic models have the highest detection coefficient. Given the lowest error value in estimating these models, we choose the exponential relationship. This relationship can accurately predict about 35% of the variations in the relationship between the calculated pre-index (using individual and income index) and the rate of entrepreneurial activity. Additionally, the dispersion diagram of these two indices along with the said exponential model is as follows: The formula of this estimated curve (the exponential model) is as follows: TEA ¼ eð0:586Preindex of Entrepreneurial Capabilityþ1:137Þ  error

ð4:11Þ

The above dispersion diagram (Fig. 4.9) also refers to the strong and positive relationship between the pre-index and the rate of entrepreneurial activity. As shown in Fig. 4.9, the compound, growth, exponential, and logistic charts are matched onto each other. This assessment shows that the new pre-index of entrepreneurial capability can be applied solely for the prediction of the rate of entrepreneurial activities that do not necessarily impact the economy (and human development).

110

4 Entrepreneurial Capability Index

Fig. 4.9 Scatter plot of the pre-index of entrepreneurial capability vs. the rate of entrepreneurial activities. Source: Authors’ own figure

The question that arises in this section is: Does the interaction of individual factors and environmental factors can separate efficient businesses from total entrepreneurial activities? The next section presents a thorough response to this point.

4.5.3

Stage Three: Entrepreneurship Driving Force

As you studied in previous sections, the interaction between individual factors and environmental factors cannot measure the part of entrepreneurship affecting the economic growth (or life–health) of societies. Additionally, the only application of the pre-index of entrepreneurial capability is to predict the rate of entrepreneurial activity that not necessarily has remarkable effects on the economic cycle of societies. Meanwhile, the lack of existence of a stimulus factor affecting the successful businesses in line with the calculation of a reliable entrepreneurship index such as the Entrepreneurial Capability Index is thoroughly observable.

4.5 Entrepreneurial Capability Indexing Procedure

111

Fig. 4.10 Conceptual model of the Entrepreneurial Capability Index. Source: Authors’ own figure

For Example If we resemble an efficient business to a car with a strong body (environmental factors) with a driver who has sufficient capability to drive it (individual factors), so the question that arises here is this: Is this powerful and fast car, whose driver is skilled, able to move with the engine off? Since a car will not be able to move without ignition of the engine, although it is the fastest car and has the best driver, so an entrepreneur even if has the best individual abilities (such as skill, capability, education, risk-taking, understanding opportunity, and so on) and if lives in a society with the best business environment will not be able to run a successful business if there is no hope and motivation to startup the business. Given the above example, it seems that the entrepreneurship motivation index is a vital sub-dimension for the accurate assessment of an index to measure the entrepreneurial capability across countries. As a result, in this chapter, we assert that having the individual competencies (e.g., entrepreneurial skills perceiving opportunity) along with environmental factors will not necessarily reflect the status of the quality of entrepreneurial activities in a community. To this end, there is a need for identifying a variable for determining the right interaction of environmental and individual factors. For more details, see Fig. 4.10. In which, “ECI” is the notation for the Entrepreneurial Capability Index, “EECI” is the abbreviation for Effective Entrepreneurial Activities Index, “IDOM” is a notation of the Improvement-Driven Opportunity Motivate, and “other EA” signifies any other entrepreneurial activities. As the model shows, a small subgroup of total entrepreneurial activities that the economic cycle of any country mostly is under the effect of this group is, arbitrarily, named effective entrepreneurial activities. For summarizing the concepts behind this index and the interaction of individual and environmental factors, the Entrepreneurial Capability Index is considered as the main concept during this chapter. In order to examine the relationship between motivation index and the percentage of TEA that is launched with Improvement Opportunity-Driven motive (including financial independence or intention of high-income generation), the following regression models have been provided (Table 4.5).

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4 Entrepreneurial Capability Index

Table 4.5 Regression-based models for the Improvement-Driven Opportunity motive vs. the Entrepreneurial Motivation Index Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .584 81.449 .662 113.790 .589 83.204 .670 57.736 .671 38.239 .539 67.717 .672 105.663 .618 93.972 .539 67.717 .539 67.717 .539 67.717

df1 1 1 1 2 3 1 1 1 1 1 1

df2 58 58 58 57 56 58 58 58 58 58 58

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 33.681 6.034 36.651 16.388 64.113 27.148 21.835 16.090 18.224 20.707 34.530 1.133 36.470 .350 4.195 .601 3.542 .125 34.530 .125 .029 .882

b2

b3

1.550 3.131

.154

Source: Authors’ own table

In these regression estimations, we used the motivation index as the independent variable and the rate of TEA (under the approaches of financial independence or more income) as the dependent variable. Among the 11 linear and nonlinear models, although all models are significant at the level of 95%, the maximum determination coefficient pertains to the power model. The R-square of the power model is more than 67%. This means that the motivation index is able to predict the variations of the dependent variable up to 67%. In other words, for calculation of the rate of entrepreneurial activities that influence the economic cycle (or HDI), the motivation index allows researchers to access the group of such entrepreneurs who affect the life–health. See the scatter plot of these variables and the curve estimated (power model) for the relationship between these indicators in Fig. 4.11. The equation of the estimated regression-based model is as follows: ln ðIDOmÞ ¼ 36:47 þ 0:804  ln ðEMIÞ  error or, equivalently IDOm ¼ k  EMI0:804  error

ð4:12Þ

where ln is the notation for natural logarithm function and k ¼ e36.47. In fact, this equation shows an allometric relationship between considered variables.

4.6 Calculation of Entrepreneurial Capability Index

113

Fig. 4.11 Scatter plot of the Improvement-Driven Opportunity motive vs. the Entrepreneurial Motivation Index. Source: Authors’ own figure

4.6

Calculation of Entrepreneurial Capability Index

With regard to the explanations in the previous sections, we aim to calculate the Entrepreneurial Capability Index in this section. As mentioned, in order to extract the interaction effects of environmental factors, individual factors, and motivation index, the geometric mean (GM) formula will be applied for this purpose. The stages for calculating this index are summarized as follows: Stage 1: Individual Factor As referred to in the relevant section, the geometric mean equation utilized for calculation of individual factor was as follows: Individual Factor for jth country ¼ Ind j qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 5 ðRole ModelÞ j  ðPerceived OpportunityÞ j  ðPerceived CapabilityÞ j  ðRisk  TakingÞ j  Intention j

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4 Entrepreneurial Capability Index

Stage 2: Pre-Index of Entrepreneurial Capability As such, the pre-index for entrepreneurial capability was calculated using the following equation: Pre  Index of Entrepreneurial Capability for jth country ¼ P j qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 2 Ind j  ðEnvironmental FactorÞ j Stage 3: The Rate of Entrepreneurial Capability that Leads to Effective Entrepreneurship This rate, in summary, named the Entrepreneurial Capability Index and its equation can be written as follows: Entrepreneurial Capability Index for jth country ¼ ECI j qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ 2 P j  ðEntrepreneurial Motivation IndexÞ j

ð4:13Þ

To create convenient circumstances for the comparison of different variables with different units, it is necessary to use some relevant methods which have been developed in mathematics and statistics. The creation of unitless variables is one of the ways to compare them in terms of their value. One of the scientific right methods to make unitless variables is standardization. For example, because entrepreneurship indicators (like TEA, established businesses rate, Entrepreneurship Intention Index, or entrepreneurial capability herein) may vary from society to society or even from person to person, so the real amount of these indexes may have different values. To this end, with the aim of making the comparable units, we use the standardization method. For instance, if the Entrepreneurial Capability Index in France is 30% and in Colombia is 60%, so are the Colombians more capable in the initiation of a business than French? Perhaps because of the high competition in launching businesses in France and the low competition in Colombia, the amount of these two indicators are equivalent. In point of fact, 30% of the entrepreneurial capability which has been estimated in Colombia maybe activate the same opportunities as much as 60% of this index provides chances in France. To avoid these problems, in the next section, we will go through various methods for standardizing the variable unit.

4.6 Calculation of Entrepreneurial Capability Index

4.6.1

115

Standardization

When you are doing data analysis, you might find yourself with a number of different variables to work with. For example, perhaps you have invited participants to your lab and run them through your experiment. You have collected data on variables such as the participant’s age (in years), their reaction time to a particular stimulus (in milliseconds), their reaction time to another stimulus (in milliseconds), and their rating of a preference (on a 1–10 scale). When you want to analyze this data to find patterns in it, you first need to make sure that your data variables are compatible with each other. Considering the example above, the two measures of reaction time would be comparable, as they are both measured in milliseconds and should be rather similar to each other. However, how would you look at, say, the relationship between age and reaction time? To do this, you need to use a statistical technique called standardization. The researchers are not authorized to compute different variables with different units to create a new index. In order to make a new index by some variables, there is a need to create a common unit. Since some variables (such as innovation, export, etc.) seem to have no unit (or hard to obtain), so remaking the variables to omit their units will be helpful. In other words, in this case: “

No unit ” is the unit of measure for variables

In statistics, standardization is the process of putting different variables on the same scale. This process allows you to compare scores between different types of variables. Typically, to standardize variables, you calculate the mean and standard deviation for a variable. Confusingly, standardization can have several meanings, depending on the field. In addition, to add to the confusion, others may refer to the same process as normalization. The process we are talking about here is defined as the transforming of data of different types to a uniform scale so that they can be compared. Some different types of standardization are as follows. • Z-score—The most common method for standardization is the calculation of a zscore. To do this, you need to know the mean and standard deviation of the population which your data is drawn from. You calculate a z-score by subtracting the mean of the population from the score in question and then dividing the difference by the standard deviation of the population. This means that each variable will have a mean of 0 and a standard deviation of 1, so you can compare your different variables meaningfully:

116

4 Entrepreneurial Capability Index

Table 4.6 Standardization models Standardization model Z-score

Advantage • Considers the dispersion of data

xi SDðxÞ

• Considers the dispersion of data

xi  min ðxÞ max ðxÞ min ðxÞ

• Considers the variation range of data • Considers the dispersion of data

xi  min ðxÞ SDðxÞ

xi max ðxÞ

xi min ðxÞ

xi max ðxÞ min ðxÞ

• Compares the data with respect to the maximum value • If; yi ¼ maxxi ðxÞ, yi is always less than 1 • No advantage (in this research) • If; yi = minxi ðxÞ, yi is always more than 1 • Considers the variation range of data

xi meanðxÞ

• No advantage (in this research)

xi sumðxÞ

• No advantage (in this research)

Disadvantage • Compares the data only with respect to the average value • Sensitive to outlier data • Sensitive to outlier data • Compares the data only with respect to the minimum value • Compares the data only with respect to the minimum value • Sensitive to outlier data • It is a relative comparison

• It is a relative comparison

xi • If; yi ¼ max ðxÞ min ðxÞ, yi is not always less than 1 • Compares the data with respect to the average value xi • If; yi ¼ mean ðxÞ, mean(yi) ¼ 1

• Compares the data with respect to the summation value • If; yi ¼ sumxiðxÞ, mean(yi) ¼ 1

Source: Authors’ own table

xi  min ðxÞ x  min ðxÞ xi xi xi , , , , i , SDðxÞ max ðxÞ  min ðxÞ max ðxÞ min ðxÞ SDðxÞ xi xi xi , , , ... max ðxÞ  min ðxÞ meanðxÞ sumðxÞ Using these formulas, a new structure of the dataset will put different variables on the same scale (no scale). Where xi is the amount of the answer to the question x (for i-th person) and SD(x) represents the standard division of the variable x and min(x), max(x), sum(x), and mean(x) are the minimum, maximum, summation, and mean values of the variable x. Table 4.6 presents the advantages and disadvantages of each standardization model with respect to the topics of this book. The use of these standardization models will lead to rescale datasets and, in its wake, will make the same units, and that doing so will allow individuals to compare the manipulated data. But the use of a specific type of these models will be more beneficial with the least disadvantage.

4.6 Calculation of Entrepreneurial Capability Index

117

The selection of the most-fitted model for standardizing the data depends on the type of data and researchers’ objectives. As our data have been arranged in a positive direction (positive spectrum), so the maximum measure of each variable is the most valuable in our study. In other words, comparing this data with the maximum value will compute the rates in a positive direction. Additionally, since the maximum value of this data is 14 (after using the six sigma method), therefore the maximum value is a constant parameter. In short, the maxxi ðxÞ  100 is the standardization model that will be used during this study.

4.6.2

Entrepreneurial Capability Index

The results of the Entrepreneurial Capability Index (ECI) based on the Global Entrepreneurship Monitor (GEM) dataset in the year 2015 have been sorted in Table 4.7. As shown in Table 4.7, the entrepreneurial capability in Luxemburg, Switzerland, Norway, and Sweden is the top four values of this index among countries that have participated in the GEM survey in the year 2015. This means that the individual and environmental factors besides the entrepreneurial motivation of the people in these countries have made convenient circumstances for launching the most positive and effective entrepreneurial activities that mainly influence the economic situation and life–health in these countries. Indeed, the environmental conditions in these countries create an appropriate situation to make a feasible plan for doing a business that influences life quality. On the other hand, this open space gives a striking opportunity to the people to continue their own entrepreneurial activities with higher motivation approach, whereas the people of Bulgaria, Egypt, Burkina Faso, and Guatemala are at the bottom of this table. Thereby the inappropriate condition, in terms of individual and environmental factors, in these countries will not allow their people to focus on their interests and ideas for launching effective businesses in order to flourish the economy at least in their country. For increasing their life quality and in order for the fulfillment of their daily needs, the people of these countries have to process any activity to increase income. While, in contrast, because of a lack of sufficient knowledge as to such businesses that society requires and no doubt is accompanied by profits for their owners, instead of the start of unique and innovative activities, people start to run repetitive businesses which are easily launched by everyone. Execution of similar businesses will meet insignificant credibility, whereas low-frequency ones, which bring more job opportunities and more income, are visible in innovational businesses. Perhaps this is the reason why the rate of exit from business in these countries increases. Furthermore, when the basic needs of people do not resolve, they are compelled to launch a weak business only for gaining the primary needs. On the other hand, clearly, to have a stable and profitable business, a comparatively long period of time

Estonia

Israel Barbados Chile Malaysia Thailand

10

11 12 13 14 15

86.4272 86.15233 83.39928 82.51837 82.38974

88.21737

95.98524 90.38834 90.04486 89.44878

Source: Authors’ own table Asterisks indicate unavailable data

6 7 8 9

Country Luxembourg Switzerland Norway Sweden United States Australia Netherlands Canada Finland

Rank 1 2 3 4 5

Entrepreneurial Capability Index (ECI) 100 99.09683 98.71239 97.56879 96.05475

26 27 28 29 30

25

21 22 23 24

Rank 16 17 18 19 20

Argentina Iran Indonesia Colombia South Korea

Lebanon Ireland Peru United Kingdom Botswana

Country Germany Tunisia Uruguay Latvia Mexico

71.11585 70.9809 70.44637 70.18548 70.08225

72.29593

76.27825 75.37706 74.72182 73.00675

Entrepreneurial Capability Index (ECI) 81.80893 81.50505 80.71028 79.70186 77.73954

41 42 43 44 45

40

36 37 38 39

Rank 31 32 33 34 35

Table 4.7 The Entrepreneurial Capability Index (ECI) based on GEM dataset in 2015

Italy Romania Brazil Vietnam Kazakhstan

Ecuador

Spain Belgium Portugal Senegal

Country Hungary Poland Slovakia Slovenia Philippines

62.81436 62.79783 61.78184 61.06607 60.42899

63.40091

66.24288 65.6709 64.81651 63.9177

Entrepreneurial Capability Index (ECI) 70.03656 68.74869 68.31644 68.01157 67.17245

56 57 58 59 60

55

51 52 53 54

Rank 46 47 48 49 50

Burkina Faso Egypt Bulgaria Macedonia Taiwan Puerto Rico

South Africa Cameroon Croatia Guatemala

Country Morocco Greece Panama India China

52.94597 50.41407 ** ** **

53.11884

57.42721 57.39932 57.19764 55.01081

Entrepreneurial Capability Index (ECI) 60.33468 59.5599 59.13145 58.31061 58.29841

118 4 Entrepreneurial Capability Index

4.6 Calculation of Entrepreneurial Capability Index

119

Table 4.8 Regression-based models for the Entrepreneurial Capability Index (ECI) and Human Devolvement Index (HDI)

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .359 30.220 .365 31.060 .367 31.272 .364 15.156 .380 15.156 .322 25.610 .332 26.893 .337 27.698 .322 25.610 .322 25.610 .322 25.610

Parameter estimates df1 1 1 1 2 2 1 1 1 1 1 1

df2 54 54 54 53 53 54 54 54 54 54 54

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Constant .417 .860 1.191 .161 .161 .470 .082 .301 .754 .470 2.126

b1 .005 .388 27.794 .012 .012 1.007 .529 38.204 .007 .007 .993

b2

b3

4.603E-5 4.603E-5

.000

Source: Authors’ own table

is required. Maybe this fact that the weak businesses are launched mostly due to the maintenance of existing income is the remarkable aspect of weak businesses that finally leads to bankruptcy. The owners of such businesses not only obtain no profits but lose what they invested for their startup after a short time. Probably this is the reason why the most part of entrepreneurial activities in factor-driven economies not only have no positive impact on the economy but lead society towards an economic downturn. In general, although the environmental and individual factors drive the main role in startups, but for launching businesses that are able to accelerate the improvement of economic conditions, a strong motivation is required. To show how the Entrepreneurial Capability Index can be advantageous in studies of economic development factors (and life quality in its wake), the assessment of several regression-based models is required. In the following table (Table 4.8), we have accomplished several regressionbased estimations in order to observe the relationship between the Entrepreneurial Capability Index (as an independent variable) and the Human Development Index (as a dependent variable). Among the 11 linear and nonlinear models, although all models are significant at the level of 95%, the maximum determination coefficient belongs to the Cubic model. The R-square of the Cubic model is 38%. This means the Entrepreneurial Capability Index is able to predict the variations of the Human Development Index by 38%. Figure 4.12 shows the scatter plot and estimated Cubic curve for this relationship. This model shows that with the increase in the amount of entrepreneurial capability, the value of the Human Development Index (which represents the health of a community) will grow. In other words, whatever the amount of a community’s

120

4 Entrepreneurial Capability Index

Fig. 4.12 Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Human Devolvement Index (HDI). Source: Authors’ own figure

ability to run businesses is high; it reflects the stability and trustworthiness of the business sector in that country. This may, in turn, hold a positive effect on the startup of businesses that have a direct impact on the economic cycle. The estimated Cubic model equation is as follows: HDI ¼ 2:44 þ 0:12  x  1:5E  3  x2 þ 6:4E  6  x3  error

ð4:14Þ

where x denotes the ECI and E  a ¼ 10a. All in all, by increasing the amount of entrepreneurial capability that comes from financial support (herein Economic Index), individual attitude factor, and motivation of business startups, the social health and quality of life can increase. For example, if we consider the Gross Domestic Product (GDP) per capita as an indicator for the economic well-being of any individual in a country, we can examine the relationship between the Entrepreneurial Capability Index (ECI) and this income-based indicator. The results of the “goodness of fit” based on regression models between Entrepreneurial Capability Index and Gross Domestic Product (GDP) per capita is summarized in the following table (Table 4.9). Among the 11 linear and nonlinear models, although all models are significant at the level of 95%, the maximum determination coefficient belongs to the Cubic

Model summary R square F .507 56.658 .471 48.894 .431 41.698 .592 39.238 .667 41.750 .417 39.339 .413 38.735 .405 37.364 .417 39.339 .417 39.339 .417 39.339

Source: Authors’ own table

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

df1 1 1 1 2 2 1 1 1 1 1 1

df2 55 55 55 54 54 55 55 55 55 55 55

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant 41,287.398 251,043.307 87,724.397 113,917.570 38,835.635 1171.745 .112 12.698 7.066 1171.745 .001 909.406 64,610.306 4,424,878.380 3329.892 .000 1.039 2.821 199.709 .038 .038 .962

b1

Table 4.9 Regression-based models for Gross Domestic Product (GDP) per capita and the Entrepreneurial Capability Index (ECI)

28.000 19.893

b2

.223

b3

4.6 Calculation of Entrepreneurial Capability Index 121

122

4 Entrepreneurial Capability Index

Fig. 4.13 Scatter plot of Gross Domestic Product (GDP) per capita vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure

model. The R-square of the Cubic model is 66.7%. This means the Entrepreneurial Capability Index is able to predict the variations of the GDP per capita by about 67%. In summary, by increasing the entrepreneurial capability of society, many entrepreneurs enter into the field of competition, and with high-quality innovations and products, they try to compete with others. Such competition will lead to innovation-driven business markets which, in addition to the creation of the products for export, will present high-quality goods for the inside consumers. Further, these activities will allow business owners to create more jobs. Therefore, in its wake, the opportunities to land a good job in these societies will grow, and the level of income per capita increases also. See Fig. 4.13. On the whole, the Entrepreneurial Capability Index (ECI) is an index that is able to measure the approximate amount of the entrepreneurial activities affecting the economic cycle positively. The real values (value before standardization) of this index are presented in the Appendix section (Appendix K). Note that this is the exact amount of the rate of entrepreneurship affecting the economic (or more precisely, the factual amount of the rate of “Entrepreneurial Capability” affecting economic cycle). The equation of the best-fitted model for Entrepreneurial Capability Index (as the independent variable) and GDP per capita (as the dependent variable) is as follows:

4.6 Calculation of Entrepreneurial Capability Index

GDP ¼ 6:98E5 þ 3:03E4  x  4:26E2  x2 þ 2  x3  error

123

ð4:15Þ

in which x denotes the ECI. As Fig. 4.13 displays, the results of regression estimation can also be analyzed by different economic groups. What seems to be reasonable is the strong connection between the index of entrepreneurial capability and the amount of Gross Domestic Product per capita in innovation-driven countries (high-income economies). Perhaps this is due to the high impact of environmental factors in these communities. Although the sample size of each economy group is not remarkable, as mentioned in Appendix A2, we derived the regression-based estimation for ECI (as an independent variable) and GDP per capita (as a dependent variable) for all groups (factor-driven, efficiency-driven, and innovation-driven economies). To show how entrepreneurial abilities affect the situation of business startups, we made attempt to examine the relationship between the Entrepreneurial Capability Index (ECI) with the rate of Total early-stage Entrepreneurial Activities (TEA) that have been launched with independence or increasing income motives (named improvement-driven opportunity motive). The results of this fitted curve are summarized in Table 4.10. Among the 11 linear and nonlinear models, although all models are significant at the level of 95%, the maximum determination coefficient pertains to the quadratic model. The R-square of the quadratic model is more than 60%. This means the ECI is able to predict the variations of the dependent variable up to 60%. This result shows that the growth of the entrepreneurial capability in a country allows entrepreneurs to keep up with their own well-selected3 direction, which makes safe economic conditions and also brings a safe life. Figure 4.14 presents the scatter plot of these variables. The estimated Quadratic equation which has been deemed as the best-fitted model is as follows: y ¼ 63:93 þ 2:42  x  0:01  x2  error

ð4:16Þ

where y refers to the rate of the TEA that is calculated into the group of motivated entrepreneurs (called improvement-driven opportunity) and x denotes the ECI. In addition, x is being defined in the range of 0–100. At the end of this chapter, we presented another type of business activity to prove that entrepreneurial capability plays a key role in creating a healthy world by activation of effective entrepreneurship. One of the serious parts of the business world is referred to as the organizational business. This effective portion of the business sector is frequently visible in small

3 The accurate meaning of well-selected direction in the business sectors refers to a path in which the choice of any business is not out of necessity and it is only because of high motivation.

Model summary R square .577 .594 .600 .607 .605 .556 .579 .590 .556 .556 .556

Source: Authors’ own table

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

F 74.973 80.508 82.329 41.711 42.093 68.926 75.539 79.175 68.926 68.926 68.926

df1 1 1 1 2 2 1 1 1 1 1 1

df2 55 55 55 54 54 55 55 55 55 55 55

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 .163 .671 165.662 50.269 100.326 3612.756 63.931 2.422 44.706 1.596 17.078 1.014 .517 1.059 4.948 76.482 2.838 .014 17.078 .014 .059 .986

b3

5.266E-5

b2

.012 .000

Table 4.10 Regression-based models for the Total early-stage Entrepreneurial Activities based on Improvement-Driven Opportunity motive and the Entrepreneurial Capability Index (ECI)

124 4 Entrepreneurial Capability Index

4.6 Calculation of Entrepreneurial Capability Index

125

Fig. 4.14 Scatter plot of rate of the Total early-stage Entrepreneurial Activities based on Improvement-Driven Opportunity motive vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure

and big institutions, and even governmental entities often provide exceptional conditions to their employees for such activities.

4.6.3

Entrepreneurial Employee Activity

The surveys carried out by the Global Entrepreneurship Monitor (GEM) have traditionally concentrated on the characteristics, motivations, and ambition of those who launch new businesses under 42 months old. But, newly, Global Entrepreneurship Monitor has begun to focus on another portion, too: the entrepreneurial activities that are being run inside the other organizations or established centers. Specifically, it includes entrepreneurial activities that have been launched by the employees who are trying to develop new goods and services for the main employer. This investigation reaffirms the existence of another type of entrepreneurship called Entrepreneurial Employee Activity (EEA) which will be found in established businesses. Likewise, based on the GEM’s reports, Entrepreneurial Employee Activities not only appear in the business sector but are able to be considered in the public sector, too. Further, these types of entrepreneurial activities can be found

Model summary R square .506 .482 .452 .536 .534 .381 .380 .375 .381 .381 .381

Source: Authors’ own table

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

F 56.269 51.076 45.334 31.136 31.484 33.813 33.749 33.027 33.813 33.813 33.813

df1 1 1 1 2 2 1 1 1 1 1 1

df2 55 55 55 54 54 55 55 55 55 55 55

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 6.630 .134 38.031 9.633 12.587 667.601 6.934 .237 1.520 .000 .061 1.049 5.423E-7 3.540 4.264 251.578 2.803 .048 .061 .048 16.489 .953

b3

1.550E-5

b2

.002 .001

Table 4.11 Regression-based models for the Entrepreneurial Employee Activity (EEA) and the Entrepreneurial Capability Index (ECI)

126 4 Entrepreneurial Capability Index

4.6 Calculation of Entrepreneurial Capability Index

127

Fig. 4.15 Scatter plot of the rate of Entrepreneurial Employee Activity (EEA) vs. the Entrepreneurial Capability Index (ECI). Source: Authors’ own figure

in various parts worldwide, especially with greater frequency in the innovationdriven economies (also called developed countries). For the purpose of appraisal of the impact of the Entrepreneurial Capability Index on the Entrepreneurial Employee Activity (EEA), we estimated a total of 11 models to obtain the relationship between these indices. Table 4.11 displays the results of these regression-based offers. This table has gathered advantageous information on the status of the goodness of fit for 11 linear and nonlinear models. The determination coefficient (R-square) and the P-value (specified with sig.) are the criteria for the model selection. As this table shows, all 11 models are significant at the level of 95% (because the P-value is less than 0.05). Hence, according to the determination coefficient, the R-square value of the quadratic model is more than of other ones. The R-square of the quadratic model is about 54%. It means that the rate of entrepreneurial capability is able to predict the fluctuations of the Entrepreneurial Employee Activity (EEA) by about 54%. See the scatter plot of the said indexes below (Fig. 4.15). The estimated equation of this quadratic model is as follows: EEA ¼ 6:93  0:24  x þ 2:45E  3  x2  error

ð4:17Þ

where x is the notation of the ECI which is being defined between 0 up to 100%.

128

4.7 4.7.1

4 Entrepreneurial Capability Index

Results Overview

Since entrepreneurship is a driving force of the economic development of societies, so, as you studied, our goal was to examine the rates of entrepreneurs who their activities have a positive impact on the economic cycle at the country level. According to the investigations conducted in this research, the rate of entrepreneurship in factor-driven (undeveloped or low-income) countries was much higher than those of innovation-driven countries (developed or high income). On the other hand, the rate of exit from business in low-income countries was higher than in high-income countries. In addition, throughout this chapter, it was shown that entrepreneurial attitudes in factor-driven countries are much more than innovationdriven ones. At first glance, these results seem to be much more confusing. According to GEM reports, there are societies with both high levels of individual factors and high rates of entrepreneurial activities. Nonetheless, these countries can be developed, and correspond to low-income groups. Therefore, it seems that a high percentage of entrepreneurial activities in factordriven economies not only have no positive effects on economic development but also reduce the health of communities. This contradiction induced us to look for an indicator for evaluating the rate of entrepreneurial activities that specifically have a positive effect on the economic cycle. In this regard, we studied individual factors (role model, perceived opportunity, perceived capability, risk-taking, and entrepreneurial intention) and environmental factors (herein only the Income Index) as factors influencing entrepreneurship. In addition, we used the geometric mean to take account of the interactions of sub-indicators. In this chapter, it has been shown that the use of an index that is resulted from the interaction of both the environmental factors and the individual factors can only indicate the rate of any type of entrepreneurship (whether or not effective on economic) which is defined by the Global Entrepreneurship Monitor (GEM).4 Thereby, the interactions between individual and environmental factors do not apply to express the entrepreneurial rate which affects the economics of societies. Thus, in the next step, we found that the motivation of entrepreneurs plays the main role in accelerating the business owners’ activities which are associated with positive results in the country’s economic cycle. Eventually, by using the geometric mean equations in this section, an indicator entitled “Entrepreneurial Capability Index” was calculated for 57 out of 60 Global Entrepreneurship Monitor (GEM) member countries in the year 2015.

4

Note that this idea is the base of the Global Entrepreneurship Index (GEI) which is known as one of the most renowned entrepreneurship indexes worldwide. In fact, the interaction of individual and environmental factors is the underlying method for authors of the GEI to release the annual reports.

References

4.7.2

129

Conclusion

In order to assess the contribution of the entrepreneurial capability to improving community health, a collection of linear and nonlinear regression models between Entrepreneurial Capability Index (ECI) and Human Development Index (HDI) were estimated. The results showed that the Entrepreneurial Capability Index can predict fluctuations and variations in the Human Development Index up to 38%. In addition, it has been shown that this index has a strong and positive effect on the health of a community. On the other hand, the relationship between the Entrepreneurial Capability Index and the Gross Domestic Product (GDP) per capita was estimated using 11 regression models. The results of these estimates also confirm that this index is capable of predicting 67% of GDP’s variations. The regression graph and estimated models of these two variables also clarified that the effect of entrepreneurial capability in the growth of the GDP per capita is very remarkable. In other words, if the potential of entrepreneurship increases, the economic security and health of the community will grow sharply. In another assessment, we considered the relationship between the Entrepreneurial Capability Index and the rate of entrepreneurs who had started a business with the approach of increasing income and/or financial independence. We found that these two indexes are in a strong relationship. That is, the Entrepreneurial Capability Index can predict more than 60% of the variations in the rate of entrepreneurial that are launched based on income growth and/or financial independence. In the last step, in order to prove the reliability of the Entrepreneurial Capability Index (ECI), we examined the relationship between this new index and the indicator of the entrepreneurial Employee Activities (EEAs). The results of these regression equations also showed that the Entrepreneurial Capability Index can measure up to 54% of the variations of the Entrepreneurial Employee Activity. This index also referred to the fact that by increasing the capabilities of business startups, which depends on the entrepreneurial environment, entrepreneurial attitudes, and entrepreneurial motivation, the rate of Entrepreneurial Employee Activity that affects the economy and also increases the health of life will grow sharply.

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Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211 Aldrich H (2000) Organizations evolving. Sage, Beverly Hills Audretsch DB, Keilbach M (2004) Does entrepreneurship capital matter? Enterp Theory Pract 28 (5):419–429 Bula HO (2012) Performance of women entrepreneurs in small scale enterprises (SSEs): marital and family characteristics. Eur J Bus Manag 4(7):85–99 Cooney T (2012) Report for the workshop on skills development for SMEs and entrepreneurship: entrepreneurship skills for growth-orientated businesses. OECD, Paris Cruz LD, Suprapti S, Yasa K (2015) Aplikasi theory of planned behavior Dalam Membangkitkan Niat Berwirausaha Bagi Mahasiswa Fakultas Ekonomi Unpaz, Dili Timor Leste. E-Jurnal Ekonomi dan Bisnis Universitas Udayana 4(12):895–920 Drucker P (2014) Innovation and entrepreneurship. Routledge Edelman LF, Manolova TS, Brush CG (2008) Entrepreneurship education: correspondence between practices of nascent entrepreneurs and textbook prescriptions for success. Acad Manag Learn Educ 7(1):56–70 European Commission (2006) Recommendation of the European Parliament and of the Council of 18 December 2006 on key competencies for lifelong learning (2006/962/EC) European Commission (2012) Towards a job-rich recovery European Commission (2013) Entrepreneurship 2020 action plan: reigniting the entrepreneurial spirit in Europe report based on the Eurobarometer Survey on Entrepreneurship Faghih N, Bonyadi E, Sarreshtehdari L (2019) Global entrepreneurship capacity and entrepreneurial attitude indexing based on the global entrepreneurship monitor (GEM) dataset. Global Dev 13–55 Fairlie RW (2017) Opportunity versus necessity entrepreneurship: two components of business creation. Paper No. 17-014 Gartner WB (1988) “Who is an entrepreneur?” is the wrong question. Am J Small Bus 12(4):11–32 Gilad B, Levine P (1986) A behavioral model of entrepreneurial supply. J Small Bus Manag 4:45–53 Hannan MT, Freeman J (1984) Structural inertia and organizational change. Am Sociol Rev 49:149–164 Hartwick J, Barki H (1994) Explaining the role of use participation in information system use. Manag Sci 40(4):440–465 Hill RC, Levenhagen M (1995) Metaphors and mental models: sensemaking and sensegiving in innovative and entrepreneurial activities. J Manag 21(6):1057–1074 Hisrich RD, Peter MP, Shepherd DA (2009) Entrepreneurship. McGraw-Hill/Irwin Hofstede G, Noorderhaven NG, Thurik AR, Uhlaner LM, Wennekers AR, Wildeman RE (2004) Culture’s role in entrepreneurship: self-employment out of dissatisfaction. Innovation, Entrepreneurship and Culture: the interaction between technology, progress and economic growth, 162203 Kabui WE, Maalu J (2012) Perception of entrepreneurship as a career by students from selected public secondary schools in Nairobi. DBA Africa Manag Rev 2012 2(3):101–120 Kolvereid L, Isaksen E (2006) New business start-up and subsequent entry into self-employment. J Bus Ventur 21:866–885 Krueger NF (1993) The impact of prior entrepreneurial exposure on perceptions of new venture feasibility and desirability. Entrepreneur Theory Pract 18(1):5–21 Kuratko D, Welsch H (2003) Strategic entrepreneurial growth. South-Western College, Bloomington Lee Y, Kozar K (2005) Investigating factors affecting the anti-spyware system adoption. Commun ACM 48(8):72–77 Moy J, Luk V, Wright P (2003) Perception of entrepreneurship as a career. Views of young people in Hong Kong. Equal Oppor J 22(4) Naudé WA (2011) Entrepreneurship is not a binding constraint on growth and development in the poorest countries. World Dev 39

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Chapter 5

Entrepreneurship Viability Coefficient

The main idea throughout this chapter is the verification of the relationship between the Entrepreneurial Capability Index and the Entrepreneurship Viability Index. To this end, we aim to examine the impact of entrepreneurial capability on the entrepreneurship viability. Additionally, the appraisal of the contribution of the entrepreneurial capability to the viable entrepreneurship, which is an indicator of a healthy society and a progressive economy, would be characterized in this chapter. A study on viability concept in the field of entrepreneurship requires an in-depth investigation into the business sector with either profitable or unprofitable–inefficient activities. If something is viable, it means that it can work successfully. In other words, it is capable of being run and will overcome difficulties and also can set aside obstacles, too. For example, in botany, a seed is viable if it has the ability to sprout. Particularly, from the view of biology, the term viable is used when the plant can survive under difficult conditions. We use this term for anything or any situation which can continue developing, growing, or living successfully. The use of this term for describing a company refers to the company’s health that means it is able to last for a long time and finally can overcome the difficulties at the end of the period of environmental pressures. In business, this term is being used for describing the feasibility of running a business plan. What seems to be remarkable in the viable businesses is their profitability and their advantageousness that, no doubt, influence economic situation. With a view to this reasonable theory that the entrepreneurial activities are an economic driving force, which is known as Schumpeterian entrepreneurship, there is a need to prove it by means of any scientific ways available to researchers. On the one hand, the accumulated information about the status of entrepreneurial activities, gathered from several institutes and organizations around the world, is associated with just a trifle result in entrepreneurship investigations. In fact, the

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_5

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nonexistence of researchers who publish basic theories over the reliable relationship between the entrepreneurship factors has made the big volume of data useless. The emergence of the Global Entrepreneurship Monitor (GEM), as the world’s foremost consortium for studying entrepreneurship issues, has provided a unique field for studying entrepreneurship individual and environmental factors. Apart from the comprehensive and well-defined GEM dataset, the separation of this dataset into various dimensions like individual factors, environmental factors, established businesses, early-stage entrepreneurial activities, and so on is so admirable. As you observed, for creating the Entrepreneurship Viability Index, we applied a statistical method in Chap. 3, and also in Chap. 4, we assessed the Entrepreneurial Capability Index for the first time. In the present chapter, we attempt to introduce another concept of entrepreneurship with the use of both newly developed indexes. As the main objective, at the end of this chapter, we will comprehend the correlation of created indices.

5.1

Assessment of Entrepreneurship Viability Coefficient

What seems to be logical between the Entrepreneurship Viability Index and the Entrepreneurial Capability Index is the high and positive effect of entrepreneurial capability on the viability of business–entrepreneurship across countries. In other words, in this section, using linear and nonlinear curves, we intend to show that whatever the entrepreneurial capability increases, the size of the entrepreneurship viability will also grow. As is shown in Chap. 4, you studied that high measure of the Entrepreneurial Capability Index conducts a community toward the high value of the Human Development Index (HDI), where HDI is used as the health of the society. Furthermore, the high Entrepreneurial Capability Index creates a high amount of GDP per capita for the countries. The main reason that has induced us to present this chapter is an evaluation of the impact of entrepreneurial capability on the viability of businesses. Generally, we will represent that only the high entrepreneurial capability which has a strong and positive impact on the economic cycle will make a reliable business. On the other hand, the reliable entrepreneurial activities (which are launched by using the high abilities) will have a steady effect on economic and alternatively on the life–health of society under study. It will be proven that the entrepreneurial activities that have been started with high capabilities will be viable. Moreover, we will prove that the rate of exit from business in the countries with finally high entrepreneurial capability is declining toward zero. Meanwhile, in order to the careful appraisal of the effect of these indexes on the economy, we created the third index and named it Entrepreneurship Viability

5.1 Assessment of Entrepreneurship Viability Coefficient

135

Coefficient (EVC). Because of the big crowd of the member countries in 2015 than the recent years, we used this dataset to apply ideas existing here.

5.1.1

Calculation of Entrepreneurship Viability Coefficient

As mentioned, the motivation index is an intricate stochastic process which comes from attitudes of the individuals across communities. On the other hand, motivation includes the past, present, and future information about ease or difficulties of access to resources that, in turn, describes all of the individuals’ attitudes and environmental factors in the business ecosystem of a region (herein the countries under study). In summary, the amount of the motivation index involves facts about the entrepreneurial endogenous and exogenous factors that result from skills, capabilities, educations, social norms, risk-taking, government programs, and so on. As the first definition of Entrepreneurship Viability Coefficient (EVC), if we consider the motivation index as the individual factor that reflects from all knowhow about the individual attitudes and influences the entrepreneurship status of a country in present and future additionally and if we consider the Entrepreneurship Viability Index as the lifetime of the entrepreneurial activities, therefore by using the geometric mean (GM) function, we will able to extract the interaction of these two indexes. We call the output of interaction of these indexes the Entrepreneurship Viability Coefficient. The Entrepreneurship Viability Coefficient renders comprehensive information about the remaining lifetime of entrepreneurial activities that have been launched with maximum abilities. The equation of this new index can be written as follows:  1 EVC for the jth country ¼ EVI j  Motivation j 2

ð5:1Þ

This formula will be able to present the full information about the interaction of the remaining lifetime of entrepreneurial activities with the motivation of launching such businesses. More precisely, the value of this geometric mean (GM) presents the result of the interplay of the lifetime of entrepreneurial activities with the motivation of starting a business. In other words, the value of the geometric mean grows if and only if both of the sub-indexes, namely, the entrepreneurship viability and motivation index, increase simultaneously. If at least one of these indexes is small, the amount of the Entrepreneurship Viability Coefficient will be declined. Therefore, only in countries with a high value of durable businesses that have been launched with high motives the amount of Entrepreneurship Viability Coefficient will be remarkable. Entrepreneurship Viability Coefficient (EVC) based on the Global Entrepreneurship Monitor dataset in the year 2015 is calculated and sorted in Table 5.1. Note that the Eq. (4.16) has been applied for this calculation.

Country Switzerland Thailand Norway Estonia Finland Taiwan Netherlands United States Malaysia Barbados Australia South Korea Sweden Canada Latvia

Source: Authors’ own table

Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

EVC 25.5512 19.5300 19.3416 18.8434 16.3879 15.6763 15.3102 15.1703 14.7283 14.7182 14.4734 14.4592 14.2566 14.0457 12.6231

Rank 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Country Germany Puerto Rico Luxembourg Indonesia Lebanon Vietnam Spain United Kingdom Slovenia India Belgium Mexico Tunisia Uruguay Hungary

Table 5.1 The Entrepreneurship Viability Coefficient (EVC) EVC 12.3469 11.9604 11.8397 11.7413 11.2029 11.1347 11.0802 9.8263 9.7448 9.7182 9.7042 9.6818 9.5802 9.4667 9.1970

Rank 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Country Israel Burkina Faso Ireland Chile Senegal Poland Argentina Iran Italy Portugal Colombia Greece China Brazil Peru

EVC 9.0853 8.7064 8.6883 8.6810 8.5458 8.0828 7.8809 7.7840 7.7168 7.5647 7.5299 7.5184 7.3477 7.2143 7.1168

Rank 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Country Ecuador Panama Romania Morocco Guatemala Slovakia Cameroon Bulgaria Croatia Kazakhstan Philippines Botswana South Africa Macedonia Egypt

EVC 6.9439 6.8339 6.8055 6.7842 6.6392 6.2702 6.1908 6.0810 5.9501 5.3244 5.1325 4.8794 4.4572 4.0939 3.0647

136 5 Entrepreneurship Viability Coefficient

5.1 Assessment of Entrepreneurship Viability Coefficient

137

As shown in Table 5.1, the Entrepreneurship Viability Coefficient in Switzerland, Thailand, Norway, and Estonia is the top four amount of this index among countries which have participated in the GEM’s survey in 2015. This means the remaining lifetime of entrepreneurial activities (as the existing evidence) along with the motivation of the people in these countries (as the prior and upcoming evidence) allow the people of these countries to launch the most effective entrepreneurial activities that strongly affect the economic development and life–health. According to this table, businesses that last for a long time and start to work with high motivation can have a huge impact on the economic growth of the community. Thereby, the calculation of the interaction between entrepreneurial motivation and the viability of entrepreneurship in relation to how an entrepreneurial activity can affect the economy of the society under study can be extremely useful. With regard to Table 5.1, the people of Egypt, Macedonia, South Africa, and Botswana are at the bottom of this table. The interpretation of the situation of entrepreneurship in these countries is as follows. Since the interaction of sub-indexes and their constructive variables are multiplying to each other, the reason for the low coefficient of entrepreneurship viability in the countries of the bottom of this table reflects the fact that the viability of the entrepreneurial index and also the Entrepreneurial Motivation Index in these countries have not been satisfactory. As a result, entrepreneurial activities in these countries will not have much impact on the country’s economic cycle. All in all, the entrepreneurial activities of these countries are probably lunched for other purposes. For instance, maintenance of the previous income or nonexistence of adequate job options can be the motives and reasons behind the creation of businesses in such countries. If we divide the business lifetime into two parts, viable and non-viable, and also if we divide the entrepreneurial motivation into high-motivated and low-motivated subgroups, clearly the intersection of these two sub-indexes will be four states (2  2). All of these four situations are summarized in Table 5.2. As shown in Table 5.2, the most influential entrepreneurship in an economy is the entrepreneurial activity that, in addition to its high durability (long lifetime), has also been launched with high motivation. In other words, the entrepreneurship that has a high impact on the economy and has last for a long time in a society can greatly affect the economic development, and also the quality of life of that community will grow in its wake. The Entrepreneurship Viability Coefficient (EVC) is also constituted with this view. Indeed, when the entrepreneurial motivation along with the entrepreneurship viability are simultaneously high valued, then EVC is high, and if at least one of these two sub-indexes has a small amount, then this coefficient will reduce. Therefore, the interpretation of the Entrepreneurship Viability Coefficient is like an entrepreneurial rate that only focuses on that subgroup of the business sector that strongly affects the economic situation and life–health. The other states of these two subcategories, which will have far less impact on the economic cycle, are also presented in Table 5.2.

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Table 5.2 Different types of entrepreneurial activities in view of both the Entrepreneurship Viability Index and the entrepreneurial motivation Viable business

Nonviable business

High-motivated business The most influential entrepreneurial activities are reposed in this subgroup. The businesses in this subgroup are the main driving force of economic development, albeit they are too rare Entrepreneurial activities in this subgroup influence the business sector only for a short period of time

Low-motivated business These types of businesses are common in undeveloped economies and have been launched only for income generations and because of lack of other options in the labor market These types of businesses are common in undeveloped economies and have been launched only for income generations too. The businesses in this subgroup not only have no positive effect on the business sector but, because they cannot supply the primary needs of their owners, will result in a negative effect in the business sector

Source: Authors’ own table

Because the motivation of individual contains all perspectives and perceptions, which include all inner and outside factors, so in order to evaluate the rate of businesses that affect the economic conditions in the country, we can use the interaction between the rate of Total early-stage Entrepreneurial Activities [either nascent (SU), baby (BB), or established (EB)] and the motivation index. Note that studying the group of businesses that have a positive effect on the economic cycle but with a short lifetime is as much as worthless than the noneffective businesses with a long lifetime are. In order to analyze the behavior and trend of the Entrepreneurship Viability Coefficient, we have reviewed a great deal of inter-factors connections. To investigate the accuracy of the coefficient of entrepreneurship viability, we focused on the relationship between this index and some of the global indices that reflect the health of the economies.

5.2

Entrepreneurship and Economic Resilience

Today, economic vulnerability is one of the most important threats that usually arise out of the set of exogenous shocks that affect the economy of a country. In other words, the level of economic vulnerability is defined as the magnitude of external shocks, resulting from economic openness, that may destroy the economy. In contrast, economic resilience is the economic viability against these external shocks. In other words, the amount of an economy’s ability to withstand the threats and shocks is called economic resilience. Economic resilience is a set of norms and regulations that, in the occurrence of any recession over the economy, can maintain the sustainability of the economic system. The stability of a country’s economy

5.2 Entrepreneurship and Economic Resilience

139

strongly depends on its degree of resiliency that shows how it can resist external threats. In this section, we intend to examine the relationship between economic resilience with developed index. Finding a meaningful relationship between the Economic Resilience Index and some of the other key indexes in the economy may offer policymakers and other statesmen a method to control and manage the economy in severe crises. In other words, a dependent variable (such as economic resilience) is never directly controlled by individuals but is influenced by other independent variables. Hence, in order to control and manage economic resilience, some of the variables that are under control must be introduced. For this reason, in this section, we intend to outline the relationship between an entrepreneurship index and economic resilience that may be a solution for policymakers in the economy so that they can control the state of the economy by controlling and managing part of the entrepreneurship indexes. Policymakers can study and manage the status of the entrepreneurial activities affecting the economy (including the Entrepreneurship Viability Coefficient) to apply it for the purpose of improving the quality of economic activities carried out at the country level. Furthermore, this section analyzes how entrepreneurship is central to sustain a dynamic economy and reveals that it is being forefronted in policy debates as a key aspect in making more resilient economies. Additionally, this chapter finds that entrepreneurship plays a principal role in promoting the diversification and capacity building of global economies which are characteristic of resilient economies. Prior to the assessment of the relationship between entrepreneurship concepts and economic resilience, there is a need for determining the effectiveness of the Entrepreneurship Viability Coefficient and Entrepreneurial Capability Index. After considering the entrepreneurial activities as a driving force of the economic status in countries, by examining the relationship between the Entrepreneurship Viability Index and the Entrepreneurial Capability Index, the effectiveness of entrepreneurship on the growth of the society’s economy is studied. Considering the results, verified until here, if the reliable entrepreneurial activities can affect economies for a long period of time, therefore, the Entrepreneurial Capability Index (ECI) can significantly change the entrepreneurship viability. In order to estimate the regression-based models between the Entrepreneurial Capability Index (as the independent variable) and Entrepreneurship Viability Coefficient (as the dependent variable), a set of linear and nonlinear regression models is presented. See Table 5.3. As shown in this table, the Entrepreneurial Capability Index can be considered as a variable that affects the Entrepreneurship Viability Coefficient (as the dependent variable). According to this table, all linear and nonlinear models are significant at the level of 95%. Because of the high value of the coefficient of determination in the second-order model (quadratic), we choose the quadratic model as the best-fitted model for the relationship between these two variables.

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Table 5.3 Regression-based models for the Entrepreneurial Capability Index (ECI) and the Entrepreneurship Viability Coefficient (EVC)

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .599 82.289 .578 75.274 .547 66.393 .625 42.836 .613 42.768 .600 82.593 .595 80.743 .579 75.756 .600 82.593 .600 82.593 .600 82.593

Parameter estimates df1 1 1 1 2 2 1 1 1 1 1 1

df2 55 55 55 54 54 55 55 55 55 55 55

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Constant 8.086 67.271 28.031 7.859 3.183 1.669 .006 3.969 .512 1.669 .599

b1 .250 18.121 1261.350 .188 .000 1.024 1.734 122.436 .024 .024 .977

b2

b3

.003 .000

1.010E-5

Source: Authors’ own table

This estimated relationship demonstrates that entrepreneurial capability has a positive and severe effect on the growth of the Entrepreneurship Viability Coefficient. The determination coefficient of the quadratic model is 62.50%. This means that the Entrepreneurial Capability Index is able to predict the fluctuations of the Entrepreneurship Viability Coefficient up to 62.50%. Meanwhile, we discussed in the foregoing chapters that the longer lifetime of business in society will result in higher entrepreneurship reliability. In this regard, it can be said that reliable entrepreneurial activities start to happen when entrepreneurs give much energy to gain the abilities to business startups and then venture into business for the purpose of income increases or more worth objectives. Clearly, with the increase in the viability (durability) of a business, the hazard rate (hazard function) also diminishes. In addition, by reducing the hazard rate of entrepreneurial activities, due to its reverse relationship with reliability function, the level of reliability of entrepreneurship will increase. Thus, with the growth of entrepreneurial abilities in a society, it can be said that the entrepreneurship reliability of that society will be greatly enhanced and the economic resiliency of that community will be increased in its wake. The dispersion diagram of these two variables (the Entrepreneurial Capability Index and the Entrepreneurship Viability Coefficient), along with the estimated relationship function, is presented in Fig. 5.1. The equation of this curve is estimated as follows: EVC ¼ 7:86  0:19  x þ 2:88E  3  x2  error

ð5:2Þ

in which x denotes the Entrepreneurial Capability Index (ECI). One of the controversial topics in entrepreneurship science is the reasons why some entrepreneurs leave their businesses and why is the rate of exit from business

5.2 Entrepreneurship and Economic Resilience

141

Fig. 5.1 Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Entrepreneurship Viability Coefficient (EVC). Source: Authors’ own figure

different across countries. In other words, what is the main way to prevent the exit from businesses in different economic groups (including factor-driven, efficiencydriven, and innovation-driven)? Finding the response of this problem has forced policymakers to take advantage of trial-and-error methods or some scientific theories that are not easy to implement, specifically about a large quantity of population. In the next step, we will show that an increase in the Entrepreneurship Viability Coefficient, arising out of entrepreneurial abilities, will reduce the rate of exit from business. This relationship will be examined only for verifying that entrepreneurial abilities are the basis of successful businesses. By using the equation of Entrepreneurship Viability Coefficient, it is visible that this index will increase if the rate of exit from business decreases. More precisely, with the growth of knowledge, capabilities, skills, risk-taking, motivation, and opportunism among people in a community, policymakers can safely talk about the methods effective in lowering the rate of exit from business. Since in factor-driven economies most of the businesses are set up in necessity way (on the mandatory basis) and furthermore the motivation of individuals in underdeveloped societies for initiation of businesses is not suitable enough, so it

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Table 5.4 Regression-based models for the Entrepreneurship Viability Coefficient (EVC) and the rate of exit from business Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Model summary R square F .393 13.869 .413 15.694 .391 13.662 .416 7.871 .418 5.191 .432 17.566 .567 17.986 .400 14.461 .432 17.566 .432 17.566 .432 17.566

df1 1 1 1 2 3 1 1 1 1 1 1

df2 58 58 58 57 56 58 58 58 58 58 58

Sig. .000 .000 .000 .001 .003 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 5.295 .219 8.412 2.394 .917 18.377 6.885 .521 7.608 .727 5.070 .933 2.736 1.874 .282 5.429 1.623 .069 5.070 .069 .197 1.072

b2

b3

.012 .029

.000

Source: Authors’ own table

seems the effect of EVC on the rate of exit from business in such economies is more than developed countries. Hence, for the most part, the businesses launched in factor-driven economies will not resist the changes in environmental factors (including financial support, government programs, government policies, etc.), and they will go out of the competition when facing even least environmental shocks. For this reason, in these countries (factor-driven economies) due to the low incentive of entrepreneurs, their businesses not only do not increase the quality of human societies (like Human Development Index) but also reduce their quality of life (for more details read the previous chapters) and consequently, maybe, lead to the low economic resilience. We reaffirm that by increasing the amount of the Entrepreneurial Capability Index and the Entrepreneurship Viability Coefficient in a society, a drop in the rate of exit from business would be quite logical and natural, but in this section, we will try to set a series of linear and nonlinear to estimate the equation of this relationship. All fitted models are given in Table 5.4. As the previous method of model selection, we have chosen the best model based on the highest determination coefficient. Entrepreneurship Viability Coefficient is considered as the independent variable, and the rate of exit from business is deemed as the independent factor. As shown, all linear and nonlinear models are acceptable for fitting the relationship between these two variables. Because of the high value of the determination coefficient in the power model (about 57%), we consider this model as the best-fitted model of this relationship. This relationship shows that with the increase in the coefficient of entrepreneurship viability, the rate of exit from business sharply decreases. If the amount of entrepreneurial capability (that is in a positive and robust relationship with the Entrepreneurship Viability Index) in a society is high, then the likelihood of the exited businesses in this society will become low. Generally, with regard to this fact

5.2 Entrepreneurship and Economic Resilience

143

that entrepreneurial abilities increase the entrepreneurship reliability and also reduce the risk rate of entrepreneurial activities in society, therefore the growth of entrepreneurship reliability in such societies (societies with the high entrepreneurial capability) will reduce the entrepreneurship hazard rate and ultimately will reduce the rate of exit from business. In short, if the coefficient of entrepreneurship viability in a country is high, it shows that the rate of effective entrepreneurial activity will never decline down to zero. In other words, high entrepreneurial capability conducts the country to resist inconvenient economic conditions (e.g., economic shocks, political shocks, etc.). This country also can be dragged out of the severe economic, political, and other crises with the use of effective business in the economy. In addition, this community can retrieve its health and provide safe economic conditions for its people again. The dispersion plot and the estimated curve of the relationship between the Entrepreneurship Viability Coefficient and the rate of exit from business are as follows: The regression model estimated for the relationship of the foregoing variables is as follows: LnðRate of Exit from BusinessÞ ¼ 2:736  1:874  LnðEVCÞ  error

ð5:3Þ

We rewrite this power model in the “allometric” form as below: Rate of Exit from Business ¼ e2:736  ðEVCÞ1:874  error

ð5:4Þ

in which EVC denotes the Entrepreneurship Viability Coefficient. Note that: lim e2:736  ðEVCÞ1:874  0

EVC!100

That means the EVI significantly affects the economic cycle of a country so that by increasing the EVI, the rate of exit from business leans toward zero. This equation reflects the fact that by increasing the level of entrepreneurial skills and competencies (in general, the individual factors), as well as by increasing the amount of entrepreneurial motivation (which is a time-dependent and highly complex process), the amount of Entrepreneurship Viability Index and the Entrepreneurship Viability Coefficient will grow sharply. On the other hand, with the growth of the entrepreneurship viability (according to Fig. 5.2), the rate of exit from business is also highly reduced so that by increasing the coefficient of entrepreneurship viability, the rate of exit from business is reduced to zero. As business growth is taken as an economic development stimulus, so if the rate of exit from business diminishes, then this result will also help to increase the entrepreneurial activity rate and also will be advantageous for keeping entrepreneurship status stable. Doing so (preventing the businesses from exiting) will allow the

144

5 Entrepreneurship Viability Coefficient

Fig. 5.2 Scatter plot of the Entrepreneurship Viability Coefficient vs. the rate of exited businesses. Source: Authors’ own figure

economic system to survive a long period of time and resist against severe economic crisis and dangerous shocks that threaten the life quality of people. In general, economic resilience will also strengthen by increasing the Entrepreneurship Viability Coefficient (EVC) that results from the individuals’ entrepreneurial abilities. Since in underdeveloped countries the amount of entrepreneurial capability is very low (according to Fig. 5.2), thus economic shocks in such countries will increase the rate of exit from business, and only a very small percentage of effective businesses will be survived. In different circumstances, at the time of economic crisis, underdeveloped (factordriven) economies will no longer be able to improve the economic health of their own people and will not be able to return to the former situation. The level of financial/economic security in underdeveloped countries (due to the low entrepreneurial capability and low Entrepreneurship Viability Coefficient) will be sharply reduced in economic critical situations. In order to estimate a logical relationship between the Entrepreneurial Capability Index with the Economic Resilience Index, 11 linear and nonlinear regression models are presented in Table 5.5.

5.3 Evaluation and Conclusion

145

Although all regression models are meaningful at the 95% confidence level, but the model which has the highest determination coefficient will be selected as the best-fitted model. The second-order (quadratic) model is best suited to the relationship between the Entrepreneurial Capability Index (as the independent variable) and Economic Resilience Index (as the dependent variable). The R-square scale of this model is more than 55%. This means that the Entrepreneurial Capability Index can predict more than 55% of the variations of the Economic Resilience Index. This equation reflects the fact that to increase economic resilience, raising the entrepreneurial capability of a society can enable people to resist the economic crises which doing so will empower the people to bring society back to sustainable conditions (and the former health of life). The dispersion chart between these two variables (Entrepreneurial Capability Index and Economic Resilience Index) and the estimated curve for this communication has been drawn. See Fig. 5.3. The value of the coefficient of determination reaffirms the goodness of fit. This proves that the Entrepreneurial Capability Index is able to play a key role in studies about economic resilience. Hence, in order to exit from the economic recession, policymakers can be able to study the entrepreneurial capability and its sub-dimensions in a country instead of using complex and complicated methods that are along with difficulties and are often costly. The increase in entrepreneurial capability alone can prevent the occurrence of such economic recessions. Likewise, with reference to the results given by this study, if economic shocks creep into an economic system, increasing the level of entrepreneurial abilities can restore that country to the previous status. The estimated equation for the relationship between these two variables is as follows: Economic Resilience Index ¼ 12:4  0:77  ðECIÞ þ 0:018  ðECIÞ2  error

5.3

ð5:5Þ

Evaluation and Conclusion

What we mentioned in this chapter was the study of the relationship between the Entrepreneurial Capability Index (ECI) and the Entrepreneurship Viability Index (EVI) at the country level. To this end, it was important for us to calculate the rate of entrepreneurial activities that have a direct positive impact on the economy and also are surviving for a long period of time.

Model summary R square .540 .524 .500 .551 .550 .517 .510 .496 .517 .517 .517

Source: Authors’ own table

Equation Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

F 61.113 57.201 52.070 31.326 31.453 55.727 54.163 51.136 55.727 55.727 55.727

df1 1 1 1 2 2 1 1 1 1 1 1

df2 52 52 52 51 51 52 52 52 52 52 52

Sig. .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000

Parameter estimates Constant b1 27.611 1.168 305.349 84.952 141.967 5947.468 12.384 .773 22.615 .132 12.377 1.020 .099 1.473 5.457 104.034 2.516 .020 12.377 .020 .081 .980

Table 5.5 Regression-based models for the Entrepreneurial Capability Index (ECI) and the Economic Resilience Index b3

5.888E-5

b2

.0174 .000

146 5 Entrepreneurship Viability Coefficient

5.3 Evaluation and Conclusion

147

Fig. 5.3 Scatter plot of the Entrepreneurial Capability Index (ECI) vs. the Economic Resilience Index. Source: Authors’ own figure

As a result, we tried to examine the subscription area (joint area) between entrepreneurial activities that have a long life span with the rate of entrepreneurs whose goal was to startup businesses with a high-level motivation. Hence, we created a new index called the Entrepreneurship Viability Coefficient. In the next steps, we examined the relationship between the EVC and the rate of exit from business, as well as with the entrepreneurial capability with economic resilience. In line with the improvement of economic resilience, it can be said that supporting businesses to resist failure (scrambling to prevent businesses from exiting) is one of the most important approaches that can strengthen the country’s economy. To this end, we concluded that increasing entrepreneurial capability, as well as the growth of Entrepreneurship Viability Coefficient, can reduce the rate of exit from business. Furthermore, we directly evaluated the relationship between the Entrepreneurial Capability Index and the Economic Resilience Index, whereby we found out that by increasing the Entrepreneurial Capability Index, the amount of economic resiliency can be improved in the community.

Chapter 6

Research Results

Throughout the five chapters of this book, three entrepreneurship-based concepts were presented using statistics and probability theories. What we would be addressing in the present chapter is the presentation of a summary of all highlight findings that may be important to policymakers, researchers, and educators. Some of the ultimate findings in this book result from simple and elementary ideas which can be the first step of great researches in the future. Meantime, other computed sub-indexes and analyzed issues may be subject to extend in the upcoming studies. Additionally, because of multi-conceptual functions applied in this book, the interpretation of the created indexes may comprise several certain entrepreneurial concepts that are not mentioned in this book. Hence, the researchers are expected to develop the area of application of these new indexes in their future researches. Every concept evaluated in this book is along with the mathematical and statistical concepts that are integrated with the definitions of entrepreneurship (gained from entrepreneurship terminology), and, hence, the naming and definitions of the new structures/indexes are almost unchangeable but developable. Besides, these developed concepts may be interesting from the viewpoint of researchers who work on the entrepreneurship field. Thus, for improving the next version of this book, the authors of this book petition the readers to help and inform them about the useful findings which may have been ignored by writers.

6.1

Objectives of the Book

In brief, this section will discuss the objectives of indexes created in this book and will also discuss them mainly in relation to the field of usage of these indicators. The cases that researchers are always concern about them are related to the contribution of results and findings of their study to science (herein management © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1_6

149

150

6 Research Results

science), as well as other sectors including the public sector, economic sector, that we will discuss in detail. What is important from the reader’s viewpoint is: 1. How do the indexes generated in this book compete with the other global indexes? 2. Moreover, who are the users of these indexes? 3. In general, can policymakers, educators, researchers, and economists apply these indexes in their own areas of activity? The present chapter has been compiled in order to answer these questions. In addition, depending on the readers of this book whether or not they are familiar with the application of findings, every scientific finding of this book naturally might apply to the research of economics, culture, health, sports, and so on. Finally, at the end of this chapter, we will discuss the shortfalls and shortcomings that we encountered in writing this book. On the whole, the following items will be addressed: 1. 2. 3. 4. 5. 6.

Reviewing the objectives entire book Reviewing the findings and achievements Making attempt for evaluating the outcomes Discussing the outcomes Focusing on the application of the outcomes Appraisal of shortages and shortcomings

6.2

Overall Assessment

The Global Entrepreneurship Monitor is the world’s foremost entrepreneurship-based research consortium whose main mission is the investigation into entrepreneurship indicators worldwide. The Adult Population Survey is one of this institution’s essential project that is being enforced with many countries participating in this scientific survey every year. Although this consortium is in searching of the best methods to gather the most reliable data and notwithstanding the fact that great deal researches on the status of entrepreneurship are following out based on the data collected in this institution, the lack of existence of a comprehensive index measuring the status of entrepreneurship is visible yet. Hence, we compiled this book mainly to compute some indices that may rank countries from different perspectives in terms of entrepreneurship concepts. Many factors affect the state of entrepreneurship, and so it seems that the classification of the factors affecting the entrepreneurial situation into two parts, the individual factors, and environmental factors, completely is a logical classification. These factors, in addition to conduct entrepreneurs to launch a business in different countries, will help policymakers and state-run entities to make plans for the future of the business sector of the country. In this regard, in order to the simple

6.3 Findings and Recommendations

151

use of both environmental and individual factors in the assessment of the issue under study, we decided to produce the Entrepreneurial Capability Index (ECI) and the Entrepreneurship Viability Index (EVI) that depend on individual and environmental factors. In the process of the structure of these two indexes, the standardization of the Entrepreneurship Viability Index in the fifth chapter of this book has been fulfilled that after its computation we named it Entrepreneurship Viability Coefficient (EVC). In sum, these three indexes are capable of predicting changes of key global indexes such as the Human Development Index (HDI), the Gross Domestic Product (GDP) per capita, and so on. Rational and powerful relationships estimated by the regression-based models between the new three indexes and global indexes show the accuracy of the indexes computed in this book. The next subsections will pay in-depth attention to the application of indices given by previous chapters.

6.3

Findings and Recommendations

In this section, we aim to describe three indexes thoroughly. Main outcomes of this study are as follows: 1. Entrepreneurship Viability Index (EVI) 2. Entrepreneurial Capability Index (ECI) 3. Entrepreneurship Viability Coefficient (EVC)

6.3.1

Meaning of the Indices

6.3.1.1

Entrepreneurship Viability Index

Analysis of entrepreneurship life span is one of the approaches that will, in turn, lead to accurate estimations of the reliability of entrepreneurial activities in a community. Hence, studying the data on the length of life of entrepreneurial activities will not only provide precise predictions of the entrepreneurship status in the future but also, from the viewpoint of policymakers, will able to reveal the status of the economy of society. Since the questionnaire of the Adult Population Survey (APS) in the Global Entrepreneurship Monitor (GEM) does not include the questions about the lifetime of businesses, so using a method that is discussed in the methodology section, we could produce an indicator called the Entrepreneurship Viability Index (EVI). This index is estimating the remaining lifetime of the entrepreneurial activities at the country level. Moreover, this index is computed as a fraction of total entrepreneurial activities divided by the rate of exit from the business.

152

6 Research Results

Regardless of the restrictive assumption of calculation of the remaining lifetime of businesses, we replaced this new index with the lifetime of businesses across GEM member countries. In other words, this index is based on the rate of entrepreneurial activity [either nascent (SU), baby (BB) or established (EB)] and the rate of exit from business. Based on this index, the quantity (number) of businesses in this index is more important than their quality. The values of this index indicate the remaining lifetime of the business. It is important to note that, in the calculation of this index, the efficiency of entrepreneurial activities has not been considered. Namely, the quality of businesses has not taken into account and only their lifetime has been calculated. Therefore, the appearance of some factor-driven countries as the top countries in the index of the entrepreneurship viability is justifiable and natural. The application of this index will be subsequently discussed in detail.

6.3.1.2

Entrepreneurial Capability Index

Measuring the attitude as regards the entrepreneurship concept that is rooted within individuals’ perceptions is one of the most important issues that will lead to valuable results in this field of entrepreneurship. Entrepreneurial activities are being done through a variety of approaches. Perceived opportunity, high incomes, financial independence, and mandatory decisions are the reasons that push or pull an individual to become an entrepreneur. According to the study which has taken place in this research, we found that entrepreneurial activities in the underdeveloped countries are much larger than the developed countries, whereas some life quality indicators such as the life expectancy, Human Development Index, and GDP show that economic condition and life–health status in these countries are much lower than of developed countries. The question that arises here is it is evident in the factor-driven countries that the level of entrepreneurial activity is high, so, why do not these countries develop? As a key response, we found that many entrepreneurial activities in factor-driven countries not only have no positive effect on the economic cycle but also reduce the quality and health of life. Therefore, in this research, we decided to introduce an index that can measure the ability and capacity of the population to startup efficient businesses that have a positive effect on the economic cycle at the country level. To calculate this index, the interactions of individual factors (which represent the attitudes and abilities of individuals), environmental factors (which indicate the effect of the environment in the attitude of individuals), as well as the motivation index (which indicates the severity of communication between activities and the purpose of individuals) were applied. The interaction resulted from individual factor, environmental factor, and motivation index will be associated with a positive result in entrepreneurship knowledge. We named this new variable “Entrepreneurial Capability Index (ECI).”

6.3 Findings and Recommendations

153

In short, in light of the constitutive sub-dimensions of this index, the Entrepreneurial Capability Index can measure the ability of a community to startup businesses. We compiled this indicator using the Global Entrepreneurship Monitor dataset of the year 2015. Finally, among 60 countries, Luxembourg, Switzerland, Norway, Sweden, and the United States were ranked as one to five positions in terms of this index.

6.3.1.3

Entrepreneurship Viability Coefficient

Obtaining accurate information about the group of businesses that drive the economy of the country to growth will not only lead to the study on their characteristics but will also unfold the shortcomings of the country’s entrepreneurship and will receive the support of investors and policymakers. Hence, generating an indicator that can calculate the percentage of entrepreneurial activities that have a direct impact on economic development, in addition to identifying the type of efficient businesses, can also create a road map to prevent the growth of inactive businesses. We know that the businesses which have a positive and high impact on the economic cycle, and also last for a long time, are very important from the viewpoint of the statesmen and policymakers. In order to calculate an index for such a group of businesses, the third index presents the rate of entrepreneurial activities that have a positive effect on the economy and run for a long period of time. This index has been calculated by the interplay (interaction) between the Entrepreneurship Viability Index and the Entrepreneurial Motivation Index (see Table 5.2). This table defines four areas based on the “Entrepreneurship Viability Index” and “Entrepreneurial Motivation Index.” The most valuable part of this table pertains to the businesses that have a high impact on the economic cycle and also run for a long period of time (yellow area). We call the rate gained from this area the “Entrepreneurship Viability Coefficient.” Therefore, the Entrepreneurship Viability Coefficient (EVC) is the joining area between effective entrepreneurial activities and the durable entrepreneurship which the researchers and policymakers are looking for. As a result, the Entrepreneurship Viability Coefficient is a measure that represents high quality and viable entrepreneurs across the participated countries. With the use of the GEM’s dataset of the year 2015, this index was calculated, and finally, we saw that Switzerland, Thailand, Norway, Netherlands, and the United States had the highest coefficient of entrepreneurship viability. This index can also be called, so arbitrarily, the “Viable Entrepreneurship Index.”

154

6.4

6 Research Results

Application of the Present Book

In this subsection, we are going to continue the assessment of the findings by responding to the questions below:

6.4.1

How Much These Indexes Are Competitive to Others on the Market of Informational Products?

In order to study the very flexible and resilient economies, the Entrepreneurship Viability Coefficient will allow scholars to identify such countries. Additionally, for operating out the future plans by policymakers or for a careful investigation into a stable and resilient economy, the Entrepreneurial Capability Index and Entrepreneurship Viability Index will benefit policymakers and researchers. Generally, these indexes summarize full information including the interaction of individual factors, environmental factors, motivation, entrepreneurial activities, and the rate of exited business that totally comprising more than 12 sub-dimensions. Therefore, these indexes carry a great amount of entrepreneurship-driven concepts and information.

6.4.2

Who Are Potential Users?

Policymakers as the User Making a resilient economy is the ultimate object of those who act as policymakers and statesmen in a country. Additionally, the maintenance of the economic resiliency of a country in a stable situation is the always duty of these people. Identifying the ways to gain an appropriate level of economic resilience requires an in-depth study and understanding of entrepreneurship concepts about individual or environmental factors. To this end, the fifth chapter of this book has outlined the procedure of efficiency of the created indexes on economic resilience. Since the existence of a strong relationship between the Entrepreneurial Capability Index and the Economic Resilience Index is one of the important findings in this research, so, with the awareness of the amount of this index in each country, the economic resiliency of the country would be predictable. In other words, the Entrepreneurship Viability Coefficient allows policymakers to be aware of the amount of economic resistance when meets economic shocks. Additionally, policymakers can make decisions for improving economic resilience with the use of studying changes in the Entrepreneurship Viability Coefficient which depends on the Entrepreneurial Capability. Note that improvement of education methods, fostering entrepreneurial culture, attention to entrepreneurial ideas,

6.4 Application of the Present Book

155

providing open markets, and so on are important in the improvement of efficiency and lifetime of businesses. Educators as the Users Individual factors in the field of entrepreneurship include skills, abilities, risk-taking, perceiving opportunity, and knowing role models. On the other hand, the individual factors have a direct effect on the Entrepreneurial Capability Index, and also the Entrepreneurial Capability Index influences the Entrepreneurship Viability Coefficient. Thereby, knowing these three indexes (especially the Entrepreneurship Viability Coefficient) allows educators to educate others about entrepreneurial abilities and capabilities, and ultimately doing so will strengthen the entrepreneurship culture and will develop the social norms, and finally, proper enforcement of entrepreneurship education can increase economic resilience. Researchers as the Users One of the issues that have confused many researchers is the lack of development of countries in which the high level of entrepreneurial activities is obvious. In this research, we were able to discover that a great percentage of the entrepreneurial activities in most of the countries (especially in the factordriven economies) are not efficient enough and will fade with the least economic and political shocks. The “Entrepreneurship Viability Coefficient” will give a great opportunity for researchers to assess the country status in the term of efficient businesses that influence the economy. Generally, because a part of these indices (especially ECI) includes environmental factors, so, awareness of the amount of these indexes will present information about the situation of entrepreneurship infrastructures that are useful for researchers and policymakers, too.

6.4.3

What Is the Value-Adding of the Indexes for Policymakers?

As mentioned in Chap. 5, the Entrepreneurial Capability Index can predict about 56% of the fluctuations in economic resilience. In other words, by controlling the entrepreneurial capability in the country, policymakers and statesmen can keep economic shocks away from the economic system. Namely, by making a more flexible economy, resulting from high entrepreneurship culture, the economic system will be empowered to withstand the economic shocks and crisis. More importantly, with the precise planning by policymakers on entrepreneurial skills, the perception of opportunity and risk-taking and the motivation of the people and the entrepreneurial activities can significantly control the variations of the country’s economic resilience by more than 55%. By studying the sub-dimensions of the Entrepreneurial Capability Index and the Entrepreneurship Viability Index, policymakers, with a confidence level of 55%, will be able to prevent the economic crisis from happening.

156

6.4.4

6 Research Results

What Are Theoretical and Methodological Pitfalls and Shortcomings?

In this study, shortfall of some questions compelled us to determine an alternative variable for the lifetime of businesses with challenging methods. Hence, the theoretical methods that we took them into account although were based on statistic and probability theories but naturally have exposed our models with a little amount of error. To avoid such challenging methods and error values, we suggested some additional variables which will help to collect lifetime data. These variables have been defined in the form of questions (see Table B.1). Undoubtedly, the existence of accurate information on the date of start and end of businesses will allow researchers to conduct a comprehensive and accurate study on the lifetime of the entrepreneurial activities at the country level. But the lack of some variables containing such information deprives us of this essential research. Some of these questions that have presented in Table 3.9.

Appendices

Appendix A: Model Summary and Parameter Estimations: All Eleven Regression Models for Estimating the Relationship Between the Human Development Index (HDI), as a Dependent Variable, and the Pre-index of Entrepreneurial Capability (as an Independent Variable)

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2020 N. Faghih et al., Entrepreneurship Viability Index, Contributions to Management Science, https://doi.org/10.1007/978-3-030-54644-1

157

158

Appendices

Table A.1 Model summary and parameter estimates: all eleven regression models for estimating the relationship between the Human Development Index (HDI), as a dependent variable, and the pre-index of Entrepreneurial Capability (as an independent variable) Dependent variable: HDI Model summary

Parameter estimates

Country group GCR report—3 cat Equation

R square F

df1

df2

Sig.

Stage 1: Factor driven

.183

1.347

1

6

.290

Logarithmic .168

1.214

1

6

.313

.141

.440

Inverse

1.057

1

6

.343

1.023

2.252

Linear

.150

Constant .149

b1

b2

Quadratic

.209

.660

2

5

.557

1.192

.293

.033

Cubic

.203

.636

2

5

.568

.755

.081

.000

Compound

.178

1.297

1

6

.298

.274

1.150

Power

.161

1.147

1

6

.325

.170

.735

S

.140

.978

1

6

.361

.164

3.725

Growth

.140

.178

1.297

1

6

.298

1.296

Exponential .178

1.297

1

6

.298

.274

.140

Logistic

.178

1.297

1

6

.298

3.655

.870

Stage 2: Efficiency Linear .014 driven Logarithmic .010

.331

1

24

.571

.694

.012

.235

1

24

.632

.666

.056

Inverse

.006

.153

1

24

.699

.806

.244

Quadratic

.080

.998

2

23

.384

1.778

.385

.036

Cubic

.080

.994

2

23

.385

1.421

.188

.000

Compound

.015

.361

1

24

.554

.691

1.017

Power

.011

.259

1

24

.615

.663

.079

S

.007

.172

1

24

.682

.212

.346

Growth

.017

.015

.361

1

24

.554

.370

Exponential .015

.361

1

24

.554

.691

.017

Logistic

.015

.361

1

24

.554

1.448

.983

Stage 3: Innovation Linear .098 driven Logarithmic .100

2.172

1

20

.156

.805

.018

2.219

1

20

.152

.744

.094

Inverse

.101

2.259

1

20

.148

.994

.486

Quadratic

.103

1.095

2

19

.355

.561

.113

.009

Cubic

.103

1.095

2

19

.355

.561

.113

.009

Compound

.097

2.137

1

20

.159

.809

1.020

Power

.098

2.179

1

20

.155

.757

.105

S

.100

2.214

1

20

.152

.002

.538

Growth

.020

.097

2.137

1

20

.159

.211

Exponential .097

2.137

1

20

.159

.809

.020

Logistic

2.137

1

20

.159

1.235

.980

Source: Authors’ own table

.097

b3

.083

.002

.002

.000

Appendices

159

Fig. A.1 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the factor-driven economies. Source: Authors’ own figure

160

Appendices

Fig. A.2 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the efficiency-driven economies. Source: Authors’ own figure

Appendices

161

Fig. A.3 Scatter plot of pre-index of Entrepreneurial Capability vs. Human Development Index (HDI) into the innovation-driven economies. Source: Authors’ own figure

Appendix B: Regression-Based Models into Differ Economies for Entrepreneurial Capability Index (ECI) Versus GDP per Capita

162

Appendices

Table B.1 Model summary and parameter estimates: all eleven regression models for estimating the relationship between Gross Domestic Product (GDP), as a dependent variable, and the Entrepreneurial Capability Index (as an independent variable) Country group GCR report—3 cat Equation Stage 1: Factor has driven

Stage 2: Efficiency driven

Stage 3: Innovation driven

Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic Linear Logarithmic Inverse Quadratic Cubic Compound Power S Growth Exponential Logistic

Source: Authors’ own table

Model summary

Parameter estimates

R square F

df1 df2 Sig. Constant

b1

.244 .244 .245 .244 .244 .398 .405 .411 .398 .398 .398 .103 .104 .104 .105 .105 .118 .118 .117 .118 .118 .118 .497 .470 .443 .619 .639 .581 .559 .537 .581 .581 .581

1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1

588.089 37,012.373 2,308,558.326 768.791 1.427 768.791 1.427 .000 1.095 5.756 361.510 .091 .091 .913 164.241 11,170.796 741,403.290 459.233 2.144 318.746 .000 .011 1.012 .784 51.865 .012 .012 .988 786.451 60,827.134 4,613,950.666 6160.886 43.003 .000 38.144 .350 1.018 1.368 104.694 .018 .018 .983

2.256 2.263 2.266 .967 .967 4.634 4.759 4.879 4.634 4.634 4.634 2.770 2.799 2.793 1.347 1.348 3.207 3.216 3.181 3.207 3.207 3.207 19.762 17.750 15.930 15.436 16.833 27.712 25.360 23.159 27.712 27.712 27.712

7 7 7 6 6 7 7 7 7 7 7 24 24 24 23 23 24 24 24 24 24 24 20 20 20 19 19 20 20 20 20 20 20

.177 .176 .176 .432 .432 .068 .065 .063 .068 .068 .068 .109 .107 .108 .280 .280 .086 .086 .087 .086 .086 .086 .000 .000 .001 .000 .000 .000 .000 .000 .000 .000 .000

27,926.697 14,4052.826 46,107.373 33,593.881 33,593.881 21.122 2.915E 7 14.565 3.050 21.122 .047 4938.894 30,891.558 27,282.815 4966.449 1944.558 6892.869 558.306 10.407 8.838 6892.869 .000 22,367.728 225,073.975 100,011.008 250,203.792 96,832.188 9468.127 97.937 11.907 9.156 9468.127 .000

b2

b3

Appendices

163

Fig. B.1 Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the factor-driven economies. Source: Authors’ own figure

164

Appendices

Fig. B.2 Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the efficiency-driven economies. Source: Authors’ own figure

Appendices

165

Fig. B.3 Scatter plot of the Entrepreneurial Capability Index vs. Gross Domestic Product (GDP) per capita index in the innovation-driven economies. Source: Authors’ own figure

Appendix C: The Programming Codes for Weibull Distribution t