Drivers and Barriers of Women Entrepreneurs: An Analysis of the National and Regional Context Using GEM Data [1st ed.] 9783658315139, 9783658315146

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Forschungsreihe der FH Münster

Iulia Stroila

Drivers and Barriers of Women Entrepreneurs An Analysis of the National and Regional Context Using GEM Data

¨ nster Forschungsreihe der FH Mu

Die Fachhochschule Münster zeichnet jährlich hervorragende Abschlussarbeiten aus allen Fachbereichen der Hochschule aus. Unter dem Dach der vier Säulen Ingenieurwesen, Soziales, Gestaltung und Wirtschaft bietet die Fachhochschule Münster eine enorme Breite an fachspezifischen Arbeitsgebieten. Die in der Reihe publizierten Masterarbeiten bilden dabei die umfassende, thematische Vielfalt sowie die Expertise der Nachwuchswissenschaftler dieses Hochschulstandortes ab.

More information about this series at http://www.springer.com/series/13854

Iulia Stroila

Drivers and Barriers of Women Entrepreneurs An Analysis of the National and Regional Context Using GEM Data

Iulia Stroila Münster, Germany

ISSN 2570-3307 ISSN 2570-3315 (electronic) Forschungsreihe der FH Münster ISBN 978-3-658-31513-9 ISBN 978-3-658-31514-6 (eBook) https://doi.org/10.1007/978-3-658-31514-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 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. Responsible Editor: Marija Kojic This Springer Spektrum imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Preface

The number of female entrepreneurs is growing rapidly worldwide, but women are still less likely than men to show entrepreneurial intentions and start a new business. However, starting their own business could offer women significant career opportunities and could contribute to economic growth and greater prosperity in the family and community. Two factors influencing this are barriers (obstacles) and drivers (facilitators), but there is a lack of understanding of how their interaction affects women’s entrepreneurial intentions, especially in different contexts. It seems surprising that these findings - despite the available data material - are not yet available. The author has addressed this shortcoming. The book will investigate whether it is the barriers or (and/or) drivers that significantly influence women’s entrepreneurial intention and how the regional or national context influences this. The empirical analysis is based on the cross-national survey of the Global Entrepreneurship Monitor, which contains questions on the entrepreneurial attitude of the population. From the original data set, four “leading” countries within Europe provided a sample of 7,096 responses. Results show that there is a significant difference in the entrepreneurial barriers and drivers that influence women’s entrepreneurial intentions across European regions. Besides, different barriers and drivers were identified that significantly influence attitudes and intentions in the four countries. As barriers and drivers were able to explain well the entrepreneurial intention of women in Germany, Spain and Sweden, there was a more limited explanation of the entrepreneurial intention of Polish women. The work contributes to the literature on contextual entrepreneurship and to the understanding of factors that influence women’s entrepreneurial intentions. Furthermore, it shows that gender is a key aspect of starting

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a new business in all countries. Thus the societal relevance of the topic must be highlighted, as it has an impact, not only on the economic sphere but also on the social and individual level. The work contributes to the literature base of contextual entrepreneurship and to the understanding of the factors that influence women’s entrepreneurial intention. It also shows that gender is a key aspect in the creation of new businesses in all countries. Iulia Stroila was awarded the prize of the best Master Thesis of the year 2020 of Münster – University of Applied Sciences. In delight, we are presenting the work of Iulia Stroila and are more than happy to discuss the results. Prof. Dr. Thomas Baaken Senior-Professor Jun.-Prof. Dr. Sue Rossano Junior-Professor

Abstract

Starting a business of one’s own might provide women with important career opportunities and contributes significantly to economic growth, enhanced family and community well-being. The number of women entrepreneurs is growing rapidly worldwide, however, women are still less likely than men to exhibit entrepreneurial intentions and start a new business. Two factors influencing this are barriers (inhibitors) and drivers (motivators), however, the understanding of how their interaction affects women’s entrepreneurial intention, specifically across different contexts, is lacking. Accordingly, this research focused on two environmental settings, European regions and countries. It further seeks to understand whether it is the barriers or (and/or) drivers that most impact women’ entrepreneurial intention and how the regional or national context influences this. The empirical analysis is based on the Global Entrepreneurship Monitor 2015 nation-wide survey which contains questions about entrepreneurial perceptions of the countries’ population. From the original dataset, four “lead” countries within Europe provided a sample of 7,096 responses. The results show that there is a significant difference in the entrepreneurial barriers and drivers that affect women’s entrepreneurial intention in the European regions. Furthermore, different barriers and drivers were found to significantly affect the four lead countries. With barriers and drivers being able to provide a good explanation of women’s entrepreneurial intention in Germany, Spain, and Sweden, there was a more limited explanation of entrepreneurial intention by Polish women. The article contributes to the literature of contextual entrepreneurship and to the understanding of factors influencing women’s entrepreneurial intentions.

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Keywords Female Entrepreneurship, Context, Entrepreneurial Intentions, Global Entrepreneurship Monitor, Drivers and Barriers

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Importance of the Topic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Purpose and Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Study Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 4 5

2 The Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Definition of Entrepreneurship in This Research . . . . . . . . . . . . . . . 2.2 Falling into the Gender Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Entrepreneurial Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Context in Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Individual and Social Context Factors . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Perceived Barriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1.1 Fear of Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1.2 Lack of Self-Confidence . . . . . . . . . . . . . . . . . . . . . . 2.5.1.3 Lack of Entrepreneurial Opportunities . . . . . . . . . . 2.5.1.4 Unattractive Career Path . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Perceived Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2.1 Knowing a Nascent Entrepreneur . . . . . . . . . . . . . . 2.5.2.2 High Perceived Social Status . . . . . . . . . . . . . . . . . . 2.5.2.3 Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2.4 Perceived Ease of Starting a Business . . . . . . . . . . 2.6 Wider-Environment Context Factors . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conceptual Model and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 7 10 11 13 14 15 16 17 19 20 21 22 24 25 26 27 31

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3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Research Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Research Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Data Source, Population, and Sample . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Variable Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Quality of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33 33 35 36 38 41 44 46

4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Multicollinearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Adequacy and Fit of the Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Results of the Multiple Logistic Regression Analysis . . . . . . . . . . . 4.5 Summary of Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Theoretical and Practical Contributions . . . . . . . . . . . . . . . . . . . . . . . . . .

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8 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Figure 2.1 Figure 2.2

The Entrepreneurial Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual Model for Drivers and Barriers of Women Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 3.1 Table Table Table Table Table Table Table Table Table Table

3.2 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Barriers to Women’s Entrepreneurial Intention Assessed in Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Drivers to Women’s Entrepreneurial Intention Assessed in Previous Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison Between Countries . . . . . . . . . . . . . . . . . . . . . . . . . . GEM National Adult Population Surveys: Sample Size and Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Description of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive Statistics and Results of Kruskal-Wallis Test . . . . . Spearman Correlation Matrix & VIF Values Germany . . . . . . . Spearman Correlation Matrix & VIF Values Poland . . . . . . . . . Spearman Correlation Matrix & VIF Values Spain . . . . . . . . . . Spearman Correlation Matrix & VIF Values Sweden . . . . . . . . Model Fit Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of Multiple Logistic Regression . . . . . . . . . . . . . . . . . . . Results of Multiple Logistic Regression: Pooled Countries . . . Summary of Hypotheses: Results . . . . . . . . . . . . . . . . . . . . . . . .

16 22 29 40 43 51 54 55 56 57 59 61 64 65

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1

Introduction

This chapter of the paper proceeds with an introduction of the importance of studying gender in the context of entrepreneurship. Moreover, the gaps that exist in the current literature on women entrepreneurship are presented and the issues that will be addressed by this study are identified. This, in turn, will lead to the objectives and research questions of this study. Afterwards, the study structure is introduced.

1.1

Importance of the Topic

Latest research highlights that the number of women is growing rapidly at the global level; 163 million women were starting businesses across 74 countries, women’s entrepreneurial activity increased by ten per cent from the year 2016 to the year 2017, and the gender gap decreased by five per cent (Kelley, Baumer, Cole, Dean, & Heavlow, 2017). Starting a business of one’s own might provide women with important career opportunities: it contributes significantly to economic growth, enhanced family and community well-being, and societal gains (Kelley et al., 2017). At its best, entrepreneurial activity empowers women to reduce career obstacles such as corporate glass ceilings, direct and indirect discrimination in organisations, mid-career crisis, and age discrimination (Arenius & Kovalainen, 2006). Furthermore, becoming an entrepreneur might provide women with more opportunities to earn money, enhance their capabilities and knowledge, offer them personal independence and economic freedom, and improve their status in society (Haugh & Talwar, 2016). Thus, the contexts and meanings of entrepreneurial activities vary. Several scholars have empirically demonstrated that the flexibility offered by entrepreneurial activates enable women to balance their work © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_1

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Introduction

and family activates in a way that large corporations do not (Agarwal & Lenka, 2015; Poggesi, Mari, & De Vita, 2019). Even if the self-employment trend did appear to be upwards in the last decades, women are still less likely than men to exhibit entrepreneurial intentions and start new businesses and the male-female differential in entrepreneurship has continued to be unaltered overtime (Wu, Li, & Zhang, 2019). In this line, according to the Global Entrepreneurship Monitor (GEM) study for 2018, 21 economies of the 74 in the sample show low entrepreneurial activity rates and gender gaps persist in many regions of the world with levels that are half or less than half the level of men. Reasoning for this discrepancy is that entrepreneurship continues to be heavily male dominated (Brush, Bruin, & Welter, 2009; Dileo & Pereiro, 2018), and women’s participation in entrepreneurial endeavours remains hindered by a series of barriers and constraints (Naidu & Chand, 2017; Wu et al., 2019).

1.2

Problem Formulation

Entrepreneurial barriers faced by women have become a “hotly debated topic” in the recent decade (Naidu & Chand, 2017). However, studying and understanding women’s entrepreneurial activities is a difficult task since existing research on this topic is hindered by a series of gaps (Ahl, 2006; Wu et al., 2019). Women entrepreneurs are in most cases omitted in the statistics or in policy programmes that target entrepreneurship; unless they are designed to be gendersensitive, such as entrepreneurship education for women or loan schemes (Arenius & Kovalainen, 2006). Even official statistics may be inaccurate with regard to the number of women entrepreneurs since various sources present different numbers depending on the data gathering period and classification (Ahl, 2006). In many countries, entrepreneurs are not even divided according to gender. Research on women entrepreneurship has been predominantly conducted through small surveys or qualitative case studies that focus on a certain country or regional context, thus generating comprehensive information on micro-level, case-specific reasons, and country-specific facts. Relatively few scholars provide cross-country comparisons that study women entrepreneurs at the macro-level (e.g., Arenius & Kovalainen, 2006; Langowitz & Minniti, 2007; Shinnar, Giacomin, & Janssen, 2012; Naidu & Chand, 2017; Wu et al., 2019). Even though their findings provide extensive and relevant insights about women’ entrepreneurial activities, the researchers further emphasise the need for additional cross-country studies for an improved understanding of women entrepreneurship within a more diversified context.

1.2 Problem Formulation

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Prior research has often adopted conventional quantitative approaches which favour gender (male/female) comparisons (e.g. Langowitz & Minniti, 2007; Cetindamar, Gupta, Karadeniz, & Egrican, 2012; Shinnar et al., 2012). Research that holds a “gender as a variable” approach (Cromie, 1987) has often been criticised since comparisons between men and women have generated little additional light to explanations arising from societal and cultural characteristics (Ahl, 2006). Thus, the question remains whether the identified differences between men and women serve as universal characteristics or emerge from other aspects, such as the socio-economic position, education, or class of women and men (Arenius & Kovalainen, 2006). By focusing on alleged gender differences, prior research overlooks that gender is socially and culturally constructed (Henry, Foss, & Ahl, 2015), and further reinforces women’s subordination in the entrepreneurial landscape (Ahl, 2006; Henry et al., 2015). Consequently, scholars call for the need to investigate the differences within the group, instead of gender comparisons. In order to increase women’s entrepreneurial activity, which for many countries is the most notable means to foster overall entrepreneurial activity and women’s employment situation (Arenius & Kovalainen, 2006), scholars highlight the need to learn more about the factors that not only hinder but also support the participation of women in the formation of new businesses (Locke & Baum, 2007). Consequently, there are relatively little studies that combine both barriers as well as drivers for women entrepreneurship at the macro-level within comparable datasets. Prior research has focused on barriers to women entrepreneurship, and their studies are based on the common assumption that once barriers are overcome, the road is paved for more engagement in entrepreneurial activates (Davey, Baaken, Galán-Muros, & Meerman, 2012; Galán-Muros & Plewa, 2016). However, a point of critique is that focusing only on barriers is a “factual error” and influencing drivers, such as motivation and beliefs, can stimulate entrepreneurial activates to the point in which the impact of barriers to entrepreneurship lacks explanatory power (Davey et al., 2012). The impact of drivers is considered to be persuasive enough to compensate for barriers in entrepreneurial engagement. Thus, strong drivers have higher importance when engaging in entrepreneurship than strategies to overcome or remove barriers. Finally, additional research approaches that combine both factors that hinder and facilitate women’s participation in entrepreneurial endeavours could help enhance the understanding of women’s entrepreneurial intentions.

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Introduction

Purpose and Research Questions

To address the abovementioned gaps in the existent literature, this study intends to analyse the hindering and facilitating factors that may affect women’s intention to start a business. This study further focuses on entrepreneurial intention of women across four different European countries, by adopting both a national and supranational perspective. In this research, the supranational level refers to the European regions north, south, west, and east, while the national level to the “lead countries” Sweden, Spain, Germany, and Poland. Furthermore, this study does not attempt to confirm the differences or similarities between genders, nor to compare men and women, as such. It aims at investigating the differences within the group, which is affected by increasing pressures in labour markets, changing organisational processes, and policies. The arguments of this research are positioned at two different levels: the individual and social level, and widerenvironment level. Specifically, based on the existing literature, this study argues that women’ perception of drivers and barriers at the individual and social level may inhibit or support their intention to open a business and that entrepreneurship is context-dependent. To achieve the objectives outlined above, the following questions will be addressed: 1. What effect do perceived drivers and barriers at the individual and social level have on women’s intention to open a business? 2. Are there any differences in women’s perception of entrepreneurial drivers and barriers across countries? To answer the research questions, data from the GEM 2015 survey is used. The GEM data is based on a large national survey of the general adult population and has the advantage of giving possibilities to larger macro-level societal analysis. In this study, a large enough sample of women is used to facilitate the analysis and comparison across countries of a variety of factors that hinder or facilitate women’s entrepreneurial intention, at both individual and social level. It has to be noted that this research uses variables similar to those used in studies on men. However, no male image is imposed on variables and this study does not assume that the variables care “male-laden” images with them. Alternatively, this study aims at constructing a gendered theory of entrepreneurial intentions and shows how context operates differently as an entrepreneurial domain. This study makes the following contributions. First, is shows that gender represents a key aspect in entrepreneurship across countries. Second, the study

1.4 Study Structure

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contributes to entrepreneurship theory by adding a comparative aspect through the analysis of the differences and similarities across country data; thereby showing how context is more engaged in the entrepreneurial process. Third, this research is of use for policymakers. With entrepreneurship being one of the main pillars for public policies that promote economic development (OECD/European Union, 2017), this study proposes directions with regard to entrepreneurial support programs that include context and gender to improve outcomes.

1.4

Study Structure

The first chapter entitled “Introduction” provides an overview of women entrepreneurship, the existing gaps in the literature, the objectives of the study, the research questions, and it presents the structure of this research paper. The second chapter, “Literature Review”, presents relevant literature around the concept of entrepreneurship, gender, and context. Moreover, it presents the barriers and drivers that influence women’s entrepreneurial intention. This is followed by the conceptual model which is created on the basis of the examined literature. The chapter “Methodology” builds the third chapter where the research method chosen to conduct the empirical part is presented. In the “Results” chapter, the outcomes of the empirical study are presented. In the “Discussion” and “Theoretical and Practical Contributions” chapters, an explicit discussion based on the results is provided and theoretical and practical implications of this study are presented. The paper ends with overall conclusions and the limitations of the study are drawn which further lead to suggestions for future research.

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The Literature Review

Since research on gender and entrepreneurship is a complex landscape, this chapter provides an overview of existing research on the topic of woman entrepreneurship and it begins with a definition of this concept and determines its importance. Moreover, this paper argues that perceptions of women of the drivers and barriers to embarking on an entrepreneurial venture affect their intentions to open a business. Therefore, this chapter further aims to define women’s entrepreneurial barriers and drivers and synthesise them to establish broad classifications with regard to why women are hinged or sustained to engage in entrepreneurial activates. Afterwards, a conceptual model is developed which further serves as a foundation for the empirical analysis.

2.1

Definition of Entrepreneurship in This Research

Entrepreneurship has become the focus of much research in recent decades (Carlsson et al., 2013; Block, Fisch, & van Praag, 2016). The domain of entrepreneurship research is evolving rapidly in many fields of study—primary economics, politics, sociology, psychology, management, marketing, and finance as well as geography—depicting a variety of perspectives, methods, and beliefs. Even though entrepreneurship has been studied extensively in scientific literature, there is still no agreement on a coherent definition, nor there is unanimity about how the concept should be understood (Block et al., 2016). As a consequence, “entrepreneurship” has been a cause of controversy, with varying levels of acceptance and differing views on what actually constitutes an “entrepreneurial activity” (Iversen, Jørgensen, & Malchow-Møller, 2008).

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_2

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2 The Literature Review

Indeed, authors have used the term “entrepreneurship” in a broader way to represent the dynamic process of creating a new business (e.g. Gartner, 1985; Low & MacMillan, 1988). Schumpeter (1934) defines entrepreneurship as the innovation of new goods and services by identifying or discovering new combinations and acting on those that are profitable. While Kirzner (1973) appears to share the view that entrepreneurship is a contest of ideas, the researcher highlights that opportunities emerge only when information is revealed and, as a result, individuals change their behaviour and act differently when reacting to different opportunities. Furthermore, Shane and Venkataraman (2000) stress that “entrepreneurship involves the nexus of two phenomena: the presence of lucrative opportunities and the presence of enterprising individuals”. Their theory is inspired by the Kirznerian entrepreneurial discovery process; however, the researchers emphasise that prior information is needed to complement the new information in the discovery of business opportunities. In this respect, they further argue that human capital is an important determinant of entrepreneurial ability. Casson (2003) tries to encompass both the Schumpeterian and the Kirznerian definitions by arguing that entrepreneurs are individuals who specialise in making judgemental decisions, under uncertainty, with regard to the coordination of scarce resources. The majority of definitions tend to concentrate on the pursuit of opportunity (Kirzner, 1979; Stevenson, Roberts, & Grousbeck, 1989; Shane & Venkataraman, 2000). Often mentioned in the literature is the definition proposed by Stevenson et al. (1989). The researchers define entrepreneurship as a process through which individuals—either on their own or inside organisations—pursue opportunities without regard to the resources they currently control. Similarly, Shane and Venkataraman (2000) stress that entrepreneurship involves the study of sources of opportunity, the process of opportunity discovery, evaluation, and exploitation, and the individuals who are involved in the process. While Zahra and Dess (2001) agree with the definition proposed by Shane and Venkataraman (2000), the researchers further complement it with the study of the outcomes of entrepreneurial behaviour which involves the creation of wealth. However, they further highlight that the outcomes do not necessarily represent only the economic payoff but as well an investment in the human, social, and intellectual capital of entrepreneurs (Zahra & Dess, 2001). The selection of a definition for entrepreneurship in this research has been dependent upon the objectives of this thesis, a strategy that is often adopted and recommended in the entrepreneurship literature (Gartner, 1990; Hébert & Link, 1989). Therefore, it was decided to define entrepreneurship as a process of opportunity pursuit, thus “entrepreneurship” is defined as “the process of recognising and pursuing opportunities for the purpose of generating economic growth and

2.1 Definition of Entrepreneurship in This Research

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human welfare (Eckhardt & Shane, 2003, p. 336) regardless of the location of the resources currently controlled” (Stevenson & Jarillo, 2007, p. 164). This definition highlights that the essence of entrepreneurship is the willingness to pursue an opportunity, regardless of the availability of resources. Hence, for the sake of clarity, this paper follows the process of entrepreneurship proposed by van der Veen and Wakkee (2006). As shown in Figure 2.1, the entrepreneur is the driver of the process. At the same time, the socioeconomic environment in which the entrepreneur is embedded is of critical importance to his or her success to generate value.

Entrepreneur Opportunity recognition

Opportunity exploitation

Value creation

Environment

Figure 2.1 The Entrepreneurial Process. (Source: Adapted from van der Veen & Wakkee (2006))

Despite the lack of a general definition in scientific literature, there is broad consensus, both in academic and practice fields, that entrepreneurship is crucial to economic growth (Olaison & Meier Sørensen, 2014; Doran, McCarthy, & O’Connor, 2018). Already, Holcombe (1998), who stated that entrepreneurship is “the engine of economic growth”, and Anokhin, Grichnik, and Hisrich (2008), who argued that entrepreneurship is “the main vehicle of economic development”, recognised the significant contributions of entrepreneurship to society. According to Audretsch and Keilbach (2004), entrepreneurship is a mechanism for knowledge spill-overs, increase competition, and diversity among firms. Further mechanisms encompass employment generation, introduction of innovations, and productivity improvement (Van Praag & Versloot, 2007). Although Fritsch (2007) agrees on these mechanisms, the researcher adds that entrepreneurship can lead to greater availability of goods and problem-solving methods and can accelerate the pace of creative destruction (Schumpeter, 1934), whereby new firms substitute old-established incumbents. These potential contributions to society have stimulated not only academic but also political interest in the matter, driving actors in the political arena to develop policies that support entrepreneurship (Block et al., 2016). Recently, national funding agencies have spent significant

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2 The Literature Review

amounts of money to support the growth of enterprises (European Commission, 2012). In Europe, higher investment volumes were realised in all major markets in 2018 than in the previous year, with the UK being on the first position followed by Germany (Lennartz, 2019). For instance, in Germany the total value of these investments rose by a good seven per cent, from e 316 million in 2017 to around e 4.6 billion in 2018, exceeding the previous year’s record level (Lennartz, 2019).

2.2

Falling into the Gender Gap

Several scholars have empirically tested and validated the gender differences around entrepreneurship (Barrett & Moores, 2009; Lockyer & George, 2012). Research on gender and entrepreneurship is a complex landscape, with some characterisations being contingent depending on the business development stage and others contingent on a different definition of “gender” (Lockyer & George, 2012). In this regard, gender in this study is observed from a feminist perspective that refers mainly to “a system of values that challenges male dominance and advocates social, political, and economic equity of women and men in society” (Riger, 1992, p. 731). What emphasises the feminism epistemology are the acknowledgement of the social subordination of women and the desire to end this situation (Ahl, 2006). Such a perspective considers that the constructs “masculine” and “feminine” determines what men and women are supposed to be, thus, they influence individual behaviours and highlight that the life experiences of women are different to the ones of men (Giménez & Calabrò, 2017). Furthermore, the literature is built on the assumption that men and women are essentially different (Ahl 2006; Henry et al., 2015), and their characteristics are embedded in the expectations of society, national culture, and institutions (Gildemeister, 2010). The subject of gender in entrepreneurship has received considerable attention through a broad spectrum of surveys and qualitative studies (Link & Strong, 2016), indicating that while gender can influence entrepreneurial behaviour, the ways in which it plays out can manifest through different entrepreneurial identities (Stead, 2017) and differs within socio-economic contexts (Lewellyn & Muller-Kahle, 2016). The number of women entrepreneurs is growing rapidly worldwide, however, women are still less likely than men to exhibit entrepreneurial intentions and start a new business (Wu et al., 2019). Reasoning for this discrepancy and low female participation is that women’s fear of failure is higher than of men’s, they are more likely to not see good business opportunities, and they doubt to have the necessary skills to start a business (Bosma & Kelley, 2019). While this suggests

2.3 Entrepreneurial Intentions

11

that women are less ambitious for their businesses than men, it does not mean that their growth ambitions are missing. In this regard, Dalborg (2015) found in a study conducted in Sweden with women entrepreneurs that they were aiming for business growth which was possible with a specific range of support measures. In the context of entrepreneurship, the behaviour of women has been observed to be more risk-averse (Dawson & Henley, 2015) and to be driven by intrinsic motives and by their long-term pursuits such as personal development, social recognition, and work-life balance rather than by financial success (Dalborg, von Friedrichs, & Wincent, 2012). Women seem to find entrepreneurship less appealing, perceive the environment less supporting, and show less determination towards the achievement of business success (Gupta, Wieland, & Turban, 2018). While women’s less optimistic attitudes may be the reason for a low propensity to start a business, researchers argue that these attitudes might result in more stable business behaviour and slower but steady development (Furdas & Kohn, 2010). Initiatives targeting entrepreneurship, in general, might not benefit women but rather reduce their effectiveness (Bosma & Kelley, 2019). This suggests the relevance of discovering root causes that underline low rates of women involvement in entrepreneurship. Contextual factors represent a critical factor that can stimulate or hinder women entrepreneurs. Moreover, policymakers should accommodate both men and women in the entire variety of their needs, preferences, and perspectives, both gender-specific and otherwise. In this way, a competitive advantage can be secured, and innovation can be fully tapped.

2.3

Entrepreneurial Intentions

Entrepreneurial intention is defined as “the conscious state of mind that precedes action and directs attention toward entrepreneurial behaviors such as to start a new business and becoming an entrepreneur” (Moriano et al., 2012, p. 165). Likewise, Peng, Lu, and Kang (2012) suggest a more comprehensive definition by arguing that intention represents a mental orientation which includes the desire, wish, and hope that guide individuals’ attention, experiences, and actions towards a specific goal. Several scholars argue that the intention to start a business represents a necessary precursor to performing entrepreneurial behaviour (Fayolle, Gailly, & Lassas-Clerc, 2006; Sharma, 2018). In this line, intentions are acknowledged to be the single indicator of actual behaviour (Ajzen, 1991). They are, therefore, critical to better understand individuals’ entrepreneurial behaviour in the process of recognising, pursuing, and exploiting opportunities.

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2 The Literature Review

Entrepreneurship literature holds various theoretical intention models. A distinctive attribute of intention-based models is the way antecedents of behaviour are handled (Krueger, 2009). According to the theory of reasoned action by Ajzen and Fishbein (1980), the behaviour is best predicted by intentions, which are derived from individuals’ self-efficacy, their attitudes towards the behaviour, and the subjective norms (Schlaegel & Koenig, 2013). Accordingly, social factors and attitudes affect intention, which consequently is a predicting factor of entrepreneurial behaviour. Thus, intentions play the role of a mediator or action catalyst in this theoretical model (Sharma, 2018). However, the theory of reasoned action is limited to predict behaviour due to one major drawback. According to Ajzen (1991), this theory is a sufficient model in behaviour prediction only when the behaviour is under volitional control of individuals. As a result, another intention-based model has been developed to overcome this limitation, which is an extension of the theory of reasoned action and is known as the theory of planned behaviour. This intention-based model was developed by Ajzen (1991) and was later introduced to the entrepreneurship literature by Krueger and Carsrud (1993). Accordingly, the theory of planned behaviour is underlined by the following three variables: attitudes toward a behaviour, subjective norms, and perceived behavioural control; whereas the last variable was introduced as an indicator of the control degree individuals have over their behaviour. These variables are established by an individual’s set of beliefs that correlate to each element respectively. Moreover, individuals’ background, culture, demographics, and experiences determine their behavioural, normative, and control beliefs (Ajzen, 1991). The elements of the theory of planned behaviour demonstrated that intentions have antecedents. Likewise, the entrepreneurial event model suggests that intentions depend on three variables: perceived desirability, propensity to act, and perceived feasibility (Shapero & Sokol, 1982). Unlike other intention-based models, the model developed by Shapero and Sokol (1982) is used specifically for determining entrepreneurial intention rather than predict any intention. There have been persistent efforts to extend the existing intention-based models, to develop new ones, or to develop a single, coherent model that integrates elements from existing ones (Esfandiar, Sharifi-Tehrani, Pratt, & Altinay, 2019). Thus, scholars have found other antecedents such as perceived barriers and perceived drivers to have the ability to predict entrepreneurial intention (Luthje & Frank, 2003). Buttar (2015) has empirically tested and validated the role of social capital in entrepreneurship; the personal relationships that affect individuals’ behaviour are a predictor of entrepreneurial intentions. Likewise, entrepreneurial experience and education have been proven to indirectly influence entrepreneurial intention since they are related to some of the elements of the theory of planned behaviour

2.4 Context in Entrepreneurship

13

(Yang, 2013). Hence, the entrepreneurial intention is a result of other antecedents than the ones stipulated by Ajzen (1991) as well as Shapero and Sokol (1982). Therefore, in this study, it is argued that perceptions of women of the drivers and barriers to embarking on an entrepreneurial venture affect their intentions to open a business.

2.4

Context in Entrepreneurship

Most research has drawn on the assumption that entrepreneurship occurs as a function of the individuals involved, whereas the entrepreneurial intention of individuals is determined by their background, experiences, demographic characteristics, or personality traits (Shook, Priem & McGee, 2003). However, Wickham (2006) argues that although research on personality and demographic characteristics is conceptually powerful, it does not provide enough evidence for anticipating an individual’s willingness to become an entrepreneur. In this line, recent research looks to cognitive processes and cognitive properties of entrepreneurs (Frederiks, Englis, Ehrenhard, & Groen, 2019), which influences the recognition and exploitation of a business opportunity. Even though the framework of such cognitive processes remains in a “black box”, self-perception and attitudes have been empirically tested and validated to have an influence on the entrepreneurs’ willingness to recognise an opportunity and act on it (Gatewood, Shaver, & Gartner, 1995). This does advocate that opportunity recognition and exploitation are likely to be linked to the individual context. However, several researchers have argued that the ability of entrepreneurs to identify and capitalise on opportunities is highly influenced by the environment in which they operate (Ireland, Hitt, and Sirmon, 2003; Ucbasaran, Westhead, & Wright, 2008). Gartner (1995) stresses a need for entrepreneurship research to recognise the context in which entrepreneurship occurs, due to the tendency of underestimating the impact of external factors and overestimating the factors at the individual level. This is in line with Gaddefors and Anderson (2016) who argue that context shapes what become entrepreneurial and entrepreneurial opportunities are conditions that find themselves in the external environment. An entrepreneur’s immediate environment consists of the business context (management, money and market) (Bates, Jackson, & Johnson, 2007); however, as organisations are connected with their wider national and supranational environments1 , one can argue that national and regional environment have an influence 1 Which

are acknowledged as European regions in this study

14

2 The Literature Review

on entrepreneurial activities of individuals. In the same line, Davey, Rossano, and van de Sijde (2016) acknowledge the significance of the national and regional context in a study conducted in 12 countries from four European regions. The researchers state that there is a significant difference in the drivers and barriers that affect entrepreneurship since European regions hinge on different levels of economic development and have unique histories and cultures. This argument is further supported by the National Innovative Capacity Index (NICI) which demonstrates how national environments affect innovation and entrepreneurship and how countries are different worldwide, by measuring the quality of “common innovation infrastructure”, “cluster-specific environment” and “quality of linkages” (Porter & Stern, 2001). The former theoretical concepts argue that entrepreneurship is considered a social process that is dependent on context and the perception of women’s entrepreneurial drivers and barriers will be influenced by national environments and the individual context. At the national level, countries differ in resource availability and institutions’ quality to promote and support entrepreneurial activities.

2.5

Individual and Social Context Factors

In the entrepreneurship literature, it has been empirically tested and validated that barriers have a significant impact on entrepreneurship and entrepreneurial activates (Lockyer & George, 2012; Naidu & Chand, 2015; Wu et al., 2019). Several authors discuss strategies and practices to manage and overcome barriers to foster entrepreneurship. The common assumption is that once barriers are overcome, the road is paved for more engagement in entrepreneurial activates. However, review on scientific literature suggests that focusing only on barriers is a “factual error” and influencing drivers, such as motivation and beliefs, can stimulate entrepreneurial activates to the point in which the impact of barriers to entrepreneurship lacks explanatory power (Davey et al., 2012). The impact of drivers is considered to be persuasive enough to compensate for barriers in entrepreneurial engagement. Consequently, strong drivers have higher importance when engaging in entrepreneurship than strategies to overcome or remove barriers. Hence, linking barriers to drivers that entrepreneurs perceive has been found to help entrepreneurs overcome perceived barriers to entrepreneurship (Bruneel, Ratinho, Clarysse, & Groen, 2012). Accordingly, drivers and barriers to women entrepreneurship are discussed in the following section.

2.5 Individual and Social Context Factors

2.5.1

15

Perceived Barriers

To enable comparability of all factors that affect entrepreneurship, “barriers” refer to any entrepreneurial conditions that are expressed in the negative. Moreover, the terms “barrier”, “obstacle”, “problem”, and “impediment” are used interchangeably. Whereas the term “barriers” is often interpreted in economics as factors established to restrict entry, in this research the term is used in the broader sense of any external or internal condition adverse to creating a new business (Kouriloff, 2000). Even though there is a body of literature on barriers to entrepreneurial intention, the perception of barriers from women’s perspective has received scant attention, despite its significance to the entrepreneurial process (Wu et al., 2019). Sarasvathy (2004) recognises the significance of barriers in the entrepreneurial process and states that the lower the barriers, the more individuals are motivated to engage in entrepreneurial activity. European Commission report on “Promotion of Women Innovators and Entrepreneurship” in 2008 identified three specific barriers to women entrepreneurship which are: economic (e.g. access to opportunities), contextual (e.g. stereotypes and visibility of women) and soft barriers (e.g. lack of confidence, knowledge, and fear of failure) (Ganzerla, 2008). There are high expectations, illustrated by reports like this, in the political and social world that there will be a rise in the level of women entrepreneurship. However, as Baines and Wheelock (2000, p. 45) highlight, much of the research tends “to follow the old add women and stir recipe” without fully examining entrepreneurship through a lens of gender. This suggests that by adopting a “one size fits all” approach and focusing on the business rather than the gender of the applicant, there is nothing done to redress the gender imbalance. Many of the barriers perceived for entrepreneurship are generally based on actions attributed to men (Lockyer & George, 2012). Consequently, support programmes designed to foster entrepreneurship among potential entrepreneurs are built around these gendered stereotypes. In order for women to succeed in a male-dominated environment, the barriers specific for women have to be further identified. Therefore, in order to identify the barriers mentioned in the relevant literature, previous studies have been examined. Four barriers were found to be frequently mentioned and explored by researchers which can be seen in Table 2.1.

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2 The Literature Review

Table 2.1 Barriers to Women’s Entrepreneurial Intention Assessed in Previous Research Factor

Relevant literature

Fear of failure Arenius & Minniti (2005); Wagner (2007); Shinnar, Giacomin, & Janssen (2012); Noguera, Alvarez, & Urbano (2013); Cacciotti, Hayton, Mitchell, & Giazitzoglu (2016); Chua & Bedford (2016); Giotopolus, Kontolaimou, & Tsakanikas (2017) Lack of Bandura (1992); Wilson, Kickul, & Marlino (2007); Trevelyan (2011); self-confidence Kasouf, Morrish, & Miles (2013); Austin & Nauta (2015); Giotopoulos et al. (2017); Bosma & Kelley (2019) Lack of Arenius & Minniti (2005); Wilson, Kickul, & Marlino (2007); Nelson, entrepreneurial Maxfield, & Kolb (2009); Liñán, Santos, & Fernández (2011); Gupta, opportunities Goktan, & Gunay (2014); Wu et al. (2019) Low attractiveness of career

Nicholson & Anderson (2005); Holliday (2008); Radu & Redien-Collot (2008); Hamilton (2013); Achtenhagen & Welter (2003); Santos, Azam Roomi, & Liñán (2016); Byrne, Fattoum, & Diaz-Garcia (2018)

Source: Own Illustration

2.5.1.1 Fear of Failure Fear of failure is seen as a critical barrier to the entrepreneurial activity which would constrain individuals from starting a business, even when an opportunity is recognised (Foo, 2011). Fear of failure is described as “the disposition to avoid failure and/or the capacity for experiencing shame and humiliation as a consequence of failure” (Atkinson, 1966, p. 13), and as the “disposition to become anxious about failure under achievement stress” (Atkinson & Litwin, 1973, p. 146). Fear of failure is further correlated with high perceived risk (Foo, 2011) and tends to negatively influence the individual’s propensity to open a business (Giotopolus, Kontolaimou, & Tsakanikas, 2017). According to Arenius and Minniti (2005), individuals who exhibit a high fear of failure tend to have a significantly lower intention to start a business. Fear of failure has not only been associated with lower entrepreneurial intention but also with an individual’s achievement motivation and their career aspirations influencing negatively the start-up behaviour and entrepreneurial growth ambition (Wagner, 2007). Cacciotti et al. (2016) argue that fear of failure can be approached from an individual trait, which might stay constant in different economic environments, or from an emotional state, which occurs as a one-off response to an environment that is threatening. Most of the studies using either the individual trait approach or the emotional state approach have empirically tested and validated the negative effect of fear of failure on women’s entrepreneurial intentions. In this regard, Wagner (2007) argues that fear of failure prevents women from starting a business

2.5 Individual and Social Context Factors

17

due to the perceived risks associated with the process of business creation. While Noguera, Alvarez, and Urbano (2013) support these findings, the researchers further argue that women tend to have a higher fear of failure when compared to man since women are more risk-averse in their choice behaviour and are less likely to expect debt financing to subsidise their businesses. However, contradictory results were found in China by Shinnar, Giacomin, and Janssen (2012), who argue that gender makes no difference in the experience of fear of failure due to the country’s culture of protecting one’s good reputation. In the same line, Chua and Bedford (2016) emphasise that the perception of failure is influenced by the social, cultural, and economic context and the degree of fear of failure varies across countries. Although most of the research suggests that fear of failure has negative effects on entrepreneurial intention, some opponents challenge this negative correlation (e.g. Cacciotti et al., 2016). For instance, Cacciotti, et al. (2016) report that fear of failure has positive effects on entrepreneurial intention since it encourages individuals to perform a higher commitment to run their business and it can support a higher determination to achieve success. Finally, despite the debate over the link between fear of failure and entrepreneurial intention, even the opponents have suggested that fear of failure both inhibits and motivates entrepreneurial intention. Therefore, it should not be neglected since it represents a rich opportunity to better understand the motivation of women to become entrepreneurs.

2.5.1.2 Lack of Self-Confidence The role of subjective beliefs and perceptions in entrepreneurship has been widely recognised in economic theories of entrepreneurship (e.g. Kirzner, 1973) and empirically validated in various studies (Giotopoulos et al., 2017; Schmutzler, Andonova, & Diaz-Serrano, 2018). Grounded in social cognitive theory (Bandura, 1986), self-confidence represents a key concept of the control belief structure that significantly predicts the behavioural intentions of individuals (Giotopoulos et al., 2017). In the entrepreneurship literature, self-confidence refers the collection of skills, experiences, and knowledge of an individual and it is described as an individual’s belief in their ability to engage in entrepreneurship and successfully start a business (Kasouf, Morrish, & Miles, 2013). Prior research demonstrates that self-confidence has a critical influence on individuals’ intention and competence to start a business, their persistence to overcome the changes and challenges of the business creation process, and their success in fulfilling tasks associated with entrepreneurship (Trevelyan, 2011). Moreover, self-confidence is the capability of individuals that motivates their entrepreneurial behaviours and enable them to face difficulties in the opportunity recognition and exploitation process (Tumasjan

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2 The Literature Review

& Braun, 2012). Therefore, self-confidence does not only affect the decision of individuals to engage in entrepreneurship but also leads their future performance in the process of managing and developing a business (Giotopoulos et al., 2017). The effects of self-confidence on entrepreneurial intention appear to differ by gender (Wilson, Kickul, & Marlino, 2007). In the case of women, the decreased likelihood of demonstrating entrepreneurial intentions and becoming small business owners is not due to inadequate training since men and women are equally likely to have participated in training on how to open a new venture (Austin & Nauta, 2015). However, several scholars (e.g. Wilson et al., 2007; Austin & Nauta, 2015) argue that women tend not to act on opportunities to pursue an entrepreneurial activity because they exhibit lower levels of self-confidence in comparison to men. These differences in the level of self-confidence appear in adolescence as well. For instance, Wilson et al. (2007) argue that entrepreneurial self-confidence is more important for teenage girls than for boys in exhibiting entrepreneurial career interest. Hence, it is more important for teen girls to perceive that they have the necessary skills in order to succeed as entrepreneurs when considering future career options. Likewise, research on adults has empirically tested and validated that women are more likely than men to limit their career choices or discontinue a business due to the lack of confidence in their skills, knowledge, and experiences (Bandura, 1992). While there is limited research that examines the link between self-confidence, entrepreneurial intention, and gender, previous evidence suggests that women exhibit lower levels of self-confidence and entrepreneurial intentions (Wilson et al., 2007). Furthermore, as argued by the research of Bandura, Barbaranelli, Caprara, and Pastorelli (2001), women might be more strongly affected by the perception of their skills deficiencies in comparison to men. As mention earlier, this pattern seems to be present before adulthood with girls being more likely to feel ill-prepared with regard to the necessary skills to pursue a specific career. However, the most recent report from the GEM project suggests that these trends occur among women worldwide, i.e. women exhibited lower levels of confidence and readiness in their capabilities to succeed as entrepreneurs (Bosma & Kelley, 2019). As indicated earlier, the belief of having the required skills is seen to be a dominant variable that has an impact independent of other contextual variables (Giotopoulos et al., 2017). Finally, there are gender differences in self-confidence and women are more likely than men to inhibit their career aspirations. Therefore, this study further adds the lack of self-confidence as a barrier since women’s intentions are more strongly affected by any perceived skill deficiencies in the entrepreneurial realm.

2.5 Individual and Social Context Factors

19

2.5.1.3 Lack of Entrepreneurial Opportunities Beside these perceptions, it is relevant to acknowledge other barriers related to the women’s environment which can affect women’s entrepreneurial intention. In this line, the impact of perceptions on business opportunities is worth mentioning. Based on the Kirznerian view of an entrepreneur as an opportunity seeker (Kirzner, 1973), individuals’ differential access to information makes it possible for entrepreneurial opportunities to arise. In this sense, individuals perceive opportunities by acknowledging the worth of new information to which they are exposed (Shane & Venkataraman, 2000). Opportunity perception is thereby defined as the assessment of individuals of a situation that leads to new economic activity (Davidsson & Honig, 2003). Previous research argues that opportunity perception is a key factor that motivates and triggers entrepreneurial behaviour. For instance, such opportunity perception can drive entrepreneurial intentions which consequently result in entrepreneurial activity (Krueger, 2000). Economic opportunities can be increased by the particular characteristics of markets, such as market size, the availability of financial resources, and various forms of capital such as physical, technological, human, and social (Liñán, Santos, & Fernández, 2011). This, consequently, will boost the number of people who engage in entrepreneurial activities (Casson, 1982). Even in this case, however, individuals need to perceive these business opportunities as desirable and feasible (Krueger, 2000). Therefore, cognitive processes might increase the level of sensitivity of some individuals in the way they perceive business opportunities provided by the market. In this line, the evolution of available entrepreneurial opportunities will have a significant effect at the macroeconomic level on entrepreneurial intentions and the new business creation rate (Liñán et al., 2011). However, at the individual level, one might exhibit entrepreneurial intentions based on their cognitive processes and perceptions of business opportunities, no matter whether the perceptions are real or not (Arenius & Minniti, 2005). The effects of opportunity perceptions on entrepreneurial intention appear to differ by gender (Wilson et al., 2007). In the case of women, scholars argue that opportunity recognition and exploitation is negatively impacted by gender roles and stereotypes and the fact that they exhibit less human capital (Wu et al., 2019). For instance, Gupta, Goktan, and Gunay (2014) highlight that masculine stereotypical information about entrepreneurs increases the opportunity evaluation of men. While Wu et al. (2019) agree on these findings, the researchers further add that men and women might recognise and evaluate business opportunities in the same manner only if entrepreneurs were described using gender-neutral information. Furthermore, even resource providers (e.g. venture capitalists) asses

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2 The Literature Review

the entrepreneurial role as being masculinised, setting women at a disadvantage when it comes to resource access (Nelson, Maxfield, & Kolb, 2009).

2.5.1.4 Unattractive Career Path In the entrepreneurship literature, the dominant disclosure of entrepreneurship is portrayed as a form of masculinity and representations of gender in contemporary society depict a complex and paradoxical phenomenon (Ahl, 2006; Hamilton, 2013). In this line, representations of the entrepreneur are monopolised by male experiences and built on a cramped range of stereotypes such as the entrepreneur being the heroic adventurer who is individualistic, ruthless, and aggressive (Hamilton, 2013). Gender theorists stress that the representation of women entrepreneurs in society has been fluctuating and sometimes in contradiction (Holliday, 2008), even though stories of successful women have been reinforced lately across a range of media. In a study conducted between 1989 and 2000 about the representation of women entrepreneurs in the media, Nicholson and Anderson (2005) found that women represented only 2.7 per cent of the total sampled cases. The researchers further examined the shifts in the portrayal of entrepreneurs and entrepreneurship and came to the conclusion that the entrepreneurial myth remains “resolutely male” (Nicholson & Anderson, 2005). Likewise, Radu and RedienCollot (2008) support these findings by highlighting that the representation of an entrepreneur is objectified, and the information selected to describe entrepreneurship is selected according to cultural norms and used to create a “figurative norm”. The researchers further argue that the entrepreneur image in the French context is represented by males between 30 and 40 years old, individualistic and focused on launching or acquiring businesses as a main entrepreneurial activity. This figurative norm of an entrepreneur is further used to shape the social interactions of entrepreneurs no matter the gender. Female entrepreneurs are on limited occasions featured in the popular culture and they are portrayed in a different manner from their male counterparts. Particularly, women are portrayed at the interface between the public and private sphere (Hamilton, 2013). Moreover, the achievements of women entrepreneurs are portrayed in their terms of domestic responsibilities and how they balance these two spheres (Hamilton, 2013). Achtenhagen and Welter (2003) further challenge this discrepancy and argue that it is normal for female entrepreneurs to be asked how they combine work and family responsibilities while managing their businesses; questions that do not naturally arise in interviews with male entrepreneurs. These gender norms and unequal approaches to entrepreneurship need to be transformed in order to change women’s perception of entrepreneurship and frame it as an attractive and feasible career. Women are entranced in a context that holds

2.5 Individual and Social Context Factors

21

deeply engrained gender stereotypes and a masculine construction of entrepreneurship, and, as a result, women are prevented from perceiving entrepreneurship as an attractive and feasible career option (Santos, Azam Roomi, & Liñán, 2016). At the same time, a lack of encouragement for dialogue on the gender gap and a lack of a supportive environment for women’s entrepreneurial activity further demotivate women to start a business (Byrne, Fattoum, & Diaz-Garcia, 2018). Lastly, as long as the portrait of an entrepreneur is reinforced as a form of masculinity and does not vary, it does not enable identification. If women identification with the image of an entrepreneur is tenuous, then, in tur, women consider entrepreneurship an unattractive career choice (Byrne et al., 2018). Therefore, this study further tests the unattractive career path as a further barrier for women to start a business. Summarising the literature review on perceived barriers to women’s entrepreneurial intention, the following hypothesis is formulated: H1: The perception of barriers to start a business (fear of failure, lack of selfconfidence, lack of entrepreneurial opportunities, unattractive career path) has a significantly negative effect on the entrepreneurial intention of women.

2.5.2

Perceived Drivers

Drivers are outlined as factors that facilitate a women’s decision to choose entrepreneurship instead of other career paths and supports them outreach barriers (Hessels, van Gelderen, & Thurik, 2008; Davey et al., 2012). In the entrepreneurship literature, the terms “drivers”, “facilitators”, “motivators”, and “enablers” are used interchangeably. Whereas the term “drivers” is often interpreted in economics as factors established to ease entry, in this research the term is used in the broader sense of any external or internal condition supportive to creating a new business. Several scholars have researched various factors as to why some individuals might be more motivated or inclined to engage in behaviours necessary to follow an entrepreneurial career (Hessels et al., 2008; Simón-Moya, RevueltoTaboada, & Guerrero; 2013; Davey et al., 2016). Hessels et al. (2008) argue that these reasons are to be found at the individual level and include the desire for autonomy (e.g. independence, freedom), income and wealth, and motivation to innovate and create new technologies or products; with independence being one of the most cited drivers for starting a business (van Gelderen & Jansen, 2006). While Wilson et al. (2007) agree upon these drivers, the researchers add the need

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2 The Literature Review

for recognition and high social status. Moreover, Radu and Redien-Collot (2008) stress that media is one of the factors that have the most critical effect on human minds and, as a result, it plays a fundamental role in the decision of an individual to get engaged in entrepreneurial activities. In addition to these pulls, there are drivers that push individuals towards entrepreneurship (Thurik, Carree, van Stel, & Audretsch, 2008). Necessity reasons arise, for instance, when unemployment steers individuals towards self-employment (Hessels et al., 2008). Many of the drivers perceived for entrepreneurship are generally based on actions attributed to men (Lockyer & George, 2012). Consequently, support programmes designed to foster entrepreneurship among potential entrepreneurs are built around these gendered stereotypes. In order for women to succeed in a male-dominated environment, the drivers specific for women have to be further identified. Therefore, in order to identify the drivers mentioned in the relevant literature, previous studies have been examined. Four drivers were found to be frequently mentioned and explored by researchers which can be seen in Table 2.2. Table 2.2 Drivers to Women’s Entrepreneurial Intention Assessed in Previous Research Factor

Relevant literature

Knowing a nascent entrepreneur

Morales-Gualdron & Roig (2005); Arenius & Kovalainen (2006); Bogren, Friedrichs, Rennemo, & Widding (2013); Wu, Li, & Zhang (2019)

High perceived social status

Carter, Gartner, Shaver, & Gatewood (2003) Kalden, Cunningham, & Anderson (2017); Bryne, Fattoum, & Diaz-Garcia (2018)

Media

Achtenhagen & Welter (2007); Radu & Redien-Collot (2008); Ruth Eikhof, Summers, & Carter (2013)

Perceived ease of starting a business

Ozaralli & Rivenburgh (2016); Aldrich & Wiedenmayer (2019); Bosma & Kelley (2019)

Source: Own Illustration

2.5.2.1 Knowing a Nascent Entrepreneur Recent entrepreneurship literature has viewed entrepreneurs from a social perspective and the process of founding a business as embedded in social networks (Newbert & Tornikoski, 2012; Schmutzler et al., 2018). In this line, various literature strands have evaluated the impact of social context on entrepreneurship and have contributed to the development of fundamental research themes such as the effect of social networks and social capital, mentors, peers, and

2.5 Individual and Social Context Factors

23

role models (Gedajlovic et al., 2013; Eesley & Wang, 2017). A common argument in this literature is that potential women entrepreneurs might benefit from their social environment by accessing tangible as well as intangible resources (Hoang & Yi, 2015; Schmutzler et al., 2018). In this regard, network relations enable potential women entrepreneurs to recognise and exploit entrepreneurial opportunities by having access to information, knowledge, guidance, skills, and financial resources (Anderson, Park, & Jack, 2007). Moreover, social ties facilitate intangible resources which bring emotional support for risk-taking actions of entrepreneurs, promote persistence to continue the business, and build trust (Schmutzler et al., 2018). In this context, social legitimacy, reputation, and credibility are established and they act as a prism that modifies women’s intentions to engage in entrepreneurship. Generally, entrepreneurial role models, mentors, and peers who are involved in entrepreneurial activity that form a women social network are perceived as key influencers (Schmutzler et al., 2018). Above all, several scholars highlight that entrepreneurial intentions interact with cognitive perceptions since nascent entrepreneurs appear as references to potential women entrepreneurs who later associate greater value to the entrepreneurial activity (Arenius & Kovalainen, 2006; Bogren, Friedrichs, Rennemo, & Widding, 2013). For instance, Bogren et al. (2013) have empirically tested and validated the effect of social networks on entrepreneurial behaviour by conducting a survey with women entrepreneurs in Sweden and Norway. The researchers further argue that networks are seen as a supportive asset and women express greater willingness to expand their networks and seek further growth. The results of the study contend that social ties are of high importance to women’s intentions and women who have parents, close friends, or neighbours active in business seem to have a higher chance of engaging in entrepreneurial activities. In respect to personally knowing nascent entrepreneurs, Morales-Gualdron and Roig (2005) found positive correlations in their sample drawn across 29 countries. These outcomes are consistent with research conducted by Arenius and Kovalainen (2006) in the context of Nordic countries that evaluates the importance of role models to a woman’ s propensity to start a business. While the extent of such positive relationship between one’s intention to become an entrepreneur and the social ties is debatable (e.g. Eesley & Wang, 2017), several researchers argue that women may convey social value to entrepreneurship even though there was no interaction. Lastly, this research argues that knowing a nascent entrepreneur determines a woman to have a higher chance of engaging in entrepreneurship by associating this activity with social value which can manifest itself in greater admiration or

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2 The Literature Review

higher social status. This study considers that knowing an entrepreneur is a further driver that motivates the entrepreneurial intention of women.

2.5.2.2 High Perceived Social Status Perceived social status refers to the subjective evaluation of an individual of their standing in the society and has been found to affect various individual choices including health, consumption, and career (Abebe & Alvarado, 2016). An extensive body of research has investigated the subjective evaluation and occupation rankings within the social structure and the influence of such evaluations on the career choice of individuals and ultimately on their intention to start a business (Liñán & Chen, 2009; Abebe & Alvarado, 2016). Frank (1984), one of the first researchers who acknowledged the importance of social status in entrepreneurship, claims that an individuals’ status among their peers is no less valuable than their level of income in establishing their well-being level. Moreover, Abebe and Alvarado (2016) argue that individuals’ likelihood to pursue an entrepreneurial opportunity is higher if they believe their social status improves as a result of their choice. While Stryker and Burke (2000) agree, the researchers further add that careers are an essential facet of individuals’ social identities. Previous literature has empirically tested and validated that the social status associated with entrepreneurship is high if the role of the entrepreneur is perceived as being economically and socially important (Liñán & Chen, 2009; Kalden, Cunningham, & Anderson, 2017). Thus, the admiration afforded to entrepreneurship might shape its attractiveness to engage in entrepreneurial activates and foster entrepreneurial intention (Fayolle, Liñán, & Moriano, 2014). However, Kalden et al. (2017) stress that the social identity of individuals is not the only predicting factor of entrepreneurial intention; the individual’s identity needs to be considered to the same extent. In this line, it is argued that the entrepreneurial intention is influenced by the belief individuals hold about themselves (McGrath & MacMillan, 1992). Furthermore, women try to understand the entrepreneurial individual in order to decide whether an entrepreneur is a good thing to become. The entrepreneur is regarded in the enterprise culture as a “hero” (Carr & Beaver, 2002) and more specifically, the women entrepreneur as a “superwoman” (Bryne, Fattoum, & Diaz-Garcia, 2018). In this line, Carr and Beaver (2002) argue that the entrepreneur is portrayed as an economic saviour who takes part in the generation and regeneration of the economic fortune of a country. The heroic construct, therefore, represents a desirable status to have in society. Kalden et al. (2017) argue that mental prototypes encourage women to project themselves in the role of an entrepreneur and how their performance is received by the societal audience.

2.5 Individual and Social Context Factors

25

This, in turn, implies that entrepreneurs who are active and portrayed as “superwomen”, can bring representations in the mind of women who are willing to engage in entrepreneurship in the form of a desirable role model or an affirmation that they seek for their career (Carter, Gartner, Shaver, & Gatewood, 2003). Finally, the status and social acceptance of entrepreneurship as a future career act as a driver for women to engage in entrepreneurship when an entrepreneur is portrayed as a cultural and economic hero. However, Hytti (2005) stresses that such a perspective of entrepreneurs is sensitive to time and place and consequently the portrait of an entrepreneur depends on the country. Therefore, this study further adds the social status associated with entrepreneurship as a further driver for women to start a business.

2.5.2.3 Media A last potential driver of entrepreneurial intention is the contact of a woman with media, such as television, social media channels, and media campaigns. Representations of entrepreneurs in the media have a significant effect on entrepreneurial intentions (Radu & Redien-Collot, 2008). Acknowledging this relationship, various scholars discuss how media representations of female entrepreneurs influence the society’s perceptions of female entrepreneurs and their entrepreneurial capabilities (Achtenhagen & Welter, 2007). The representation of women in media influences the perception of women of business ownership. If further affects how women perceive its attainability and shapes the strength and direction of their entrepreneurial intentions (Radu & Redien-Collot, 2008). Khajeheian (2013) stresses that media is one of the factors that have the most critical effect on the minds of individuals. As a result, it plays a fundamental role in the decision of a woman to get engaged in entrepreneurial activities. In this line, the researcher further adds that media represents a good possibility for economic growth, given the right preparation and learning in the first instance. Levie, Hart, and Karim (2010) empirically tested and validated the effect of television business reality programmes on entrepreneurial intention in the context of the UK. However, their findings conclude that these programmes are more focused on audience entertainment rather than promoting entrepreneurial behaviours. The researchers further contend that there is no clear evidence that the media acts as a driver for entrepreneurial intention. Against this backdrop, Levie et al. (2010) contend that media campaigns are positively correlated with positive media coverage of entrepreneurs and television programmes do promote entrepreneurfriendly environments. A report from GEM project conducted in 2013, in which the media types that influence founders the most are displayed, highlights that television is ranked highest before print media, internet research, social media and

26

2 The Literature Review

radios. Thus, presenting stories about entrepreneurs in the media brings the topic of business foundation closer to the public and leverages intentions to engage in entrepreneurial activities. However, scholars stress that there is always a question of more than one condition that leads to business creation (Ruth Eikhof, Summers, & Carter, 2013). Lastly, media can act as a starting point for the entrepreneurial behaviour of women and, as a consequence, this factor cannot be neglected in the conceptualisation of drivers. Thus, Ruth Eikhof et al. (2013) argue that even though the media can influence women to engage in entrepreneurship, the content presented to the audience might enrich existing gender inequalities in entrepreneurship. Thus, media will be included as a driver since the triggering effect of this factor can be perceived in a broader context.

2.5.2.4 Perceived Ease of Starting a Business The intention of starting a business of a woman might be influenced by the existing and perceived economic and political environment of the country. According to Aldrich and Wiedenmayer (2019), the socio-political climate can have such a powerful influence that it can support or diminish entrepreneurship. For instance, an unfavourable economic environment with market fluctuations, high unemployment, and inflation rates may create scepticism and, as a result, discourage individuals from engaging in entrepreneurial activities (Ozaralli & Rivenburgh, 2016). Moreover, bureaucratic barriers, lack of intellectual property rights and corporation laws, corruption, and economic and political instability are aspects among many others that threaten entrepreneurship growth. Hence, in an environment which supports entrepreneurship economically and politically, individuals become motivated to act on entrepreneurial opportunities (Ozaralli & Rivenburgh, 2016). However, several scholars argue that evidence of the regulatory environment on entrepreneurial intention is less strong and clear than it is assumed (Levie & Autio, 2008; Bosma & Kelley, 2019). For example, the GEM project reports that less developed countries characterised by negative economic conditions (e.g. high inflation and unemployment rates) record that individuals get more engaged in entrepreneurial activities in comparison with individuals from developed economies. Likewise, Davey et al. (2011) come to the same conclusion and argue that respondents in developing countries demonstrate higher entrepreneurial intentions and are more likely to imagine their future career as an entrepreneur. Finally, given this discrepancy, it is argued that the existing regulatory environment does not have a direct influence on the entrepreneurial intention, but rather on how women perceive the ease of doing business in a specific country. For this reason, the GEM global report conducted in 2019 has featured for the first time an

2.6 Wider-Environment Context Factors

27

indicator that measures the women’s perception of the ease of starting a business (Bosma & Kelley, 2019). Therefore, this study further adds women’s perception of the ease of starting a business as a further driver to entrepreneurial intention. Summarising the literature on factors that support women’s entrepreneurial intentions, the following hypothesis is formulated: H2: The perception of drivers to start a business (knowing a nascent entrepreneur, high perceived social status, media, perceived ease of starting a business) has a significantly positive effect on the entrepreneurial intention of women.

2.6

Wider-Environment Context Factors

Acknowledging the role of environment in entrepreneurship, scholars argue that drivers and barriers to entrepreneurial intentions are context-specific (Shane & Venkataraman, 2000; Davey et al., 2016) and, as a result, entrepreneurial activities of women cannot be treated in isolation from the broader context in which they are active. An entrepreneur’s ability to perceive opportunities depends on the availability of data in the environment since resources are needed in order to exploit these opportunities (Davey et al., 2016). Moreover, Barney (1991) argues that firms decide to generate when they possess the necessary sustainable competitive advantage which represents the valuable, hardly imitable, and non-substitutable resources. The previous argument is further supported by Hitt, Ireland, Sirmon, and Trahms (2011) who stress that firms’ resources are the main contributors of the entrepreneurial and strategic actions of individuals. Besides the availability of resources, scholars further highlight that the “rules of the game” dictate the decision of an individual to start a business. Under this view, the degree to which the rule of law is respected in the country, and the degree to which laws support the appropriation of returns from entrepreneurial efforts are considered further important factors that influence the allocation of effort to entrepreneurship (Davey et al., 2016). In this line, the Entrepreneurial Framework Conditions (EFC) developed by GEM project underline the specific conditions under which entrepreneurship can occur by “defining the rules of the game” in a specific context. Under this view, the rate and nature of entrepreneurship will change if the conditions in this framework will be modified (Levie & Autio, 2008). To achieve the objective of this research, both a national and supranational perspective is

28

2 The Literature Review

adopted. Even though there are various reasons for researching the national environment, there are fewer that support the supranational level2 (Davey et al., 2016). The tendency is to ignore differences between regions since national data is more favourable to be acquired. However, a study conducted by the GEM project in 2018 stresses that the level of entrepreneurship involvement is relative to the framework conditions in the European region (Bosma & Kelley, 2019). For this reason, the influence of four European regions on the perception of barriers and drivers will be analysed. Summarising, the following hypothesis with regard to barriers and drivers that affect women’s entrepreneurial intention to start a business in Europe can be formulated: H3: The women’s perception of drivers and barriers to start a business differs among regions.

To provide more relevant analysis of the role of environment in shaping entrepreneurial intentions, a lead country was selected for the European regions: (1) Sweden (North), (2) Spain (South), (3) Germany (West), (4) Poland (East). The countries were selected since they represent the largest economy in the region. However, other decision criteria have been taken into consideration in order to enrich the analysis. Poland offers a different perspective since it is a “new” European member state and a country in transition, whilst Spain presents a nation whose economy is suffering substantially due to the economic crisis which consequently affects women’s employment rates. Sweden is considered a gender equality role model with the current government self-declared a “feminist government” devoted to a feminist foreign policy and laws against gender discrimination (Sweden Institution, 2019). In recent years Germany has taken significant measures towards facilitating and fostering entrepreneurship among women. Even though in Germany there are areas of improvement, the entrepreneurship support for women is strong overall, with targeted training, coaching, and mentoring being widely available for women (Bijedi´c & Welter, 2015). Table 2.3 illustrates the environment for selected countries in Europe to highlight the differences in availability of resources for innovations and entrepreneurship, as well as entrepreneurial framework conditions.

2 In

this research, the supranational level refers to the European regions north, south, west, and east.

2.6 Wider-Environment Context Factors

29

Table 2.3 Comparison Between Countries Factors

Sub-factors

Poland

Spain

Sweden

Germany

Entrepreneurship Specific Data (1)

GEM Ranking 2018

50.4

45.3

73.1

65.9

Financial& Human & Global innovation 39 28 2 9 Technological Factors index Rank 2018 (2) GDP per capita 2017 29,521.3 38,286.0 51,474.8 50,425.2

Institutional & Policy Factors (3)

Gender Specific Factors (4)

Government expenditure on R&D as a percentage of GDP

1.0

1.2

3.3

2.9

% of government spending on total education

4.9

4.3

7.7

4.9

New 1.7 businesses/population

3.2

8.1

1.3

High-tech exports ($ million)

7.2

3.9

8.8

13.9

Patent applications by residents

0.3

0.8

7.6

4.6

Political stability

0.52

0.27

0.98

0.58

Government effectiveness

0.63

1.03

1.84

1.72

Rule of law

0.47

1.01

1.94

1.61

Control of corruption 0.73

0.49

2.14

1.84

Regulatory quality

0.88

0.94

1.80

1.78

Gender equality index 2018

56.8

68.3

82.6

65.5

Work

66.8

72.4

82.6

84.2

Money

73.3

75.9

87.5

84.2

Knowledge

56.0

65.3

72.8

52.9

Time

52.5

64.0

90.1

65.0

Source: (1) Global Entrepreneurship Index (Bosma & Kelley, 2019); (2) Global Innovation Index (Dutta, Lanvin, & Wunsch-Vincent, 2018); (3) World Governance Indicators (Kaufman & Kraay, 2019); (4) European Institute for Gender Equality (Barbieri et al., 2017)

30

2 The Literature Review

Comparing entrepreneurship specific data, it can be concluded that Germany and Sweden are ranked all markedly superior environments that support entrepreneurship when compared with Sweden and Spain. Concerning the resource availability, Sweden and Germany rank over Spain and Poland when analysing data from the Global Innovation Index. According to Davey, Plewa, and Struwig (2019), there is a link between GDP per capita and entrepreneurial intentions, and in this line, Germany and Sweden are leading again over the four countries. This is reinforced over the availability of human and technical resources, such as the proportion of new business created, high-tech export rates, and patent applications by residents. The pattern of superiority continues for Germany and Sweden when assessing the institutional and policy framework conditions. In this case, the countries representing the Southern and Eastern regions face more political instability, corruption, government ineffectiveness, and low regulatory quality. In respect to gender-specific factors, Sweden and Germany rate higher than their southern and eastern counterparts with respect to work, money, and time aspects. This means that in Germany and Sweden, women benefit more from equal access to employment, good working conditions, financial resources, and they face fewer gender inequalities with regard to their allocation of time spent on doing domestic work and social activates. However, with regard to gender inequalities in educational attainment and participation in education, Germany scores the lowest among the four countries. This is due to the low level of segregation in study fields among men and women, with low levels of men involved in the fields of education, health and welfare, humanities, and arts (Barbieri et al., 2017). These statistics highlight that women entrepreneurs in Poland and Spain have lower levels of available resources for innovation, which might be a reason for the low levels of entrepreneurial activity. Despite the simplified view, differences between Sweden, Spain, Germany, and Poland exist concerning country conditions to entrepreneurship. Summarising, the following hypothesis with regard to barriers and drivers that affect women’s entrepreneurial intention to start a business in Europe can be formulated: H4: The effects of drivers and barriers to start a business on the women’s entrepreneurial intentions differ among countries.

2.7 Conceptual Model and Hypotheses

2.7

31

Conceptual Model and Hypotheses

The literature review provided critical theoretical aspects that supported to build the background for the empirical study and accomplish the research objectives. To see which drivers and barriers women across the northern, southern, eastern, and western regions of Europe experience and to what extent these influence their intention to open a business, factors that hinder and support the creation of business for women have been identified in the literature and explored. To do so, the following hypotheses advanced based on the above discussion are summarised: H1: The perception of barriers to start a business (fear of failure, lack of selfconfidence, lack of entrepreneurial opportunities, unattractive career path) has a significantly negative effect on the entrepreneurial intention of women. H2: The perception of drivers to start a business (knowing a nascent entrepreneur, high perceived social status, media, perceived ease of starting a business) has a significantly positive effect on the entrepreneurial intention of women. H3: The women’s perception of drivers and barriers to start a business differs among regions. H4: The effects of drivers and barriers to start a business on the women’s entrepreneurial intentions differ among countries.

The four hypotheses and their linkages are illustrated in Figure 2.2.

Perceived Barriers Fear of Failure Lack of Self-Confidence Lack of Entrepreneurial Opportunities Unattractive Career Path

Perceived Drivers Knowing a Nascent Entrepreneur High Social Status Media Ease of Starting a Business

H1

-

H2

Entrepreneurial Intention of Women

+

Figure 2.2 Conceptual Model for Drivers and Barriers of Women Entrepreneurship. (Source: Own illustration)

3

Methodology

The third chapter presents the research methodology adopted to solve the research problem and to answer the research questions. The methodology of this study is shaped by the philosophical assumptions undertaken by the author, which is positivism with an empirical realist ontology. The empirical analysis is based on a comparative study of Germany, Poland, Spain, and Sweden 2015 GEM Adult Population Survey data. The main interest of this paper focused on the analysis of entrepreneurial intentions among women in the context of Germany, Poland, Spain, and Sweden. The following sections of this chapter justify the choice of the research philosophy, research strategy and design, research methods, and data analysis.

3.1

Research Philosophy

The research philosophy reveals the nature and development of knowledge which in turn shapes the understanding of the research questions, the methods used to answer them, and how the findings are interpreted (Saunders, Lewis, & Thornhill, 2016). The adopted research philosophy can be thought of as the assumptions about how the world is viewed. These assumptions further underpin the research strategy and design which are chosen for the research project. However, there is no generally recognised single best philosophical assumption; the selection of a research philosophy is done with regard to its suitability to fulfil the objectives of the research project (Ritchie & Lewis, 2003). The main philosophies underlined in the literature with regard to the use of quantitative and qualitative methods (or both) are “positivism” (Pugh & Hickson, 1976), “realism” (Bhaskar, 2014), and “interpretivism” (Lin, 1998). On the © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_3

33

34

3

Methodology

one hand, positivism is found mostly in quantitative studies that apply a strictly scientific approach where hypotheses are deducted and tested by applying precise measurement while interpretivism is generally associated mostly with qualitative studies which require an approach that is more sensitive to the special qualities of people (Saunders et al., 2016). On the other hand, realism has been found in mixed methods studies where the ontological perspective represents the conceptualisation of the world. Realism is simply a way of knowing the reality whereas positivism assumes that the conceptualisation of the world reflects the reality. Nevertheless, “pragmatism” (Howe, 1988) has been used in multiple methods studies where the importance of the research finding stays in its practical consequences. Moreover, in pragmatism, the world can be interpreted in various ways and there is no single point of view which can project the whole picture of the world (Saunders et al., 2016). Therefore, based on the research objective, the selected research philosophy underpinning this research is the positivism assumption (Pugh & Hickson, 1976). Positivists adopt an empirical realist ontology where the world “out there” consists of phenomena that are observable, perceptible, calculable, and quantifiable and that can be discovered, explained, and noticed by individuals. Therefore, the ontological perspective of positivism states that the world is “real”, and it exists external to and independent of human knowledge (Sayer, 2000; Saunders et al., 2016). In other words, what cannot be noticed and explored most likely does not exist—ultimately, is excluded from scientific research. However, the existence of phenomena that cannot be observed or experienced is not denied but rather is excluded from inquires (Fleetwood, 2001). Concerning the causality relations, positivists assume that causes are the results of another event, typically a proceeding one. The truth of scientific knowledge is pursued fiercely by positivists, hence seeking to project that knowledge as an accurate representation of reality. This truth of knowledge is determined through hypothesis testing which is postulated via an inductive method and is established whenever validation is empirically accomplished or when falsification cannot be reached (Sousa, 2010). According to Sayer (2000), positivists often employ an inductive method in the development of scientific explanations and predictions by making extensive use of quantitative research analyses such as variance and regression analyses. Translating these concepts to the research design, it can be considered that the hypotheses of this research have been developed by applying a deductive approach to the relationship between theory and research. In this line, the purpose of this study is to collect data about an observable reality and search for relationships between the drivers and barriers perceived by women and their entrepreneurial intention. Moreover, this study assumes that the effect of the drivers and barriers

3.2 Research Strategy

35

on the entrepreneurial intention is measurable and that methods that employ objective analysis are best suitable to fulfil the research objectives. Therefore, based on this philosophical foundation, the following sections of the chapter illustrate the use of quantitative methods approach in this research.

3.2

Research Strategy

Now that the overall philosophical assumptions and the research approach of the study are known, the research strategy which represents the general orientation to conduct the research has to be selected. According to Bryman (2016), when the research strategy has to be chosen there is a common distinction between quantitative and qualitative research. Although the status of distinction is considered ambiguous and unrewarding, it is regarded as a convenient approach to classify and simplify various possible methods and strategies to conduct the research (Saunders et al., 2016). At first glance, there would seem to be little distinction between a quantitative and a qualitative approach other than the fact that measurement is employed in quantitative studies. There is certainly the case that there is a predisposition among researchers to adopt this view, however, scholars suggest that more differences exist besides the superficial issue of the presence or absence of quantification (Bryman, 2016). In this line, quantitative and qualitative research is seen to have different philosophical and ontological assumptions which further affect the theoretical framework and the tactics used for the collection and analysis of data. Thus, quantitative research can be formulated as a research strategy that emphasises the testing of theories by applying a deductive approach to the relationship between research and theory. Moreover, the emphasis is on quantification where research embodies a view of social reality as an external and objective reality which is grounded in hypotheses, variables, and statistics (O’Leary, 2017). In quantitative research, researchers are on the quest of finding a relationship between variables and explaining the objective causerelation outcomes. By contrast, in qualitative research, the emphasis is on words, text, and stories rather than on the numerical and statistical data. Furthermore, it represents a research strategy that predominantly adopts an inductive approach to the relationship between theory and research, in which the generation rather than the testing of theories is emphasised. Studies of qualitative nature do not aim to propose generalisations or draw population-wide solutions but rather focus on exploring issues more in-depth and investigate the occurrence of events rather their frequency (Saunders et al., 2016). Moreover, in qualitative research, multiple realities are accepted by researchers who try to get a detailed understanding

36

3

Methodology

of a certain phenomenon rather than just proving its existence and relationships (Creswell, 2014). In addition to the these sharply outlined approaches, mixed methods represent a strategy that has emerged to integrate aspects from both quantitative and qualitative approaches (Bryman & Bell, 2015; Saunders et al., 2016). This approach to mixed methods research is best suited when the researcher cannot rely on either a quantitative and qualitative method alone and the findings need to be strengthened with a method drawn from the other research strategy (Bryman & Bell, 2015). Moreover, mixed methods approach is adopted when the gathering of two kinds of data is crucial: qualitative data that allows the access to people’s perspectives; and quantitative data which allows the exploration of specific issues. When this occurs, researchers seek to explore a phenomenon in both ways, so that an unstructured approach can be adopted for data collection and a more structured one for data analysis (Mertens, 2014). With reference to the research on hand, a quantitative research strategy has been adopted in order to achieve the study’s objective and answer the research questions. In this line, the study is quantitative since it applied a deductive approach to the relationship between theory and research (Bryman & Bell, 2015). In this research the testing of theories has an important role and already existing theory on the topic of women entrepreneurship has been elaborated and hypotheses were constructed based on the review of the literature. Moreover, this study assumes a measurable and fixed reality that is external and objective. This study is rather concerned with discovering facts about a certain phenomenon, which is the women’s perception of drivers and barriers in different countries, rather than understanding human behaviour and interpreting words and meanings (Cooper & Schindler, 2013). Since quantitative approaches enable scholars to learn about the variation and interaction between various variables (Bryman & Bell, 2015), in this research the relationship between the drivers and barriers perceived by women and their entrepreneurial intention in being investigated.

3.3

Research Design

After the research strategy has been chosen, the framework for conducting the research project has to be set. Even though a broad approach to the study’s objective has already been developed, the research design determines the details, practical aspects of implementing the quantitative approach (Saunders et al., 2016). Particularly, aspects concerning generalisation, the degree of understanding behaviour, temporal dimension, and cause-effect relationships between variables

3.3 Research Design

37

will be asserted through the type of design (Kopp & Steinbach, 2018). The research strategy is connected to a group of possible research designs which support the achievement of objectives for each strategy. For instance, grounded theory research, ethnographic, or narrative research are the most generally accepted designs for qualitative studies. Since these research designs are focused on the exploration of behaviours, stories, and experiences of individuals, they help fulfil the objective of qualitative research which is to understand and explain a phenomenon in depth (Bryman, 2016). The purpose of this research is to explore how women perceive different drivers and barriers among countries and to investigate to what extent these drivers and barriers influence their entrepreneurial intentions. In order to achieve the study’s objective, a quantitative research strategy has been developed. The prevailing research design associated with this strategy is experimental research, cross-sectional survey, longitudinal design, case study design, and comparative design (Bryman, 2016). In an experimental design, the independent variables are manipulated in order to see if there is a change in the dependent variable to establish causal relationships (Bryman, 2016). Moreover, the basis for experimental manipulation is formed by two groups which are treated independently from one another. Hence, it is then possible to find how far differences between the groups are accountable for changes in the dependent variable. Comparative analysis performs several functions that are linked to each other. More specifically, through a comparative analysis the understanding of a society is enhanced by placing its structures and patterns against those of other systems and the awareness of other systems, cultures, and patterns of thinking and behaving is heightened, thereby casting a fresh light on a society’s communication arrangements and enabling a critical contrast with those prevalent in other countries (Esser & Vliegenthart, 2017). In this line, this research design embodies to the logic of comparison (Bryman & Bell, 2015) which suggests that social phenomena can be better comprehended when they are put in comparison with two or more meaningfully contrasting cases or contexts. The research design adopted in this study is comparative since this paper explores a particular phenomenon in different contexts with the aim to compare their manifestations by using a single research instrument (Bryman, 2016). In this study, the phenomenon of women entrepreneurs in Germany, Poland, Spain, and Sweden is explored with the purpose to seek explanations for differences or similarities and to gain a greater knowledge and a deeper understanding of social reality in various national contexts. Moreover, the comparative design adopted in this research supports the testing of theories across various contexts and contributes to the development of universally applicable theories by allowing an evaluation

38

3

Methodology

of the significance of a specific phenomenon. Besides the generalisation benefits, comparative analysis allows for the relativisation of findings since researches are prevented from over-generalising their studies based on their experiences, beliefs, and challenges (Esser & Vliegenthart, 2017). For this research, the comparative design might be accomplished in the context of a quantitative approach while data for each case is being collected within a cross-sectional design format. In order for this research to contribute to cumulative development of knowledge and theory, the research method in Germany, Poland, Spain, and Sweden must explore the same phenomenon, namely women entrepreneurship, pursue the same research goal, adopt equivalent research strategies, ask the same set of standardised questions, and select the same theoretical focus and the same set of variables. In order to fulfil all these prerequisites for successful comparative analysis, the research method used in this research draws upon data from the GEM project, which represents a cross-national harmonised database of entrepreneurship.

3.4

Data Source, Population, and Sample

The empirical research builds on data from the GEM project. The GEM research programme, initiated in 1999 with only 10 participating countries, provides the required knowledge to understand the link between entrepreneurship and national growth and by compiling relevant data on an annual basis. Since 1999 a growing number of countries have joined the GEM project reaching more than 100 economies around the world in 2019 (Bosma & Kelley, 2019). This unique research initiative has been assembled to facilitate cross-national comparisons in the level of national entrepreneurial activity, estimate the role of entrepreneurial activity in national economic growth, determining the factors that account for national differences in the level of entrepreneurship, and facilitating policies that may be affecting in enhancing entrepreneurship (Reynolds et al., 2005). The GEM data set is suitable for this research since it represents a comprehensive source of information that enables the analysis and understanding of drivers and barriers for women entrepreneurs and how these differ across countries. Benefits of GEM data are among others a high degree of sample quality, comprehensibly prepared data, high level of available socio-demographic and variables and years and reliable representation of the sampled population. Moreover, the

3.4 Data Source, Population, and Sample

39

GEM data has been analysed in recent research1 which indicates that the data source is reliable and scientific research can be conducted on its basis. The GEM project consists of two research programmes. On the one hand, the GEM project consists of the National Expert Survey (NES) which envisions characterisation of countries in terms of nine dimensions, regarded as entrepreneurial framework conditions. This survey is conducted with experts who are selected based on their reputation and experience and its main focus is to evoke an open discussion of their views of the national strengths and weaknesses as a context of entrepreneurship and, furthermore, what policies would stimulate the level of entrepreneurship in their country (Reynolds et al., 2005). On the other hand, the Adult Population Survey (APS) represents the second research programme part of the GEM project. APS builds on an adult population (18–64 years old) survey that focuses on entrepreneurial behaviour, attitudes, activities, and aspirations of individuals (Giotopoulos et al., 2017). Since the purpose of this research is to understand how perceived drivers and barriers at the individual and social context of women affect their entrepreneurial intentions, the APS dataset has been selected to conduct the empirical analysis. In the latest available GEM adult population survey, which was in 2015, over 180,000 individuals in 60 countries were interviewed about their entrepreneurial activities and their attitudes towards entrepreneurship. The normal minimum sample is 2,000 adults (Reynolds et al., 2005). Since Germany, Poland, Sweden, and Spain are advanced countries where the majority of the population live in households that have landline phones, the surveys are completed by telephone. Generally, the households are contacted at random and the phone call is placed on a weekday night or during the day on the weekend. The first adult in the household who answers the call is asked to be a participant of the survey; in some cases, an adult would be randomly selected to represent the household for the survey (Reynolds et al., 2005). In order to enhance the confidence in the extent to which the samples represent the populations, all case weights are adjusted for all participant countries using an estimation of the age and gender structure of each country that is standardised by following the US Census International Population Data Base. Once all separate datasets for the participating countries were checked and harmonised and all survey respondents have been assigned the final weights, the files containing country-specific data were consolidated into a singer database. Moreover, all survey respondents are assigned a unique identification number, and,

1 A comprehensive list of studies analyzing the GEM data can be found at https://www. gemconsortium.org/research-papers

40

3

Methodology

for convenience reasons, the first digits and the appropriate international phone codes are set for tracking different countries (Reynolds et al., 2005). In this research, the focus is on women aged 18–64 years old who took part in the latest GEM survey in Germany, Poland, Sweden, and Spain. The latest GEM adult population survey which gathered data from 2015 was made available on February 2019. The total size of the sample is 17,451. Table 3.1 provides an overview of total national samples for 2015 and the details of the sampling procedure for the most recent sample in Germany, Poland, Sweden, and Spain. Table 3.1 GEM National Adult Population Surveys: Sample Size and Procedures Country

Total sample 2015 (women)

Sampling, procedure

Interview procedure

Responsible entity for data collection

Germany

1,906

RDD phone

CATI method

Institute of Economic and Cultural Geography, Leibniz Universität Hannover/RKW Kompetenzzentrum

Poland

1,005

RDD phone

CATI method

Polish Agency for Enterprise Development/University of Economics in Katowice

Spain

12,065

RDD phone

CATI method

UCEIF Foundation-CISE/GEM Spain Network

Sweden

2,475

RDD phone Random from personal register

CATI method

Swedish Entrepreneurship Forum

Total sample

17,451

Source: Based on Reynolds et al., 2005 & data from GEM Adult Population Survey 2015 Note: “RDD Phone” refers to phone interviews completed by calls to randomly created landline phone number to locate residential phone numbers without reference to published lists. “CATI” refers to computer-assisted telephone interviewing

3.5 Variable Description

3.5

41

Variable Description

The empirical study aims at identifying significant variables that contribute to estimating the likelihood of a women intention to start a new business within the next three years. The specific variables used to measure the concepts that have been developed in the literature section are further discussed. Dependent variable. Entrepreneurial intention of women (intention) is the dependent variable of interest. Intentions, which are deeply grounded in psychological antecedents (Hindle, Klyver, & Jennings, 2009), represent the individual’s cognitive state that anticipates action and explains the ability of individuals to perform the behaviour (Ajzen, 1991; Krueger, 2000). In this research, entrepreneurial intention of women is measured as a dummy variable that takes the value of 1 if the respondent of the GEM Adult Population Survey has an affirmative answer to the question “Within the next 3 years, do you expect to start alone or with others a new business, including any type of self-employment?” and 0, otherwise. This dichotomous variable has been used by various scholars who use the GEM database as a basis for their empirical research (e.g. Klyver, Nielsen, & Evald, 2013; Wennberg Pathak, & Autio, 2013; Schmutzler et al., 2018). Hence, the approach to measure entrepreneurial intentions by a single item has been extensively acknowledged in the entrepreneurship literature has been widely accepted (Schmutzler et al., 2018) since a single-item measure might be sufficient for concepts that are determined in an explicit and narrow way. With regard to expectation measurement, the chosen dependent variable is suitable to capture the construct (Wanous, Reichers, & Hudy, 1997) and the intention measure used in this research is appropriate for the objectives of the study. Independent variables. Eight independent variables of interest will be used during all of the regression analysis within this research, as they have been identified as significant determinants of entrepreneurial intentions. These are fear of failure (fearfail), lack of entrepreneurial opportunities (opport), lack of self-confidence (susskill), unattractive career path (Nbgoodc), knowing a nascent entrepreneur (knowent), high social status (nbstatus), media (nbmedia), and perceived ease of starting a business (easystart). The first four variables represent the women’s perceptions of barriers that are encountered when women intend to start a business whereas the other four variables represent the perceptions of drivers that support women’s intentions. The independent variables vary at the individual and social level. Fear of failure (fearfail) is represented by a dummy variable that takes the value of 1 if the respondent confirms that the fear of failure would prevent her from starting a business and 0, otherwise. Since women exhibit a more risk-averse behaviour than men, a high fear of failure is expected to lower

42

3

Methodology

their propensity to start a business. Lack of entrepreneurial opportunities (opport) represents the most distinctive and fundamental aspect of entrepreneurship. This is represented by a dummy variable that takes the value of 1 if the respondent does not expect good opportunities in her area, and 0 otherwise. Low self-confidence (susskill) is measured as a dummy variable and depicts women’s confidence in their skills, knowledge, and experiences to start a business. This dummy variable takes the value of 1 if the respondent believes she has the necessary skills, knowledge, and experiences to start her own business and 0, otherwise. Unattractive career path (nbgoodc) represents a dummy variable that takes the value of 1 if respondents’ perception that in their country, most people consider starting a new business a desirable career choice for women is a negative one and 0, otherwise. Knowing a nascent entrepreneur (knowent) is used as a proxy for the social network of the individual (Schmutzler et al., 2018) and takes the value of 1 if the respondent knows someone involved in entrepreneurial activities and 0, otherwise. High social status (nbstatus) represents a dummy variable and takes the value of 1 if the respondent associates the career of entrepreneurship with high social status and 0, otherwise. Media (nbmedia) is expected to increase the propensity of women to start a business if success stories of entrepreneurs are presented in the television, radio, and social media. This construct is depicted by a dummy variable that takes the value of 1 if the respondent agrees that in their country, there will often be seen stories in the public media about successful new businesses and 0, otherwise. The existing regulatory environment does not have a direct influence on the entrepreneurial intention, but rather how individuals perceive the ease of doing business in a specific country. In this way, the ease of starting a business (easystart) is represented by a dummy variable that takes the value of 1 if the respondent agrees that in her country it is easy to start a business and 0, if otherwise. Control variables. The use of control variables is compelled by the empirical test of the proposed hypotheses. Three control variables are included: age (age), education level (GEMEDUC), and annual income level (GEMHHINC). To measure the educational level (GEMEDUC), respondents were asked about their educational studies using a five-category variable from primary, some secondary, secondary degree, post-secondary, and graduate level. Scholars have found a positive relationship between education and entrepreneurial intention (see e.g. Schmutzler et al., 2018)—an educated individual is more informed and has more options regarding their career, which in turn is likely to affect their entrepreneurial intention. Since this variable plays a significant role for entrepreneurs (Ahmad, Xavier, & Abu Bakar, 2014; Schmutzler et al., 2018), it is expected that level of education will increase the women’s likelihood of starting a business. The annual

3.5 Variable Description

43

income level (GEMHHINC) represents the third control variable. The relationship between income and entrepreneurial intention has been the topic of many studies and the general pattern found is that individuals who are in higher ranges of income are more likely to exhibit entrepreneurial intentions (Ahmad et al., 2014) A summary of the definitions and types of the dependent, independent, as well as control variables, can be seen in Table 3.2. The variables which are used during the analysis stem from data of the GEM Adult Population Survey 2015 and are representative of women within Germany, Poland, Spain, and Sweden. Table 3.2 Description of Variables Variable

Description

Type

Dependent variable Entrepreneurial intention

Respondents were asked whether they intend to start a business within three years

Binary 1 = yes 0 = no

Independent variables Fear of failure

Respondents were asked whether fear of failure would prevent them from setting up a business or not

Binary 1 = yes 0 = no

Lack of entrepreneurial opportunities

Respondents were asked whether there will there Binary be good opportunities for starting a business in the 1 = no 0 = yes area where they live

Lack of self-confidence

Respondents answered if they believed they had the required skills and knowledge to start a business

Binary 1 = no 0 = yes

Unattractive career path

Respondents’ perception that in their country, most people consider starting a new business a desirable career choice

Binary 1 = no 0 = yes

Knowing a nascent entrepreneur

Respondents were asked whether they personally knew someone who had started a business in the two years preceding the survey

Binary 1 = yes 0 = no

High perceived social status

Agreement with the statement that in their country, those successful at starting a new business have a high level of status and respect

Binary 1 = yes 0 = no

Media

Agreement with the statement that in their country, they will often see stories in the public media about successful new businesses

Binary 1 = yes 0 = no (continued)

44

3

Methodology

Table 3.2 (continued) Variable

Description

Type

Perceived ease of starting a business

Agreement with the statement that in their country, it is easy to start a business

Binary 1 = yes 0 = no

Control variables Age

Range of age at the time of the interview, the Continuous respondents were asked to identify their age range.

Education level

Respondents were asked to provide the highest education level they had gained. The GEM coordination unit harmonises responses across all countries into a seven-category variable

Categorial primary, some secondary, secondary degree, post-secondary, graduate level

Annual income level

Respondents were asked to provide information about their annual household income

Categorial lower, middle, upper income group

Source: Own research with data from GEM Adult Population Survey 2015

3.6

Data Analysis

Since the dependent variable is dichotomous (Intention), the empirical study can apply either a linear probability model, a binary logistic regression (logit model), or a probit model (Horowitz & Savin, 2001). However, the linear probability model has a considerable deficiency since it assumes the conditional probability function to be linear and it does not restrict probabilities to lie between the interval [0,1]. Therefore, an approach that uses a nonlinear function to model the conditional probability function of a binary dependent variable is required to be adopted (Harrell, 2015). According to Horowitz and Savin (2001), probit and logit regressions are the most used widely used models in the case of binary dependent variables. Hence, probit and logit regressions deliver only approximations to the unknown population regression function E(Y|X). Even though these two models capture the nonlinear nature of the population regression better than the linear probability model and their predictions of probabilities lie between 0 and 1, their degree of interpretation is more complex (Mood, 2009). While the logit model is formed

3.6 Data Analysis

45

on the logistic cumulative distribution function (CDF), the probit model uses the normal CDF (Agresti & Tarantola, 2018). However, the choice of which model and implicitly which continuous probability distribution to use in the empirical analysis cannot be supported by theoretical grounds (Amemiya, 1985). Since previous studies with similar objectives have used a logit model as a technique in the entrepreneurship field (e.g. Ahmad et al., 2014; Giotopoulos et al., 2017; Schmutzler et al. 2018), the logit model is selected in this empirical study. Finally, this model enables the identification of variables which are the most important in categorising potential women entrepreneurs from those who do not exhibit any entrepreneurial intention. A logit model is applied when a dichotomous dependent variable is explained with the empirical specification defined in terms of a latent response variable, say y ∗ (Harrell, 2015). The latent variable stands for the propensity of individuals i to open a business and is defined by the following linear relationship: yi∗ = β0 +

K k=1

βki xki + εi

(1)

with i denoting the respondent and xki : k = 1 through K independent variables that explain the phenomenon for respondent i βk : vector that indicates the effect of xk on y ∗ β0 : constant that indicates the expected value of y ∗ when all xk equal to zero εi : random error term for respondent i. The variable y ∗ is a non-observable latent variable and represents the outcome of the observed binary variable yi where:

yi =

⎧ ∗ ⎪ ⎨ 1 i f yi > 0, and ⎪ ⎩0

(2) other wise

Dealing with the propensity of women to open a business within the next three years, (2) is to be interpreted as:

I ntention i = yi =

⎧ ⎪ ⎨ 1,

i f the r espondent i is intending to open a business

⎪ ⎩ 0, i f the r espondent i is not intending to open a business (3)

46

3

Methodology

In the case of binary dependent variables, the logit model concerns mainly the following probability: p(x) = P(y = 1|x)

(4)

where x represents the parameter of the independent variables. In the context of this research, the factors that inhibit or support the intention of women to open a business within the next year are the main focus. Therefore, a link function F can be used to express the model, which is defined by the following equation: P(y = 1|x) = F(xβ + u)

(5)

Equation (5) is a general formulation which can be suitable for both logit and probit models to estimate Equation (1). Implicit in the logit model is the assumption that the cumulative distribution function for the error term follows the logistic cumulative distribution. Moreover, the logit model is estimated by using the maximum estimation method and the estimated coefficients are interpreted in a way that differs from linear regression models (Smithson & Verkuilen, 2006). The selection of variables has been discussed in Section 3.5. and they have been modelled in the regression as categorical variables. A measure that indicates the goodness-of-fit of the logit model is the percentage of observations that are correctly predicted by the model (Greene, 2008). Other measures that assess its goodness-of-fit are the test for model coefficients, the Hosmer-Lemeshow test (Fagerland & Hosmer, 2016), and the pseudo −R2 statistics (Hagle & Mitchell, 1992). Wald statistics were used to test the significance of independent variables. In order to avoid biased estimations of the coefficients, a collinearity analysis is performed by using the variance inflation factor (VIF) and Spearman correlation matrix (Midi, Sarkar, & Rana, 2010).

3.7

Quality of Research

Validity and reliability are the criteria that need to be assessed when evaluating the measurement instrument of this research. Since this study is conducted using data from the GEM project, the validity and reliability of this study need to be secured. According to Reynolds et al. (2005), the validity and reliability of the GEM survey meet contemporary standards. The validity of a study is defined as the extent to which the adopted research instrument correctly measures the concept that it is under study (Bryman, 2016).

3.7 Quality of Research

47

The GEM project is based on employing the same survey research methodology across a wide range of countries to identify those individuals active in new venture creation (Reynolds et al., 2005). Moreover, almost all operational definitions of entrepreneurship connected to new venture creation can be implemented with the GEM dataset. Finally, the research design adopted in this study allows general estimates of women’s entrepreneurial intentions across Germany, Poland, Spain, and Sweden. The value of this study is related to the harmonised approximations that are provided. Moreover, validity refers to the degree of generalisation of the study’s findings (Saunders et al., 2016). According to Reynolds et al. (2005), when the estimates of new venture creation generated by the GEM survey are compared to existing national statistics, it is clear that both sources reflect the same phenomena and, as a result, the GEM measures appear to match the official data from national governments. In this line, it can be concluded that the study is a general representation of potential women entrepreneurs in the selected countries and the regions they represent. In quantitative research, reliability comprises the results’ consistency, stability, and repeatability. Particularly, the results of the study are considered reliable if consistent results would be obtained in identical situations (Saunders et al., 2016). In this way, errors and bias are minimised and the analytical process of this study can be followed and replicated. Thus, this study describes the methodology process in detail which includes the applied methods, data collection and analysis. Moreover, the reliability of entrepreneurial intention rates depends on the sample size (Bergman, Mueller, & Schrettle, 2014). In this line, GEM demands a minimum of 2000 respondents in order to ensure high accuracy of results. However, to achieve a representative sample, the GEM questionnaire is rather short and includes single-item constructs that are measured on a dichotomous scale. This approach is well suited when the survey is administered to a wide range of different populations, this being the case of the GEM project (Diamantopoulos, Sarstedt, Fuchs, Wilczynski, & Kaiser, 2012). However, scholars argue that singleitem constructs lack validity since they cannot capture the whole complexity of a construct. Despite this backdrop, the reliability of the study can be assured even when it deals with single-item measures. Scholars have argued that “one or two good items that elicit appropriate respondent behaviour will yield better information than multiple, poorly presented items that increase the error term correlations and/or stimulate inappropriate response styles” (Drolet & Morrison, 2001, p. 199). Moreover, to increase the reliability of the GEM data, observations are weighted to assure that the joint distribution of age and gender matches the distribution of the reference population (Peroni, Riilo, Rodrigues, 2017).

4

Results

This chapter outlines the results of the empirical analysis. Firstly, descriptive statistics of the dependent as well as the independent variables are presented. The hypotheses H1 and H2 which have been developed through a literature review were tested using a logit model. Separate regression models for Germany, Poland, Spain, and Sweden, were built and the logistic regression models across the countries were compared since the aim of the research was to test whether various predictive variables differed significantly as a function of the country. Since the independent variables were measured at the nominal level, the Spearman correlation coefficient, which is a non-parametric version of the Pearson correlation coefficient, has been used to analyse the relationships among variables and along with the VIF scores to test for multicollinearity. To assess the adequacy and fit of the multivariate model, the overall rate of correct classification of cases was examined and the goodness of fit of the models was assessed using the likelihood ratio test, Pearson Chi-square, and the Hosmer-Lemeshow test. The Nagelkerkestatistics were conducted which indicated the variance explained by our models. Hypothesis H4 is tested using a Kruskal-Wallis test (Sheskin, 2011).

4.1

Descriptive Statistics

Descriptive statistics of the dependent as well as independent variables can be seen in Table 4.1. Firstly, women in Western Europe show a low entrepreneurial intention score (M = .07, SD = .24) and perceive lack of entrepreneurial opportunities (M = .40, SD = .49) and lack of self-confidence (M = .33, SD = .47) as the highest barriers. On the other hand, the driver status (M = .79, SD = .40) scores the highest among the four regions while knowing a nascent entrepreneur © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_4

49

50

4

Results

(M = .23, SD = .42) scores the lowest among the four regions. The primary barriers in Eastern Europe, the region with the highest intention of women to start a business within the next three years, are lack of entrepreneurial opportunities (M = 31, SD = 46), lack of self-confidence (M = .48, SD = 50), and fear of failure (M = .68, SD = .46) which records the highest score among the four regions. These barriers are partially balanced by the drivers high perceived status (M = .59, SD = .49) and media (M = .52, SD = .50). In Southern Europe, which has the second lowest intention rate of women to open a business, the most prominent barriers are lack of self-confidence (M = .49, SD = .42) and lack of entrepreneurial opportunities (M = .23, SD = .49) whereas the most prominent drivers are high perceived status (M = .48, SD = .46) and media (M = .47, SD = .50). The Northern European region, however, experienced the lowest intention of women to start a business (M = .05, SD = .20). This region is further characterised by the lowest score in lack of self-confidence (M = .24, SD = 42) and the highest score in the driver media (M = .67, SD = 47) over the four regions. Furthermore, women from Northern Europe perceive that in their country, those successful at starting a new business have a high level of status and respect (M = .44, SD = .44). Across Europe (all four regions) lack of entrepreneurial opportunities (M = .29, SD = .45) and lack of self-confidence (M = .38, SD = .48) were the biggest inhibitors while high perceived status (M = .56, SD = .49) and media (M = .50, SD = .50) were regarded as the most prominent drivers. To assess whether the effects of drivers and barriers to start a business on the women’s entrepreneurial intentions differ among countries, a Kruskal-Wallis test was conducted. The results in Table 4.1 demonstrate that there is a significant difference in the dependent and independent variables in connection with the four countries in the analysis (all p < .05).

.53

.33

.40

.49

.23

.79

.50

.61

susskill

opport

nbgoodc

knowent

nbstatus

nbmedia

easystart

.48

.50

.40

.42

.50

.49

.47

.49

.25

.18

.52

.59

.39

.58

.31

.48

.68

.18

.55

.50

.49

.48

.49

.46

.50

.46

.38

SD

.03

.47

.48

.31

.53

.23

.40

.49

.06

M

Spain 4,865

.49

.50

.46

.49

.42

.49

.42

.50

.23

SD

.13

.67

.74

.30

.51

.65

.24

.47

.05

M

Sweden 645

Source: Own research with data from GEM Adult Population Survey 2015 Note: M represents the mean; SD represents the standard deviation

.07

fearfail

M

M

SD

Poland 437

Germany 1,149

Intention

Country n

Table 4.1 Descriptive Statistics and Results of Kruskal-Wallis Test

.91

.47

.44

.45

.50

.47

.42

.49

.20

SD

.11

.50

.56

.31

.53

.29

.38

.50

.06

M

.67

.50

.49

.46

.49

.45

.48

.50

.24

SD

Pooled Countries 7,096

.000

.000

.000

.000

.000

.000

.000

.000

.000

Sig.

Kruskal-Wallis Test

4.1 Descriptive Statistics 51

52

4.2

4

Results

Multicollinearity

To test if multicollinearity exists within the regressions, the Spearman productmoment correlation matrix, as well as the VIF scores, are being calculated in the following subchapter. An indicator for multicollinearity is the Spearman product-moment correlation coefficient, denoted as r, whereby the value can range from −1 (denoting a perfect negative linear relationship) to 1 (denoting a perfect positive linear relationship). A strong correlation exists if |r| > .5, a moderate correlation if .3 < |r| < .5 and a small correlation if .1 < |r| < .3 (Cohen, 1988), whereby such a correlation only depicts a first look at the data. The issue of multicollinearity arises where there is a high degree of correlation between variables. According to Midi et al. (2010), multicollinearity can be problematic in logistic regression when the correlation coefficient between two regressors is greater than 0.8 or 0.9 (r > .8 and r > .9). Hence, multicollinearity does not negatively affect the model’s predictive power or reliability. It only reduces the calculations of individual predictors (Midi et al., 2010). Correlation matrixes for each country presented in Tables 4.2, 4.3, 4.4, and 4.5 are obtained from the SPSS output and the correlation coefficients among the explanatory variables can be used to identify the presence of multicollinearity. There were small correlations among the explanatory variables. In all four European countries there was a positive correlation between entrepreneurial intention to start a business and lack of self-confidence (Table 4.2: Germany: r = .138; Table 4.3: Poland r = .167; Table 4.4: Spain r = .127; Table 4.5: Sweden r = .184; all p < .01). In addition, across all four countries, knowing a nascent entrepreneur was positively related to women’s intention to start a business within the next three years (Table 4.2: Germany: r = .163, p < .01; Table 4.3: Poland r = .108, p < .05; Table 4.4: Spain r = .044, p < .05; Table 4.5: Sweden r = .217, p < .01). Fear of failure has a negative correlation with women’s intention to start a business within the next three years in two out of four countries. In Poland r = −.137 (p < .01) and in Germany r = −.101 (p < .01). Lack of entrepreneurial opportunities has a positive correlation with entrepreneurial intention in two out of four countries. In Poland r = 127 (p < .01) and in Germany r = .099 (p < .01). In Poland, Spain, and Sweden the unattractive career path has a positive correlation with entrepreneurial intention (Table 4.3: Poland r = .100, p < .05; Table 4.4: Spain r = .031, p < .05; Table 4.5: Sweden r = .139, p < .01). Only in Spain is the correlation between media and entrepreneurial intention significant (r = .046, p < .01). Moreover, the correlation between easy start and entrepreneurial intention is significant in Sweden (r = .091, p < .05). The correlation between high

4.2 Multicollinearity

53

perceived status and entrepreneurial intention of women is insignificant in all four countries. Household income has a negative correlation with entrepreneurial intention in one country out of four (Table 4.4: Spain r = −.053, p < .01). The correlation between education and entrepreneurial intention of women is insignificant in all four countries. Hence, in all four European countries there was a negative correlation between entrepreneurial intention and age (Table 4.2: Germany: r = −.173; Table 4.3: Poland r = −.261; Table 4.4: Spain r = −.045; Table 4.5: Sweden r = −.187; all p < .01). Another indicator for multicollinearity is the VIF score which describes the extent to which the standard error of the coefficient of interest is inflated upwards. As a rule of thumb, one can say if the VIF value exceeds 4, a problem with multicollinearity exists (Salmerón Gómez et al., 2016). Table 4.2 to 4.5 show that the VIF scores in women’s entrepreneurial intention regression are below 4. It can be observed that exploratory variables have high tolerances and their variance inflation factor is very close to 1. The Spearman correlation matrixes along with the VIF scores show that no evidence for multicollinearity can be found, which supports the fact that no severe problem with multicollinearity exists, as there were small correlations among the explanatory variables. Multicollinearity can be problematic in logistic regression, to the extent that one independent variable is a linear function of another independent variable. Thus, multicollinearity will generate high standard errors of the ß coefficients and unreliable interpretations of the relative importance of the independent variables. Generally, the problem of collinearity is manifested by extraordinarily large estimated standard errors and sometimes by large estimated coefficients (Hosmer & Lemeshow, 2000). Throughout the model-building process, the explanatory variables were examined for evidence of any problems arising from multicollinearity. Since the model coefficients and VIF scores were not particularly large, it can be concluded that multicollinearity is not a problem (Salmerón Gómez et al., 2016).

1.047 −0.041

1.182 −0.009

1.225

0.938

0.92

0.932

0.933

0.955

0.951

Nbstatus (7)

Nbmedia (8)

Easystart (9)

GEMHHINC(10) 0.846

0.817

Knowent (6)

GEMEDUC(11)

Age (12)

1.000

−0.035

−.173** 0.053

.165**

−0.019

0.047 −0.052

.196** −.061*

−.081**

−0.038

−0.040 .184**

0.055

−.101**

.136**

.159**

.145**

0.037

1.000

0.055

.065*

.173**

0.041

5

0.013

−0.007

.181**

4

0.030

0.036

1.000

−.113**

.075*

.130**

−0.021

6

.147**

.079**

0.007

.143**

1.000

−0.026

7

1.000 0.022

.124**

−.062*

−0.008

8

0.003

−.112**

−.108**

1.000

9

−0.048

.346**

1.000

10

−.075*

1.000

11

1.000

12

Source: Own research with data from GEM Adult Population Survey 2015 Notes: Spearman rank correlation (2-tailed significance in brackets); ** significant at the .01 level (2-tailed), * significant at the .05 level (2-tailed). Listwise N = 1,149

1.051

.094**

1.000 −0.004

3

−0.039

.152**

0.023

1.072 −0.043

−.084**

−0.036 0.048

.163**

0.034 0.005

1.073

1.087

1.066

−.116**

Nbgoodc (5)

1.104

.099**

0.906

Opport (4)

1.000 −.171**

.138**

2 −.101**

0.908

Subskill (3)

1.101

0.932

Fearfail (2)

1.073

1.000

1

Intention (1)

Tolerance VIF

Table 4.2 Spearman Correlation Matrix & VIF Values Germany

54 4 Results

1.085

1.085

1.070

0.881

0.847

0.871

0.875

0.910

0.921

0.937

Nbgoodc (5)

Knowent (6)

Nbstatus (7)

Nbmedia (8)

Easystart (9)

GEMHHINC(10) 0.921

0.934

Opport (4)

GEMEDUC(11)

Age (12)

0.024

0.078

0.091

0.049

0.060

−0.066

.108*

0.019

0.026

.220**

.309**

0.024

1.000

−0.074

−.124**

−0.012

5

1.000

−.144**

.162**

.184**

.153**

0.007

−0.070

6

0.006

.159**

1.000

0.003

−0.052

−0.061

7

1.000

0.043

−.120*

−0.061

−0.041

8

0.066

0.073

.170**

1.000

9

−0.086

.340**

1.000

10

−.135**

1.000

11

1.000

12

Source: Own research with data from GEM Adult Population Survey 2015 Notes: Spearman rank correlation (2-tailed significance in brackets); ** significant at the .01 level (2-tailed), * significant at the .05 level (2-tailed). Listwise N = 437

−0.030 .133**

0.073

.109*

0.066

.203**

.113*

−0.025

.198**

.128**

.160**

−0.043

−.103*

.124**

.171**

.112*

1.000

4

−0.043

−0.054

−0.087

.184**

−0.087

−.108*

.100*

1.000 0.058

−.133**

1.068 −.261**

1.099

1.143

1.148

1.180

1.135

3

.127**

1.000

1.109

0.950

0.901

Subskill (3)

−0.089

2

.167**

1.000

1

1.053 −.137**

Tolerance VIF

Fearfail (2)

Intention (1)

Table 4.3 Spearman Correlation Matrix & VIF Values Poland

4.2 Multicollinearity 55

0.956

0.944

0.951

0.884

0.813

0.909

Nbstatus (7)

Nbmedia (8)

Easystart (9)

GEMHHINC(10)

GEMEDUC(11)

Age (12)

−.045**

−.034*

.186**

−.051** −0.002

.099**

−.087**

−.053** 0.015

.098**

−.084**

0.016

−0.008 .058**

.056** 0.001

.046**

−0.009

.146**

−.034*

.115**

.135**

.185**

.123**

.058**

.141**

.078**

1.000

4

1.000

.044**

−.046**

−.045**

.042**

.160**

.172**

−0.022

5

1.000

−.101**

.149**

.050**

.046**

.039**

−0.016

6

.059**

.097**

1.000

0.026

−.042**

−0.004

7

.092**

.048**

0.028

.064**

1.000

8

−.028*

0.000

.031*

1.000

9

−0.025

.392**

1.000

10

−.196**

1.000

11

1.000

12

Source: Own research with data from GEM Adult Population Survey 2015 Notes: Spearman rank correlation (2-tailed significance in brackets); ** significant at the .01 level (2-tailed), * significant at the .05 level (2-tailed). Listwise N = 4,865

1.100

1.229

1.131

1.052

1.059

1.046

1.060

−.036*

.044**

0.944

.106** −0.013

.039**

Knowent (6)

1.063

.031*

0.940

1.000

.127** −.062**

Nbgoodc (5)

1.100

1.000

3

0.010

0.909

Opport (4)

2

−.194**

−0.014

1.053

0.908

Subskill (3)

1.102

0.949

Fearfail (2)

1 1.000

VIF

Intention (1)

Tolerance

Table 4.4 Spearman Correlation Matrix & VIF Values Spain

56 4 Results

0.899

0.906

0.925

0.912

0.971

0.924

Opport (4)

Nbgoodc (5)

Knowent (6)

Nbstatus (7)

Nbmedia (8)

Easystart (9)

0.014

−.171**

.164**

.173**

.158**

.077*

.081*

.148**

.111**

1.000

4

−.188**

−.095*

−.112**

0.055

(0.048)

.209**

0.012

1.000

5

1.000

−.261**

.090*

.143**

.137**

0.026

−0.024

6

.142**

1.000

−0.061

−0.036

0.003

−0.037

7

9

1.000

0.031

−.134**

0.003 −0.055

0.050 −0.016

0.018

1.000

8

−.085*

.279**

1.000

10

12

−0.034 1.000

1.000

11

Source: Own research with data from GEM Adult Population Survey 2015 Notes: Spearman rank correlation (2-tailed significance in brackets); ** significant at the .01 level (2-tailed), * significant at the .05 level (2-tailed). Listwise N = 645

1.093

0.915

−.120**

−.187**

Age (12)

0.074

−0.055

0.049

1.122

0.891

GEMEDUC(11)

.172** .116**

−.128**

−0.009

−0.017

1.139 −0.007

.091*

GEMHHINC(10) 0.878

1.082

0.063

−0.003

−0.013 .165**

.190**

.217** 0.055

.089*

.115**

.093*

1.000

−0.019

1.000

3

−.205**

2

.139**

0.061

1.029 −0.020

1.096

1.082

1.103

1.112

.184**

0.883

Subskill (3)

1.133

1.000

1.093 −0.025

0.915

Fearfail (2)

1

Intention (1)

Tolerance VIF

Table 4.5 Spearman Correlation Matrix & VIF Values Sweden

4.2 Multicollinearity 57

58

4.3

4

Results

Adequacy and Fit of the Models

To test whether the logit models are plausible and robust, robustness checks are conducted. These checks further increase the likelihood of structural validity of the results. The overall percentage of cases correctly classified by the regression analyses was high for the four models (78.5% < % < 93.6%). However, sensitive analysis of the classification tables reveals that the models have greater difficulty in predicting the cases of women intending to start a business and the models do not notably help in predicting such cases (sensitivity varying between 0% and 3.8%). Even though the overall variance correctly explained appears to be good, consider that blindly estimating the most frequent category (not trying to start a new business) for all cases would generate a correct percentage of 92.5% in Germany, 78.5% in Poland, 93.6% in Spain, 91.8% in Sweden for the pool countries 92.3%. However, the four models help only a minuscule amount since the classification is sensitive to the relative size of the two-component groups and always favours classification into the larger group. In this research, the differences among the component groups are particularly large, because the occurrence of entrepreneurial activity in all countries is low. Therefore, the fit of the models is further tested with more rigorous methods of assessment. The goodness of fit of the models is further assessed using the Pearson Chisquare, the Hosmer-Lemeshow test, and the likelihood ratio test (Fagerland & Hosmer, 2016). The tests’ results are illustrated in Table 4.6. The likelihood ratio test was conducted to determine whether the full models containing all exploratory variables are better at predicting correct classifications than the reduced model containing only the constant term. For Model Germany, Model Poland, Model Spain, Model Sweden, and Model Pooled Countries the likelihood test statistics are statistically significant at p < .001. Thus, the full models are better than the reduced models containing only the constant term. Furthermore, the five models are significant according to the Chi-square values (all models at p < .001). Concerning the Hosmer-Lemeshow test, the appropriateness of the p-value depends on the validity of the assumption that the estimated expected frequencies are larger than p > .05. Examining the contingency table, the Hosmer-Lemeshow test reveals that for all four countries the test values are statistically insignificant (Table 4.6: Germany p = .157, Poland p = .189, Spain p = .999, Sweden p = .219, Pooled Countries p = .985), indicating that the models do not have a poor fit. Nagelkerke statistics have been further analysed, which indicate the variance explained by our models. Overall, the predictive power of individual models is good. The Nagelkerke R2 has a value of .185 in Model Germany, .225 in Model

4.3 Adequacy and Fit of the Models

59

Poland, .067 in Model Spain, .234 in Model Sweden, and .110 in Model Pooled Countries. Table 4.6 Model Fit Statistics nχ 2

d.f.

p

Germany (n = 1,149; N = 1,906) Pearson Chi-Square

91.151

11

.000

Hosmer and Lemeshow test

11.878

8

.157

Pearson Chi-Square

69.152

13

.000

Hosmer and Lemeshow test

11.239

8

.189

125.150

11

.000

.881

8

.999

Pearson Chi-Square

68.830

11

.000

Hosmer and Lemeshow test

10.707

8

.219

333.735

12

.000

1.858

8

.985

Nagelkerke = .185 Correct classification = 92.5% Poland (n = 437, N = 1,005)

Nagelkerke = .225 Correct classification = 78.5% Spain (n = 4,865, N = 12,065) Pearson Chi-Square Hosmer and Lemeshow test Nagelkerke = .067 Correct classification = 93.6% Sweden (n = 645, N = 2,475)

Nagelkerke = .234 Correct classification = 91.8% Pooled Countries (n = 7,096; N = 17,451) Pearson Chi-Square Hosmer and Lemeshow test Nagelkerke = .110 Correct classification = 92.3% Source: Own research with data from GEM Adult Population Survey 2015

60

4.4

4

Results

Results of the Multiple Logistic Regression Analysis

The results of the logistic regression models are discussed in greater detail. Table 4.7 and 4.8 present the results of the regression analyses testing the hypotheses. The fitted model on the pooled data is reported in Table 4.8 and the separate country models are reported in Table 4.7. In these tables, for each covariate, the estimates of the standard errors of the estimated coefficients, the two-tailed significance, and the odds ratio are reported. The purpose of this study is to see if the coefficients on the dummy variables for perceived barriers and drivers are significant and whether they are positive or negative. A negative and significant dummy variable means that women with this attribute experience a lower entrepreneurial intention. For instance, a woman faces a significantly lower barrier level, when the coefficient of the dummy variable for barriers is negative significant. When none of the coefficients is significant, it implies that there is no significant difference between the likelihood of opening a business. It is important to mention, that various scholars heavily criticise in recent papers the significance level and therefore the p-value as a value of implied truth. Researchers, for instance, conduct p-hacking1 or apply very liberal alpha levels in their testing of hypotheses (Branch, 2019). The drivers and barriers measure within the women’s entrepreneurial intention regression, however, is highly significant, thus if even a small alpha level of below .05 is used, the variable remains significant. Looking at the Germany Model, fearfail, susskill, opport, knowent, and age are statistically significant at the level of 5% while nbgooc, nbstatus, nbmedia, and easystart do not have a significant relationship with women’s entrepreneurial intention. Fearfail, susskill, and opport have a negative relationship while the variable knowent exhibits a positive relationship with women’s intention to open a business. If the respondent exhibits fear of failure, entrepreneurial intention of women decreases by .524 units on average. Furthermore, if lack of self-confidence increases by one per cent, entrepreneurial intention of women decreases by .989 units on average. If the respondent does not see any good opportunities for starting a business in the area where they live, their entrepreneurial intention decreases by .492 units on average. However, by knowing someone who has started a business during the past two years, women in Germany are with .823 units more intending to open a business. Like in the other three Models, age exhibits a negative 1 This

means that a scholar dredges the data until the estimated coefficient is just significant.

4.4 Results of the Multiple Logistic Regression Analysis

61

Table 4.7 Results of Multiple Logistic Regression Variable fearfail

susskill

Opport

Germany

nbstatus

nbmedia

easystart

GEMHHINC

(.271)

(.122)

(.335)

[.592]

[.555]

[1.040]

[.829]

−.989*

−1.150*

−1.062*

−.982*

(.256)

(.281)

(.130)

(.330)

[.372]

[.317]

[.346]

[.375]

.046

.096

−.492*

−.165

−.414 (.280) [661] −.351

.039

−.187

(.124)

(.381)

[1.047]

[1.101]

−.230*

−.841* (.363)

(.246)

(.296)

(.122)

[.848]

[.704]

[.794]

[.431]

.823*

.003

.194

1.245*

(.251)

(.272)

(.124)

(.347)

[2.278]

[1.003]

[1.214]

[3.474]

−.053

.160

−.119

.447

(.302)

(.284)

(.121)

(.399)

[.948]

[1.174]

[.888]

[1.563] −.218

−.239

.196

.339*

(.247)

(.268)

(.122)

(.331)

[.788]

[1.217]

[1.403]

[.804]

−.194

.324

.026

.183

(.163)

(.270)

(.109)

(.258)

[.824]

[1.382]

[1.027]

[1.201]

.053

−.347*

−.109

(.169)

(.083)

(.204)

[.707]

[.897]

−.277 (.163) [.758]

GEMEDUC

Sweden

(.249)

[.612]

knowent

Spain

−.588*

(.249) nbgoodc

Poland

−.524*

.102

[551.0] −.065

.048

.404

(.198)

(.230)

(.080)

(.299)

[1.108]

[.937]

[1.049]

[1.498] (continued)

62

4

Results

Table 4.7 (continued) Variable age

Constant

Germany −.058*

−.063

Spain

−.033* (.011)

(.011)

(.005)

[.944]

[.939]

[.983]

1.168

2.215

1,149

(.871) 437

Sweden

−.017*

(.011)

(.751) n

Poland

−1.148* (.358) 4,865

[.967] −1.852 (1.025) 645

Source: Own research with data from GEM Adult Population Survey 2015 Notes: Overall rate of correct classification: 92.5% in Germany, 78.5% in Poland, 93.6% in Spain, 91.8% in Sweden; Dependent variable: Intention to start a business; Coefficients reported. Standard error of coefficient in parentheses. Odds ratio in brackets. *significant at the .05 level

but significant relationship with entrepreneurial intention of women, thus with an increase of one per cent in age, women’s intention to open a business decreases by .058 units. As shown in Table 4.7, in the Poland Model the variables fearfail, susskill, age, and the constant are statistically significant at the 5% level. Thus, if the barriers increase by one per cent, women’s entrepreneurial intention decreases by .001 units on average. Fear of failure and women’s entrepreneurial intention have a negative relationship, thus when fear of failure increases by one per cent, entrepreneurial intention of women decreases by .588 units. Lack of self-confidence has a negative significant relationship with entrepreneurial intention of women; if lack of self-confidence increases by one unit, women’s entrepreneurial intention decreases by 1.150 units. Age and women’s entrepreneurial intention as well has a negative relationship, thus when age increases by one per cent, female happiness decreases by .063 units. Even though barriers opport and nbgoodc do not show any significant impact on women’s entrepreneurial intention to open a business, the variables do show a negative relationship with entrepreneurial intention. Drivers knowent, nbstatus, nbmedia, and easystart do show a positive relationship with entrepreneurial intention, however this one is insignificant. Table 4.7 presents the Spain Model where the variables susskill, nbgoodc, nbmedia, GEMHHINC, age, and the constant show a statistically significant relationship with women’s intention to start a business. The barriers susskill and nbgoodc show a negative relationship with entrepreneurial intention of women at the 5% level. In this line, when women believe they do not have the required

4.4 Results of the Multiple Logistic Regression Analysis

63

skills and knowledge to start a business or do not consider starting a new business a desirable career, their entrepreneurial intention decreases by 1.062 units, respectively by .230. The driver nbmedia, however, shows a positive and statistically significant relationship with women’s entrepreneurial intentions. Thus, if women will often see stories in the public media about successful new businesses, their intention to open a business has an increase of .339. Age presents a negative but statistically significant relationship with entrepreneurial intention of women, thus when age increases by one per cent, the intention of women to open a business decreases by .017 units. Moreover, when women’s income increases by one per cent their intention to open a business decreases by .347 units. As shown in Table 4.7, in the Sweden Model the variables susskill, nbgoodc, knowent, and age are statistically significant at the 5% level. The barriers susskill and nbgoodc have a significantly negative relationship. If lack of self-confidence increases with one unit, women’s entrepreneurial intention decreases by .982 units. If women’s perception that in their country, most people do not consider starting a new business a desirable career choice increases by one unit, their entrepreneurial intention decreases by .841 units. The driver knowent has a significantly positive relationship with women’s entrepreneurial intention. If women know a nascent entrepreneur their intention of opening a business increases by 1.245 units. As in in Poland Model, age has a significantly negative relationship with women’s entrepreneurial intention, thus when age increases by one per cent, the intention of women to open a business decreases by .033 units. Moreover, the barriers fearfail and opport do show a negative relationship with the intention of women to start a business, this is statistically insignificant. On the other hand, the drivers nbstatus and easystart show a positive but statistically insignificant relationship with women’s entrepreneurial intention while nbstatus shows a negative but statistically insignificant relationship. The results of the regression analysis for the pooled countries involving all collected data are presented in Table 4.8. They show that the variables susskill, nbgoodc, knowent, nbmedia, GEMHHINC, age, Country, and the constant are statistically significant at a level of 5%. Furthermore, it shows that control variables GEMHHINC and age are both significant at a level of 5%. Susskill and nbgoodc have a negative relationship with intention of women to start a business. If women believe they do not have the required skills and knowledge to start a business, then their intention to open a business decreases with 1.077 units, whereas if women have the perception that in their country, most people do not consider starting a new business a desirable career choice, their intention decreases by .348 units. However, knowing an entrepreneur and having the perception that those successful at starting a new business have a high level of status and respect, the intention

64

4

Results

Table 4.8 Results of Multiple Logistic Regression: Pooled Countries Variables

Coeff.

Std. Err.

Z

Df

p

Odds Ratio

95% C.I. for EXP(B) Lower

Upper

Fearfail

−.101

.094

1.151

1

.283

.904

.752

1.087

Susskill

−1.077

.099

118.296

1

.000

.341

.281

.414

Opport

−.094

.102

.849

1

.357

.911

.746

1.111

Nbgoodc

−.348

.096

13.238

1

.000

.706

.585

.852

Knowent

.436

.095

21.056

1

.000

1.547

1.284

1.863

nbstatus

.051

.095

.283

1

.595

1.052

.873

1.267

nbmedia

.183

.094

3.775

1

.052

1.201

.998

1.446 1.089

easystart

−.076

.082

.855

1

.355

.927

.789

GEMHHINC

−.252

.062

16.783

1

.000

.777

.689

.877

.025

.066

.143

1

.705

1.025

.900

1.168

−.034

.004

77.806

1

.000

.967

.959

.974

.050

.007

45.377

1

.000

1.051

1.036

1.067

−2.083

.366

32.450

1

.000

.125

GEMEDUC age Country Constant

Source: Own research with data from GEM Adult Population Survey 2015 Note: Overall rate of correct classification: 93.6%; Dependent variable: Intention of starting a business

of women to start a business increases by .436 units, respectively .183 units. Age exhibits a negative but significant relationship with entrepreneurial intention of women, thus with an increase of one per cent in age, women’s intention to open a business decreases by .034 units. Moreover, when women’s income increases by one per cent their intention to open a business decreases by .252 units. Moreover, the regression analysis shows that the country is significant, fact that emphasises the role that context plays in women’s entrepreneurial intention.

4.5

Summary of Hypotheses

Table 4.9 summarises the results on the paper’s hypotheses and includes the hypotheses and the independent variable, which is being tested for its ability to explain the dependent variable.

4.5 Summary of Hypotheses

65

Table 4.9 Summary of Hypotheses: Results Hypothesis description

Independent Variables

Dependent Variable

Empirical results and Hypothesis Testing

H1: The perception of barriers to start a business has a significantly negative effect on the entrepreneurial intention of women.

• Fear of failure • Lack of self-confidence • Lack of entrepreneurial opportunities • Unattractive career path

Women’s Confirmed: Negative entrepreneurial and statistically intention significant for lack of self-confidence and career unattractiveness Not confirmed: Negative but NOT statistically significant for fear of failure and lack of entrepreneurial opportunities

H2: The perception of drivers to start a business has a significantly positive effect on the entrepreneurial intention of women.

• Knowing a nascent Women’s entrepreneur entrepreneurial • High perceived social intention status • Media • Perceived ease of starting a business

Confirmed: Positive and statistically significant for knowing a nascent entrepreneur and media drivers Not confirmed: Negative but NOT statistically significant for social status and perceived ease of starting a business

H3: The women’s perception of drivers and barriers to start a business differs among regions.

NA

NA

Confirmed

H4: The effects of drivers and barriers to start a business on the women’s entrepreneurial intentions differ among countries.

• Entrepreneurial Barriers and drivers on each of the selected regions

Women’s Confirmed entrepreneurial intention

Source: Own research with data from GEM Adult Population Survey 2015

5

Discussion

This study argues that the perceptions of barriers and the perceptions of drivers to start a business has a significant influence on the entrepreneurial intention of women, thus describing the differences and similarities between the countries in question. The results provide support for Hypothesis 1. Perception of barriers significantly decreases women’s intention to start a business in all lead countries of the European regions. This finding supports most of the earlier studies emphasising that women’s entrepreneurial intention is negatively influenced by the lack of self-confidence and the portrait of entrepreneurship as an unattractive career path for women (Arenius & Kovalainen, 2006; Naidu & Chad, 2015; Wu et al., 2019). Hypothesis 2 proposed the perception of drivers would have a positive effect on women’s entrepreneurial intention in all lead countries of the European regions. The study finds support for this hypothesis. The results are in agreement with the results produced by studies done on similar lines (Arenius & Kovalainen, 2006; Naidu & Chad, 2015; Wu et al., 2019). Knowing someone who has started a business is a significant predictor of women’s entrepreneurial intentions in the pooled sample. The perception that in their country, women will often see stories in the public media about successful new businesses significantly increases their entrepreneurial intention in the pooled sample. The dataset revealed that for women in Germany, fear of failure, lack of entrepreneurial opportunities, and lack of self-confidence has a negative effect on entrepreneurial intention of women, whereas knowing someone who has started a business is a significant predictor of entrepreneurial intention. The results support the study by Wagner (2006), who found that German women exhibit high levers of fear of failure which is more prevalent than in other entrepreneurial nations. The study is in further agreement with the research of Goss (2005) who argues that © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_5

67

68

5

Discussion

business failure is a serious discouragement in the German context since it carries a stigma around it. Even though Germany is characterised by economic growth and entrepreneurship is acknowledged to be a means for capturing opportunity (Ahmad & Hoffmann, 2008), women’s entrepreneurial intention is negatively influenced by their perception of lack of entrepreneurial opportunities and lack of self-confidence. Reasoning is proposed by Mro˙zewski (2014) who reflects on the entrepreneurship situation at the time of a more stable economy in Germany. The researcher indicates that while Germany “experiencing the lowest unemployment rates in 20 years” (p. 73), motivated individuals find it easier and more lucrative to seek employment in the broader labour market than by being selfemployed. Moreover, entrepreneurship represents a “passing phase” until more favourable employment opportunities emerge (Kalden et al., 2017). This echoes Braches and Elliot (2016) who argue that there are less entrepreneurial opportunities for women due to structural constraints from a largely conservative welfare state. Women in Germany lack self-confidence when it comes to start a business, even though they are highly skilled, well-educated and inspired (Kalden et al., 2017). This might be due to the limited nature of entrepreneurship education in Germany where “German schools diminish rather than encourage pupils’ ambitions to become self-employed” (Fuchs, Werner, & Wallau, 2008). For women in Poland, whilst the perception of drivers and barriers have a limited effect on their entrepreneurial intention; thus, they are hindered to start a business when they exhibit fear of failure and lack of self-confidence. The study is in further agreement with the research of Popowska (2013) who argues that women in Poland are characterised by higher levels of fear of failure since they appreciate the sense of security more and they have a high preference for avoiding uncertainty; the results can be further supported by the fact that the Polish society represents a stereotyped one which maintains rigid codes of belief where mistake has no place. The attitudes towards entrepreneurship in Eastern European countries such as Poland are affected by their socialist past, transition and integration to the European Union (Nabi & Liñán, 2011). In Poland, lack of skills, knowledge, and experience is many times cited as one of the principal challenges to entrepreneurial growth (Trojnarska & Halabisky, 2015). This is the result of a lack of high quality of entrepreneurship teaching whereas creativity and innovation are poorly encouraged. Jones et al. (2011) report that in Poland entrepreneurial education has the potential to raise students’ interest in entrepreneurship. In addition, the researchers contend that there some gender differences, suggesting that female students tended to focus on improving their self-confidence and self-awareness. Entrepreneurial activity of women in Spain is negatively influenced by the lack of self-confidence and the unattractiveness of the entrepreneurial career. This

5

Discussion

69

result supports the study of Driga, Lafuente, and Vaillant (2009) who argue that Spanish women do regard lack of self-confidence as a barrier to entrepreneurship. A potential explanation for the high impact of lack of self-confidence for women in Spain could follow the lines of the “hegemonic masculinity” argued by Campbell and Bell (2009) which provides men with social power and puts women in a subordinate position. Moreover, most university programmes in Spain have adopted an approach that focuses on training wage-earner professionals which proved to be insufficient since unemployment, flexibility, and over-qualification have become the characteristics of young people over the last decade in Spain (Lanero, Vázquez, Gutiérrez, & García, 2011). This, together with the shortages in Spanish university entrepreneurship education (Lanero et al., 2011) are congruent with the perception of women that they do not have the required skills and knowledge to start a business. Furthermore, this study supports the findings of Fuentes-Fuentes, Bojica, and Ruiz-Arroyo (2015) who argue that the social perception of lower innovativeness in businesses run by women exist in the Spanish context reinforcing the consensus that entrepreneurship is an unattractive career for women. Thus, this social perception shapes women’s expectations of themselves. The results of this study support the importance of role models and close relation to someone having started a business in the Spanish context. This result is line with the general literature which suggests that media plays a critical role in the processes that enable the creation of new businesses. The information generated by media is helpful to potential entrepreneurs and can lead to favourable interpretations of the wealth-creating possibilities of the new business (Urbano & Turró, 2013). In Sweden, lack of self-confidence and the unattractiveness of the entrepreneurial career have a negative effect on women’s entrepreneurial intentions, whereas knowing a nascent entrepreneur positively predicts intentions to open a business. It can be concluded, therefore, that in spite of enabling structures present in Sweden such as universal childcare, relatively cheap, and universally offered education (Arenius & Kovalainen, 2006), individual empowerment of women in terms of perceptions of their skills and capabilities does not necessarily occur. A potential explanation for the significant impact of entrepreneurship as an unattractive career is the stereotyped image which represents the entrepreneur. In a study conducted in Sweden by Malmstrom et al. (2017), the researchers concluded that when women apply for VC funding, the financiers rhetorically generate stereotypical images of women as with qualities opposite to those regarded as important to be an entrepreneur. Moreover, women’s credibility, trustworthiness, experience, and skills are questioned which undermines the image of women as an entrepreneur. The results of this study are in further agreement with the results by Arenius

70

5

Discussion

and Kovalainen (2006) and Bogren et al. (2013) done on similar lines across the Nordic countries. The output was similar to the conclusion that knowing someone who has started a business is a significant predictor of self-employment preference and contacts with nascent entrepreneurs are valued as supportive assets. The results from the pooled sample including all four lead countries indicate that the perception of high perceived status and the perceived ease of starting a business are not significantly related to women’s entrepreneurial intention of starting a business. This result is contrary to the general literature which suggests that the social status associated with entrepreneurship is high if the role of the entrepreneur is perceived as being economically and socially important. Moreover, the result does not support Ozaralli and Rivenburgh (2016) who propounded that in an environment which supports entrepreneurship economically and politically, individuals become motivated to act on entrepreneurial opportunities. The results presented in Table 4.1 indicate that the regional context in Europe does play a role in the intention of women to start a business. With the European regions depending on differing levels of economic growth and gender equality and having unique cultures, a wider supranational context seems to be relevant in women entrepreneurship. With this result from Table 4.1, Hypothesis 3 is supported. Furthermore, the results provide support for Hypothesis 4. Hypothesis 4, which was tested using a Kruskal-Wallis test, predicted that the effects of drivers and barriers to start a business on the women’s entrepreneurial intentions differ among countries. The results of this study are in agreement with the results generated by studies conducted on similar lines across different countries (Shinnar, Giacomin, & Janssen, 2012; Shneor, Metin Camgöz, & Bayhan Karapinar, 2013; Sharma, 2018) arguing that context and gender shape individual perceptions of barriers and drivers to entrepreneurship and entrepreneurial intentions. This suggests that the supranational level has its conditions and, thus, policymakers and practitioners have to acknowledge, besides a national framework, a supranational one that includes different levels of gender equality and the extent of entrepreneurial and economic development.

6

Conclusions

The main purpose of this study was to examine two research questions. The first research question examined what effect perceived drivers and barriers at the individual and social level have on women’s intention to open a business. The second research question examined whether there are any differences in women’s perception of entrepreneurial drivers and barriers across countries. This study provides a general description of women in the emergent stage of entrepreneurial activities in the European regions. It further analyses the role and importance of a variety of perceived drivers and barriers to entrepreneurial intention identified from the existing literature, thus describing the differences and similarities between the four lead countries. It is argued that due to the relative difference in the European regions in many aspects, the embedded nature of gender positions would be rather different, thus imposing variations in respect to the entrepreneurial intention of women in the European countries. Factors that influence women’s entrepreneurial intentions are national, situational, and culturally bound, thus highlighting the understanding of the characteristics of the context. The allocation of individual resources to the exploitation of resources is linked to the individual and social context. The ability of entrepreneurs to identify and capitalise on opportunities is highly influenced by the environment in which they operate, thus the exploitation of opportunities cannot be treated in isolation from the wider environment context. This study discussed that entrepreneurship takes place across multiple levels of society. Logistic regression analysis was used to estimate the factors that influence women’s entrepreneurial intention to start a business. Separate regression models were built for four European regions, represented by Germany, Poland, Spain, and Sweden, in order to learn whether different predictive variables are significant

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_6

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Conclusions

across different countries and whether there are strong differences among the European regions. First, the most important factor affecting women’s intention to start a business is the individual perception of lack of confidence. Across all four lead countries, the entrepreneurial intention is lower among women who perceive themselves as not having the required skills and knowledge to start a business as it is among women perceiving themselves as having such capabilities. The results further confirm that perception of lack of entrepreneurial opportunities and unattractive career path both negatively influence women’s entrepreneurial intention across the pooled countries. Social capital and media represent a significant driver of women’s entrepreneurial intention. These results give greater attention to individual awareness and perception processes. Second, following the study’s hypotheses, there is variation in the factors affecting the entrepreneurial intention of women across the four lead European countries. This result highlights the difference in the national economies and the factors influencing the intention of women to start a business. The cross-national variation is not only evident in the economic and societal aspects among the European countries, but in the women’s perception of drivers and barriers to start a business. This study regards this as an important finding, as it argues that the entrepreneurial process is influenced by individual and social level factors besides the direct effects of macro-economic and macro-societal aspects. The empirical analysis of this study demonstrates that individual level assessment of drivers and barriers is critical to women’s decisions and actions to start a business. Moreover, the results disprove the adoption of uniform policy measures across countries or regions as the most effective tool to promote entrepreneurship among women. The study emphasises that best policy practices for one nation do not necessarily apply well to other nations. Thus, the results suggest that individual, social, as well as national, and supranational characteristics must be acknowledged.

7

Theoretical and Practical Contributions

This study contributes to the existing literature in different ways. First, it contributes to the existent literature on women entrepreneurship by addressing calls for extending empirical research on women’s entrepreneurial activates with cross-country comparisons (e.g. Wu et al., 2019). As such, a major contribution of this study has been to analyse how perceived drivers and barriers influence women’s entrepreneurial intentions across European regions and four national lead countries within those regions. This study is in line with the call for an approach that goes beyond conventional methods analyses which favour gender (male/female) comparisons (e.g. Ahl, 2006) by adopting research focused only on women. The study emphasised the difference in the extent to which barriers and drivers predict entrepreneurial intention, with them being highly relevant in Germany, Spain, and Sweden and less relevant in Poland—a country in transition with challenges relating to lower levels of development. This suggests that challenges that women face are context-specific, not yet captured in research on women entrepreneurship, and need to adapt national and regional specific mechanisms rather than just adopting strategies, mechanisms, structures, and activities that seem successful in other countries. Moreover, the study’s contribution is to extend and provide additional evidence for the extant body of knowledge concerning entrepreneurial intentions, whereas scholars have called for the need to learn more about the factors that not only hinder but also support the participation of women in the formation of new businesses (Davey et al., 2012). This may advance the theory-building process for developing existing intention-based models and hence, contributes to the argument that entrepreneurial intention is a result of other antecedents than the ones stipulated by Ajzen (1991) as well as Shapero and Sokol (1982).

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_7

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Moreover, the study contributes to the literature on contextual entrepreneurship by analysing how context plays a role in women entrepreneurship and the importance of supra-regional and national context. Scholars (Fredin & Jogmark, 2017; Sharma, 2018) have emphasised the context in entrepreneurship, where there are place-specific socio-economic development paths and where the interaction between local, political, and business people can be understood and how these interactions shape and are shaped by context (Fredin & Jogmark, 2017). As such, the study shows the impact of context on women’s entrepreneurial intentions and on the difference in the way perceived drivers and barriers play a role in women entrepreneurship. Finally, this study carries important practical implications for policymakers since the results confirm the influence of context on the entrepreneurial process. They further invite policymakers to reflect on the design of public policy measures that aim at economic development (OECD/European Union, 2017) and at stimulating entrepreneurial behaviour among women. Numerous governments acknowledge the potential for economic and social transformation that entrepreneurship has and are actively trying to promote it through public policies by lowering entrepreneurial barriers and by strengthening the business environments (OECD/European Union, 2017). The latter is particularly relevant during the opportunity recognition and exploitation phase, where the main purpose of policy tools is to raise women’s level of interest in entrepreneurship and a resulting increase of entrepreneurial intention. In particular, among public policies and initiatives that aim at promoting entrepreneurship, women entrepreneur models have been ascribed a substantial role (Bijedi´c & Welter, 2015), whereas all models of entrepreneurship are promoted including part-time and social entrepreneurship. Based on this research, policy initiatives which foster awareness about the potential of women’s entrepreneurship through role models in the media and career guidance will have a varying degree of impact on the cognitive process of women. Furthermore, policymakers should acknowledge that such policies must be evaluated from the perspective of national culture and segmented at the individual level in order to consider differences in the cognitive processes. In this line, this study extends the research findings of Wyrwich, Stuetzer, and Sternberg (2016) and Schmutzler et al. (2018) regarding the need to adjust public policies to regional characteristics and design more tailored approaches to women entrepreneurship. Effective policies to foster entrepreneurship necessarily have to take into consideration national and regional predispositions no matter how much funding a project has. In order to enhance entrepreneurial drivers, training, coaching, mentoring, and business counselling should reflect the flexibility the entrepreneurship provides in balancing work with family aspects (Bijedi´c & Welter, 2015).

8

Limitations and Future Research

Although the study provided useful information concerning women entrepreneurship, this study faced various limitations. First, the use of rather simple constructs for factors at the individual and social level instead of psychometric measures of each of drivers and barriers, and entrepreneurial intentions can be subject to criticism. The dependent and independent variables are expressed through dummy variables that do not cover all the relevant information about the intensity of women’s decision to start a business. Even though the approach measuring entrepreneurial intentions by a single item has been extensively acknowledged in the entrepreneurship literature (Schmutzler et al., 2018), a single-item measure might not be sufficient to cover the complex and multidimensional nature of variables. Moreover, single-items are widely acknowledged to have inadequate psychometric characteristics (Boyd, Gove, & Hitt, 2005). Ideally, not only complex structures but also a well-established scale of these constructs should be used. In this way, similarities between key influences and the individuals can be controlled, an aspect that has been argued to strongly influence the adoption of a particular behaviour (Bandura et al., 2001). Second, since the data used in this study was collected from the GEM project, there was no common measure to gauge the effects of drivers and barriers on women’s entrepreneurial intentions. Moreover, the GEM project was designed with the purpose to give an image of entrepreneurship in the participating countries and not to specifically assess perceptions of women concerning factors that might inhibit or facilitate their entrepreneurial activities. Therefore, the coarsegrained measures from GEM data are reasonable for a study that is among the first to look into the role of drivers and barriers to women’s entrepreneurial intentions across European regions and national countries within those regions. The scarcity of valid measures for future women entrepreneurs has been addressed © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6_8

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previously (e.g. Arenius & Kovalainen, 2006; Schjoedt, Renko, & Schaver, 2014) and, therefore, more fine-grained and richer measures are essential to continue the research agenda of women entrepreneurship. Third, the variables in this study have a limited effect on women’s entrepreneurial intention (e.g. in the case of Poland), therefore, some bias can be remaining from omitted variables. For example, recent research emphasises the relevance of individual’s access to financial, venture, as well as cultural capital (Colombo & Grilli, 2010; Danis, De Clecq, & Petricevic, 2011), motherhood (Wu et al., 2019), lack of government support, or intensive competition (Naidu & Chand, 2015) which are not comprised in this study. Although improvements in these directions are appreciated, there are avenues for further research that are considered particularly worthwhile. Future research on women entrepreneurship should be conducted at a more micro level, whereas the local concentration of entrepreneurship (Minniti, 2005) and determinants of regionally and locally relevant entrepreneurship practices are connected. In this way, entrepreneurship knowledge along with the interplay of supporting mechanisms of economic development and competitiveness would provide greater insight into this topic. This study is but a small step to draw attention to the importance of context. The inclusion of interacting layers of contextual factors and factors inhibiting or facilitating women’s intention to start a business will advise the work of development agencies and policymakers as it holds the potential to enable targeted entrepreneurial initiatives. Moreover, these aspects at later stages of the entrepreneurial process need to be investigated as well. Another avenue for future research that gives specific insights on how to encourage women entrepreneurship is the consideration of contexts where individuals face lower levels of national and regional resources that are vital to innovation and entrepreneurship. Most certainly, research should be conducted in developing countries to assess entrepreneurial behaviour in greater detail. Similar to developed contexts, the rate of self-employment among women has increased in the last decades and a growing number of initiatives are undertaken to empower women in the process (Yadav & Unni, 2016). Future studies could further investigate how factors inhibiting or facilitating new venture creation shape various types of women entrepreneurs, such as necessity, opportunity, or high-growth women entrepreneurship. Since this study cannot establish whether women’s intention to start a business remains stable over time as it adopts a cross-sectional design, future research should employ a longitudinal approach which takes at least two measures and reveals legal and regulatory features (Armour & Cumming, 2008).

References

Abebe, M. A., & Alvarado, D. (2016). Blessing in Disguise? Social and Institutional Determinants of Entrepreneurial Intentions following Involuntary Job Loss. Journal of Small Business Management, 56(4), 555–572. https://doi.org/10.1111/jsbm.12303 Achtenhagen, L., & Welter, F. (2003). Female Entrepreneurship in Germany: Context, Development and Its Reflection in German Media. In J.E. Butler (Ed.) New Perspectives on Women Entrepreneurs (pp. 71–100). Greenwich, CT: Information Age Publishing. Achtenhagen, L., & Welter, F. (2007). Media Discourse in Entrepreneurship Research. In H. Neergaard, & J.P. Ulhoi (Eds.), Handbook of Qualitative Research Methods in Entrepreneurship (pp.193–215). Cheltenham: Edward Elgar. Agarwal, S., & Lenka, U. (2015). Study on work-life balance of women entrepreneurs — review and research agenda. Industrial and Commercial Training, 47(7), 356–362. https://doi.org/10.1108/ict-01-2015-0006 Agresti, A., & Tarantola, C. (2018). Simple Ways to Interpret Effects in Modelling Ordinal Categorical Data. Statistica Neerlandica, 72(3), 210–223. https://doi.org/10.1111/stan. 12130 Ahl, H. (2006). Why Research on Women Entrepreneurs Needs New Directions. Entrepreneurship Theory and Practice, 30(5), 595–621. https://doi.org/10.1111/j.1540-6520. 2006.00138.x Ahmad, N., & Hoffmann, A.N. (2008). A Framework for Addressing and Measuring Entrepreneurship OECD Statistics Working Papers (2008/02), Paris: OECD. Retrieved from http://www.oecd.org/std/business-stats/39651330.pdf. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-t Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice- Hall. Aldrich, H. E., & Wiedenmayer, G. (2019). Chapter 4 From Traits to Rates: An Ecological Perspective on Organizational Foundings. In J. Katz & A.C. Corbett (Eds.), Advances in Entrepreneurship, Firm Emergence and Growth (pp. 61–97). Bingley: Emerald Publishing Limited

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020 I. Stroila, Drivers and Barriers of Women Entrepreneurs, Forschungsreihe der FH Münster, https://doi.org/10.1007/978-3-658-31514-6

77

78

References

Amemiya, T. (1985). Advanced Econometrics. Cambridge: Harvard University Press. Anderson, A., Park, J., & Jack, S. (2007). Entrepreneurial Social Capital. International Small Business Journal: Researching Entrepreneurship, 25(3), 245–272. https://doi. org/10.1177/0266242607076526 Anokhin, S., Grichnik, D., & Hisrich, R. D. (2008). The journey from novice to serial entrepreneurship in China and Germany: Are the drivers the same? Managing Global Transitions, 6(2), 117–142. Arenius, P., & Kovalainen, A. (2006). Similarities and Differences Across the Factors Associated with Women’s Self-employment Preference in the Nordic Countries. International Small Business Journal: Researching Entrepreneurship, 24(1), 31–59. https:// doi.org/10.1177/0266242606059778 Arenius, P., & Minniti, M. (2005). Perceptual Variables and Nascent Entrepreneurship. Small Business Economics, 24(3), 233–247. https://doi.org/10.1007/s11187-005-1984-x Armour, J., & Cumming, D. (2008). Bankruptcy law and entrepreneurship. American Law and Economics Review, 10(2), 303–350. https://doi.org/10.1093/aler/ahn008 Atkinson, J.W. (1966). Motivational determinants of risk-taking behaviour. In J.W. Atkinson & N.T. Feather, (Eds.) A Theory of Achievement Motivation (pp. 11–30). New York, NY: Wiley. Atkinson, J.W. & Litwin, G.H. (1973). Achievement motive and test anxiety conceived as motive to approach success and motive to avoid failure. In D.C. McClelland & R.S. Steele, (Eds.) Human Motivation: A Book of Readings (pp. 145–163). Morristown, NJ: General Learning Press. Audretsch, D., & Keilbach, M. (2004). Entrepreneurship capital and economic performance. Regional Studies, 38(8), 949–959. https://doi.org/10.1080/003434004200028 0956 Austin, M. J., & Nauta, M. M. (2015). Entrepreneurial Role-Model Exposure, SelfEfficacy, and Women’s Entrepreneurial Intentions. Journal of Career Development, 43(3), 260–272. https://doi.org/10.1177/0894845315597475 Baines, S., & Wheelock, J. (2000). Work and Employment in Small Businesses: Perpetuating and Challenging Gender Traditions. Gender, Work & Organization, 7(1), 45–56. https://doi.org/10.1111/1468-0432.00092 Bandura, A. (1992). Exercise of personal agency through the self-efficacy mechanism. In R. Schwarzer (Ed.) Self-efficacy: Thought control of action (pp. 3–38). Washington, DC: Hemisphere Publishing Corp. Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-Efficacy Beliefs as Shapers of Children’s Aspirations and Career Trajectories. Child Development, 72(1), 187–206. https://doi.org/10.1111/1467-8624.00273 Barbieri, D., Franklin, P., Janeckova,H., Karu,… Salanauskait˙e, L. (2017). Gender Equality Index 2017: Measuring gender equality in the European Union 2005–2015 – Report. European Institute for Gender Equality. Retrieved from https://eige.europa.eu/publications/gender-equality-index-2017-measuring-genderequality-european-union-2005-2015-report Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

References

79

Barrett, M., & Moores, K. (2009). Spotlights and shadows: Preliminary findings about the experiences of women in family business leadership roles. Journal of Management & Organization, 15(3), 363–377. https://doi.org/10.5172/jmo.2009.15.3.363 Bates, T., Jackson, W. E., & Johnson, J. H. (2007). Advancing Research on Minority Entrepreneurship. The ANNALS of the American Academy of Political and Social Science, 613(1), 10–17. https://doi.org/10.1177/0002716207303405 Bergmann, H., Mueller, S., & Schrettle, T. (2014). The use of global entrepreneurship monitor data in academic research: a critical inventory and future potentials. International Journal of Entrepreneurial Venturing, 6(3), 242. https://doi.org/10.1504/ijev. 2014.064691 Bhaskar, R. (2014). The possibility of naturalism: A philosophical critique of the contemporary human sciences. London: Routledge Bijedi´c, T. & Welter, F. (2015). Supporting Women Entrepreneurs in Germany: Rapid Policy Assessments of Inclusive Entrepreneurship Policies and Programmes. Retrieved from OECD website: http://www.oecd.org/employment/leed/Rapid-policy-assessmentGermany-final.pdf Block, J. H., Fisch, C. O., & van Praag, M. (2016). The Schumpeterian entrepreneur: a review of the empirical evidence on the antecedents, behaviour and consequences of innovative entrepreneurship. Industry and Innovation, 24(1), 61–95. https://doi.org/10. 1080/13662716.2016.1216397 Bogren, M., von Friedrichs, Y., Rennemo, Ø., & Widding, Ø. (2013). Networking women entrepreneurs: fruitful for business growth? International Journal of Gender and Entrepreneurship, 5(1), 60–77. https://doi.org/10.1108/17566261311305210 Bosma, N. & Kelley, D. (2019). Global Entrepreneurship Monitor 2018/2019 Global Report. Retrieved from Global Entrepreneurship Monitor website: https://www.gem consortium.org/report/50213 Boyd, B. K., Gove, S., & Hitt, M. A. (2005). Construct measurement in strategic management research: illusion or reality? Strategic Management Journal, 26(3), 239–257. https://doi.org/10.1002/smj.444 Braches, B., & Elliott, C. (2016). Articulating the entrepreneurship career: A study of German women entrepreneurs. International Small Business Journal, 35(5), 1–23. https:// doi.org/10.1177/0266242616651921 Bruneel, J., Ratinho, T., Clarysse, B., & Groen, A. (2012). The Evolution of Business Incubators: Comparing demand and supply of business incubation services across different incubator generations. Technovation, 32(2), 110–121. https://doi.org/10.1016/j. technovation.2011.11.003 Brush, C. G., de Bruin, A., & Welter, F. (2009). A gender-aware framework for women’s entrepreneurship. International Journal of Gender and Entrepreneurship, 1(1), 8–24. https://doi.org/10.1108/17566260910942318 Bryman, A. (2016). Social research methods (5th ed.). Oxford: Oxford University Press. Bryman, A., & Bell, E. (2015). Business research methods (4th ed). Oxford: Oxford University Press. Buttar, H. M. (2015). Retracted: Formation of Entrepreneurial Career Intentions: The Role of Sociocognitive Factors. Journal of Employment Counselling, 52(1), 2–17. https:// doi.org/10.1002/j.2161-1920.2015.00052.x

80

References

Byrne, J., Fattoum, S., & Diaz Garcia, M. C. (2018). Role Models and Women Entrepreneurs: Entrepreneurial Superwoman Has Her Say. Journal of Small Business Management, 57(1), 154–184. https://doi.org/10.1111/jsbm.12426 Cacciotti, G., Hayton, J. C., Mitchell, J. R., & Giazitzoglu, A. (2016). A reconceptualization of fear of failure in entrepreneurship. Journal of Business Venturing, 31(3), 302–325. https://doi.org/10.1016/j.jbusvent.2016.02.002 Campbell, H., & Bell, M. M. (2009). The Question of Rural Masculinities*. Rural Sociology, 65(4), 532–546. https://doi.org/10.1111/j.1549-0831.2000.tb00042.x Carlsson, B., Braunerhjelm, P., McKelvey, M., Olofsson, C., Persson, L., & Ylinenpää, H. (2013). The evolving domain of entrepreneurship research. Small Business Economics, 41(4), 913–930. https://doi.org/10.1007/s11187-013-9503-y Carr, P., & Beaver, G. (2002). The enterprise culture: understanding a misunderstood concept. Strategic Change, 11(2), 105–113. https://doi.org/10.1002/jsc.579 Carter, N. M., Gartner, W. B., Shaver, K. G., & Gatewood, E. J. (2003). The career reasons of nascent entrepreneurs. Journal of Business Venturing, 18(1), 13–39. https://doi.org/ 10.1016/s0883-9026(02)00078-2 Casson, M. (2003). The Entrepreneur: An Economic Theory (2nd ed). Cheltenham: Edward Elgar. Cetindamar, D., Gupta, V. K., Karadeniz, E. E., & Egrican, N. (2012). What the numbers tell: The impact of human, family and financial capital on women and men’s entry into entrepreneurship in Turkey. Entrepreneurship & Regional Development, 24(1–2), 29–51. https://doi.org/10.1080/08985626.2012.637348 Chua, H. S., & Bedford, O. (2015). A Qualitative Exploration of Fear of Failure and Entrepreneurial Intent in Singapore. Journal of Career Development, 43(4), 319–334. https://doi.org/10.1177/0894845315599255 Colombo, M. G., & Grilli, L. (2010). On growth drivers of high-tech start-ups: Exploring the role of founders’ human capital and venture capital. Journal of Business Venturing, 25(6), 610–626. https://doi.org/10.1016/j.jbusvent.2009.01.005 Cooper, D., & Schindler, P. (2011). Business Research Methods (11th ed). McGraw Hill: Boston. Creswell, J.W. (2014). A Concise Introduction to Mixed Methods Research. London: Sage Cromie, S. (1987). Motivations of aspiring male and female entrepreneurs. Journal of Organizational Behavior, 8(3), 251–261. https://doi.org/10.1002/job.4030080306 Dalborg, C. (2015). The life cycle in women-owned businesses: from a qualitative growth perspective. International Journal of Gender and Entrepreneurship, 7(2), 126–147. https://doi.org/10.1108/ijge-06-2014-0019 Dalborg, C., von Friedrichs, Y., & Wincent, J. (2012). Beyond the numbers: qualitative growth in women’s businesses. International Journal of Gender and Entrepreneurship, 4(3), 289–315. https://doi.org/10.1108/17566261211264163 Danis, W. M., De Clercq, D., & Petricevic, O. (2011). Are social networks more important for new business activity in emerging than developed economies? An empirical extension. International Business Review, 20(4), 394–408. https://doi.org/10.1016/j.ibu srev.2010.08.005 Davey, T., Baaken, T., Galán-Muros, V., & Meerman, A. (2012). Barriers and Drivers in European University-Business Cooperation. In T. Davey, T. Baaken, V. Galán-Muros, & A. Meerman (Eds.) The State of European University-Business Cooperation. Final

References

81

Report — Study on the cooperation between Higher Education Institutions and public and private organisations in Europe (pp. 1–24). Davey, T., Plewa, C., & Struwig, M. (2011). Entrepreneurship perceptions and career intentions of international students. Education + Training, 53(5), 335–352. https://doi. org/10.1108/00400911111147677 Davey, T., Rossano, S., & van der Sijde, P. (2015). Does context matter in academic entrepreneurship? The role of barriers and drivers in the regional and national context. The Journal of Technology Transfer, 41(6), 1457–1482. https://doi.org/10.1007/s10961015-9450-7 Davidsson, P., & Honig, B. (2003). The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing, 18(3), 301–331. https://doi.org/10.1016/ s0883-9026(02)00097-6 Dawson, C., & Henley, A. (2013). Gender, Risk, and Venture Creation Intentions. Journal of Small Business Management, 53(2), 501–515. https://doi.org/10.1111/jsbm.12080 Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. https://doi.org/10.1007/s11747-011-0300-3 Dileo, I., & García Pereiro, T. (2018). Assessing the impact of individual and context factors on the entrepreneurial process. A cross-country multilevel approach. International Entrepreneurship and Management Journal, 4(4), 1–49. https://doi.org/10.1007/ s11365-018-0528-1 Doran, J., McCarthy, N., O’Connor, M., & Nsiah, C. (2018). The role of entrepreneurship in stimulating economic growth in developed and developing countries. Cogent Economics & Finance, 6(1), 1–14. https://doi.org/10.1080/23322039.2018.1442093 Driga, O., Lafuente, E., & Vaillant, Y. (2009). Reasons for the Relatively Lower Entrepreneurial Activity Levels of Rural Women in Spain. Sociologia Ruralis, 49(1), 70–96. https://doi.org/10.1111/j.1467-9523.2008.00475.x Drolet, A. L., & Morrison, D. G. (2001). Do We Really Need Multiple-Item Measures in Service Research? Journal of Service Research, 3(3), 196–204. https://doi.org/10.1177/ 109467050133001 Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2018). Global Innovation Index 2018: Energizing the World with Innovation (11th ed.). Retrieved from https://www.wipo. int/edocs/pubdocs/en/wipo_pub_gii_2018.pdf Eckhardt, J. T., & Shane, S. A. (2003). Opportunities and Entrepreneurship. Journal of Management, 29(3), 333–349. https://doi.org/10.1177/014920630302900304 Eesley, C., & Wang, Y. (2017). Social influence in career choice: Evidence from a randomized field experiment on entrepreneurial mentorship. Research Policy, 46(3), 636–650. https://doi.org/10.1016/j.respol.2017.01.010 Esfandiar, K., Sharifi-Tehrani, M., Pratt, S., & Altinay, L. (2019). Understanding entrepreneurial intentions: A developed integrated structural model approach. Journal of Business Research, 94(1), 172–182. https://doi.org/10.1016/j.jbusres.2017.10.045 Esser, F., & Vliegenthart, R. (2017). Comparative Research Methods. In J. Matthes, C. S. Davis & R. Potter (Eds.), The International Encyclopedia of Communication Research Methods (pp. 248–269). London: Wiley-Blackwell.

82

References

European Commission (2012). Report on the results of public consultation on The Entrepreneurship 2020 Action Plan. Retrieved from https://ec.europa.eu/growth/smes/promot ing-entrepreneurship/action-plan_en Fagerland, M. W., & Hosmer, D. W. (2016). Tests for goodness of fit in ordinal logistic regression models. Journal of Statistical Computation and Simulation, 86(17), 3398– 3418. https://doi.org/10.1080/00949655.2016.1156682 Fayolle, A., Gailly, B., & Lassas-Clerc, N. (2006). Assessing the impact of entrepreneurship education programmes: a new methodology. Journal of European Industrial Training, 30(9), 701–720. https://doi.org/10.1108/03090590610715022 Fayolle, A., Liñán, F., & Moriano, J. A. (2014). Beyond entrepreneurial intentions: values and motivations in entrepreneurship. International Entrepreneurship and Management Journal, 10(4), 679–689. https://doi.org/10.1007/s11365-014-0306-7 Fleetwood, S. (2001). Causal Laws, Functional Relations and Tendencies. Review of Political Economy, 13(2), 201–220. https://doi.org/10.1080/09538250120036646 Foo, M.D. (2009). Emotions and Entrepreneurial Opportunity Evaluation. Entrepreneurship Theory and Practice, 35(2), 375–393. https://doi.org/10.1111/j.1540-6520.2009. 00357.x Frank, R. (1984). Are Workers Paid their Marginal Products? The American Economic Review, 74(4), 549–571. Retrieved from http://www.jstor.org/stable/1805123 Frederiks, A. J., Englis, B. G., Ehrenhard, M. L., & Groen, A. J. (2019). Entrepreneurial cognition and the quality of new venture ideas: An experimental approach to comparing future-oriented cognitive processes. Journal of Business Venturing, 34(2), 327–347. https://doi.org/10.1016/j.jbusvent.2018.05.007 Fredin, S., & Jogmark, M. (2017). Local culture as a context for entrepreneurial activities. European Planning Studies, 25(9), 1556–1574. https://doi.org/10.1080/09654313.2017. 1306028 Fritsch, M. (2007). How does new business formation affect regional development? Introduction to the special issue. Small Business Economics, 30(1), 1–14. https://doi.org/10. 1007/s11187-007-9057-y Fuchs, K., Werner, A., & Wallau, F. (2008). Entrepreneurship education in Germany and Sweden: what role do different school systems play? Journal of Small Business and Enterprise Development, 15(2), 365–381. https://doi.org/10.1108/14626000810871736 Fuentes-Fuentes, M. del M., Bojica, A. M., & Ruiz-Arroyo, M. (2015). Entrepreneurial orientation and knowledge acquisition: effects on performance in the specific context of women-owned firms. International Entrepreneurship and Management Journal, 11(3), 695–717. https://doi.org/10.1007/s11365-014-0336-1 Furdas, M., & Kohn, K. (2010). What’s the difference?! Gender, personality, and the propensity to start a business (Discussion Paper 4778). Bonn: IZA Institute of Labor Economics. Retrieved from http://ftp.iza.org/dp4778.pdf Gaddefors, J., & Anderson, A. R. (2017). Entrepreneursheep and context: when entrepreneurship is greater than entrepreneurs. International Journal of Entrepreneurial Behavior & Research, 23(2), 267–278. https://doi.org/10.1108/ijebr-01-2016-0040 Galán-Muros, V., & Plewa, C. (2016). What drives and inhibits university-business cooperation in Europe? A comprehensive assessement. R&D Management, 46(2), 369–382. https://doi.org/10.1111/radm.12180

References

83

Ganzerla, S. (2008). Evaluation on Policy: Promotion of Women Innovators and Entrepreneurship (25 July 2008). Retrieved from European Commission website: http://boo kshop.europa.eu/en/evaluation-on-policy-pbNB0114152/ Gartner, W. A. (1995). Aspects of organizational emergence. In I., Bull, H., Thomas, & G., Willard (Eds.), Entrepreneurship. Perspectives on Theory Building, (pp. 67–86). Oxford: Elsevier Science Limited Gartner, W. B. (1985). A Conceptual Framework for Describing the Phenomenon of New Venture Creation. The Academy of Management Review, 10(4), 696. https://doi.org/10. 2307/258039 Gartner, W. B. (1990). What are we talking about when we talk about entrepreneurship? Journal of Business Venturing, 5(1), 15–28. https://doi.org/10.1016/0883-902 6(90)90023-m Gatewood, E. J., Shaver, K. G., & Gartner, W. B. (1995). A longitudinal study of cognitive factors influencing start-up behaviors and success at venture creation. Journal of Business Venturing, 10(5), 371–391. https://doi.org/10.1016/0883-9026(95)00035-7 Gedajlovic, E., Honig, B., Moore, C. B., Payne, G. T., & Wright, M. (2013). Social Capital and Entrepreneurship: A Schema and Research Agenda. Entrepreneurship Theory and Practice, 37(3), 455–478. https://doi.org/10.1111/etap.12042 Gildemeister, R. (2010). Doing Gender. In R. Becker & B. Kortendiek (Eds.), Handbuch Frauen- Und Geschlechterforschung (pp. 137–145). Wiesbaden: VS Verlag für Sozialwissenschaften. Giménez, D., & Calabrò, A. (2017). The salient role of institutions in Women’s entrepreneurship: a critical review and agenda for future research. International Entrepreneurship and Management Journal, 14(4), 857–882. https://doi.org/10.1007/s11365017-0480-5 Giotopoulos, I., Kontolaimou, A., & Tsakanikas, A. (2017). Antecedents of growthoriented entrepreneurship before and during the Greek economic crisis. Journal of Small Business and Enterprise Development, 24(3), 528–544. https://doi.org/10.1108/ jsbed-01-2017-0003 Goss, D. (2005). Schumpeter’s Legacy? Interaction and Emotions in the Sociology of Entrepreneurship. Entrepreneurship Theory and Practice, 29(2), 205–218. https://doi. org/10.1111/j.1540-6520.2005.00077.x Greene, W. (2008). Econometric analysis. Upper Saddle River, NJ: Pearson Prentice Hall. Gupta, V. K., Goktan, A. B., & Gunay, G. (2014). Gender differences in evaluation of new business opportunity: A stereotype threat perspective. Journal of Business Venturing, 29(2), 273–288. https://doi.org/10.1016/j.jbusvent.2013.02.002 Gupta, V. K., Wieland, A. M., & Turban, D. B. (2018). Gender Characterizations in Entrepreneurship: A Multi-Level Investigation of Sex-Role Stereotypes about High-Growth, Commercial, and Social Entrepreneurs. Journal of Small Business Management, 57(1), 131–153. https://doi.org/10.1111/jsbm.12495 Hagle, T. M., & II, G. E. M. (1992). Goodness-of-Fit Measures for Probit and Logit. American Journal of Political Science, 36(3), 762. https://doi.org/10.2307/2111590 Hamilton, E. (2013). The discourse of entrepreneurial masculinities (and femininities). Entrepreneurship & Regional Development, 25(1–2), 90–99. https://doi.org/10.1080/ 08985626.2012.74687 Harrell, F. (2015). Regression modelling strategies. Cham: Springer.

84

References

Haugh, H. M., & Talwar, A. (2014). Linking Social Entrepreneurship and Social Change: The Mediating Role of Empowerment. Journal of Business Ethics, 133(4), 643–658. https://doi.org/10.1007/s10551-014-2449-4 Hébert, R. F., & Link, A. N. (1989). In search of the meaning of entrepreneurship. Small Business Economics, 1(1), 39–49. https://doi.org/10.1007/bf00389915 Henry, C., Foss, L., & Ahl, H. (2015). Gender and entrepreneurship research: A review of methodological approaches. International Small Business Journal: Researching Entrepreneurship, 34(3), 217–241. https://doi.org/10.1177/0266242614549779 Hessels, J., van Gelderen, M., & Thurik, R. (2008). Entrepreneurial aspirations, motivations, and their drivers. Small Business Economics, 31(3), 323–339. https://doi.org/10. 1007/s11187-008-9134 Hindle, K., Klyver, K., & Jennings, D. F. (2009). An “informed” intent model: Incorporating human capital, social capital, and gender variables into the theoretical model of entrepreneurial intentions. In A. L. Carsrud & M. Brännback (Eds.), Understanding the entrepreneurial mind: Opening the Black box (pp. 35–50). New York, NY: Springer. Hitt, M. A., Ireland, R. D., Sirmon, D. G., & Trahms, C. A. (2011). Strategic Entrepreneurship: Creating Value for Individuals, Organizations, and Society. Academy of Management Perspectives, 25(2), 57–75. https://doi.org/10.5465/amp.25.2.57 Hoang, H., & Yi, A. (2015). Network-based Research in Entrepreneurship: A Decade in Review. Foundations and Trends® in Entrepreneurship, 11(1), 1–54. https://doi.org/10. 1561/0300000052 Holcombe, R. G. (1998). Entrepreneurship and economic growth. The Quarterly Journal of Austrian Economics, 1(2), 45–62. https://doi.org/10.1007/s12113-998-1008-1 Holliday, R. (2015). Media and Popular Culture. In V. Robinson & D. Robinson (Eds.), Introducing Gender and Women’s Studies (pp. 233–253). London: Springer Nature Limited. Horowitz, J. L., & Savin, N. (2001). Binary Response Models: Logits, Probits and Semiparametrics. Journal of Economic Perspectives, 15(4), 43–56. https://doi.org/10.1257/ jep.15.4.43 Hosmer, D., W., & Lemeshow, S. (2000). Applied Logistic Regression. New York, NY: Wiley Interscience Howe, K. R. (1988). Against the Quantitative-Qualitative Incompatibility Thesis or Dogmas Die Hard. Educational Researcher, 17(8), 10–16. https://doi.org/10.3102/001318 9x017008010 Hytti, U. (2005). New meanings for entrepreneurs: from risk-taking heroes to safe-seeking professionals. Journal of Organizational Change Management, 18(6), 594–611. https:// doi.org/10.1108/09534810510628521 Ireland, R. D., Hitt, M. A., & Sirmon, D. G. (2003). A Model of Strategic Entrepreneurship: The Construct and its Dimensions. Journal of Management, 29(6), 963–989. https://doi.org/10.1016/s0149-2063_03_00086-2 Iversen, J., Jørgensen, R., & Malchow-Møller, N. (2008). Defining and Measuring Entrepreneurship. Foundations and Trends® in Entrepreneurship, 4(1), 1–63. https://doi.org/ 10.1561/0300000020 Jones, P., Miller, C., Jones, A., Packham, G., Pickernell, D., & Zbierowski, P. (2011). Attitudes and motivations of Polish students towards entrepreneurial activity. Education + Training, 53(5), 416–432. https://doi.org/10.1108/00400911111147721

References

85

Kalden, J. N., Cunningham, J., & Anderson, A. R. (2017). The social status of entrepreneurs. The International Journal of Entrepreneurship and Innovation, 18(2), 91–104. https://doi.org/10.1177/1465750317706439 Kasouf, C. J., Morrish, S. C., & Miles, M. P. (2013). The moderating role of explanatory style between experience and entrepreneurial self-efficacy. International Entrepreneurship and Management Journal, 11(1), 1–17. https://doi.org/10.1007/s11365-0130275-2 Kaufman, D. & Kraay, A. (2019). The Worldwide Governance Indicators. Retrieved from The World Bank website: https://info.worldbank.org/governance/wgi/#home Kelley, D. Baumer, B., Cole, M., Dean, M., & Heavlow, R. (2017). Women’s Entrepreneurship 2016/2017 Report. Retrieved from Global Entrepreneurship Monitor website: https://www.gemconsortium.org/report/49860 Khajeheian, D. (2013). New Venture Creation in Social Media Platform; Towards a Framework for Media Entrepreneurship. In M. Friedrichsen & W. Mühl-Benninghaus (Eds.), Media Business and Innovation. Handbook of social media management: Value chain and business models in changing media markets (pp. 125–142). Dordrecht: Springer. Kirzner, I. M. (1979) Perception, opportunity and profit: studies in the theory of Entrepreneurship. Chicago: University of Chicago Press Kirzner, I.M. (1973) Competition and Entrepreneurship. Chicago: University of Chicago Press. Klyver, K., Nielsen, S. L., & Evald, M. R. (2013). Women’s self-employment: An act of institutional (dis)integration? A multilevel, cross-country study. Journal of Business Venturing, 28(4), 474–488. https://doi.org/10.1016/j.jbusvent.2012.07.002 Kouriloff, M. (2000). Exploring Perceptions of A Priori Barriers to Entrepreneurship: A Multidisciplinary Approach. Entrepreneurship Theory and Practice, 25(2), 59–80. https://doi.org/10.1177/104225870002500204 Krueger, N. (2009). Entrepreneurial Intentions are Dead: Long Live Entrepreneurial Intentions. In A.L. Carsrud & M. Brännback (Eds.), Understanding the Entrepreneurial Mind (pp. 51–72). New York, NY: Springer. Krueger, N. F. (2000). The Cognitive Infrastructure of Opportunity Emergence. Entrepreneurship Theory and Practice, 24(3), 5–24. https://doi.org/10.1177/104225870002 400301 Krueger, N. F., & Carsrud, A. L. (1993). Entrepreneurial intentions: Applying the theory of planned behaviour. Entrepreneurship & Regional Development, 5(4), 315–330. https:// doi.org/10.1080/08985629300000020 Lanero, A., Vázquez, J. L., Gutiérrez, P., & García, M. P. (2011). The impact of entrepreneurship education in European universities: an intention-based approach analyzed in the Spanish area. International Review on Public and Nonprofit Marketing, 8(2), 111–130. https://doi.org/10.1007/s12208-011-0067-8 Langer, J., Alfirevi´c, N., Paviˇci´c, J., & Krneta, M. (2016). Intentions and Perceptions of the Entrepreneurial Career Among Croatian Students: Initial Results of a Longitudinal Empirical Study. In D. Bögenhold, J. Bonnet, M. Dejardin, & D. García Pérez de Lema (Eds.), Contemporary Entrepreneurship. Multidisciplinary Perspectives on Innovation and Growth (pp. 213–228). Switzerland: Springer.

86

References

Langowitz, N., & Minniti, M. (2007). The Entrepreneurial Propensity of Women. Entrepreneurship Theory and Practice, 31(3), 341–364. https://doi.org/10.1111/j.1540-6520. 2007.00177.x Lennartz, P. (2019). Start-up-Barometer Europa (März 2019). Retrieved from Ernst & Young website: https://www.ey.com/Publication/vwLUAssets/ey-start-up-barometer-eur opa-maerz-2019/$FILE/ey-start-up-barometer-europa-maerz-2019.pdf Levie, J., & Autio, E. (2008). A theoretical grounding and test of the GEM model. Small Business Economics, 31(3), 235–263. https://doi.org/10.1007/s11187-008-9136-8 Levie, J., Hart, M., & Karim, M. S. (2010). Impact of media on entrepreneurial intentions and actions (November 2010). Retrieved from Gov.UK website: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attach ment_data/file/32243/11-773-impact-of-media-entrepreneurial-intentions-actions.pdf Lewellyn, K. B., & Muller-Kahle, M. I. (2016). A configurational approach to understanding gender differences in entrepreneurial activity: A fuzzy set analysis of 40 countries. International Entrepreneurship and Management Journal, 12(3), 765–790. https://doi. org/10.1007/s11365-015-0366-3 Lin, A. C. (1998). Bridging Positivist and Interpretivist Approaches to Qualitative Methods. Policy Studies Journal, 26(1), 162–180. https://doi.org/10.1111/j.1541-0072. 1998.tb01931.x Liñán, F., & Chen, Y.-W. (2009). Development and Cross-Cultural Application of a Specific Instrument to Measure Entrepreneurial Intentions. Entrepreneurship Theory and Practice, 33(3), 593–617. https://doi.org/10.1111/j.1540-6520.2009.00318.x Liñán, F., Santos, F. J., & Fernández, J. (2011). The influence of perceptions on potential entrepreneurs. International Entrepreneurship and Management Journal, 7(3), 373– 390. https://doi.org/10.1007/s11365-011-0199-7 Link, A. N., & Strong, D. R. (2016). Gender and entrepreneurship: An annotated bibliography. Foundations and Trends® in Entrepreneurship, 12(4–5), 287–441. https://doi. org/10.1561/0300000068 Locke, E. A., & Baum, J. R. (2007). Entrepreneurial Motivation. In J. R. Baum, M. Frese, & R. A. Baron (Eds.), The organizational frontiers. The psychology of entrepreneurship (pp. 93–112). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Lockyer, J., & George, S. (2012). What women want: barriers to female entrepreneurship in the West Midlands. International Journal of Gender and Entrepreneurship, 4(2), 179–195. https://doi.org/10.1108/17566261211234661 Low, M. B., & MacMillan, I. C. (1988). Entrepreneurship: Past Research and Future Challenges. Journal of Management, 14(2), 139–161. https://doi.org/10.1177/014920 638801400202 Luthje, C., & Franke, N. (2003). The “making” of an entrepreneur: testing a model of entrepreneurial intent among engineering students at MIT. R and D Management, 33(2), 135–147. https://doi.org/10.1111/1467-9310.00288 Malmström, M., Johansson, J., & Wincent, J. (2017). Gender Stereotypes and Venture Support Decisions: How Governmental Venture Capitalists Socially Construct Entrepreneurs’ Potential. Entrepreneurship Theory and Practice, 41(5), 833–860. https://doi. org/10.1111/etap.12275

References

87

McGrath, R. G., & MacMillan, I. C. (1992). More like each other than anyone else? A cross-cultural study of entrepreneurial perceptions. Journal of Business Venturing, 7(5), 419–429. https://doi.org/10.1016/0883-9026(92)90017-l Mertens, D. M. (2014). Mixed Methods and Wicked Problems. Journal of Mixed Methods Research, 9(1), 3–6. https://doi.org/10.1177/1558689814562944 Midi, H., Sarkar, S. K., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, 13(3), 253–267. https:// doi.org/10.1080/09720502.2010.10700699 Mood, C. (2009). Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It. European Sociological Review, 26(1), 67–82. https:// doi.org/10.1093/esr/jcp006 Morales-Gualdrón, S. T., & Roig, S. (2005). The New Venture Decision: An Analysis Based on the GEM Project Database. The International Entrepreneurship and Management Journal, 1(4), 479–499. https://doi.org/10.1007/s11365-005-4774-7 Moriano, J. A., Gorgievski, M., Laguna, M., Stephan, U., & Zarafshani, K. (2011). A Cross-Cultural Approach to Understanding Entrepreneurial Intention. Journal of Career Development, 39(2), 162–185. https://doi.org/10.1177/0894845310384481 Mro˙zewski, M. (2014). An Empirical Analysis of Economic and Socio-Demographic Determinants of Entrepreneurship Across German Regions. International Journal of Management and Economics, 42(1), 60–80. https://doi.org/10.2478/ijme-2014-0043 Nabi, G., & Liñán, F. (2011). Graduate entrepreneurship in the developing world: intentions, education and development. Education + Training, 53(5), 325–334. https://doi. org/10.1108/00400911111147668 Naidu, S., & Chand, A. (2015). National Culture, Gender Inequality and Women’s Success in Micro, Small and Medium Enterprises. Social Indicators Research, 130(2), 647–664. https://doi.org/10.1007/s11205-015-1203-3 Nelson, T., Maxfield, S., & Kolb, D. (2009). Women entrepreneurs and venture capital: managing the shadow negotiation. International Journal of Gender and Entrepreneurship, 1(1), 57–76. https://doi.org/10.1108/17566260910942345 Newbert, S. L., & Tornikoski, E. T. (2010). Supporter networks and network growth: a contingency model of organizational emergence. Small Business Economics, 39(1), 141–159. https://doi.org/10.1007/s11187-010-9300-9 Nicholson, L., & Anderson, A. R. (2005). News and Nuances of the Entrepreneurial Myth and Metaphor: Linguistic Games in Entrepreneurial Sense-Making and Sense-Giving. Entrepreneurship Theory and Practice, 29(2), 153–172. https://doi.org/10.1111/j.15406520.2005.00074.x Noguera, M., Alvarez, C., & Urbano, D. (2013). Socio-cultural factors and female entrepreneurship. International Entrepreneurship and Management Journal, 9(2), 183–197. https://doi.org/10.1007/s11365-013-0251-x O’Leary, Z. (2017). The essential guide to doing your research project (3rd ed). London: SAGE. OECD/European Union. (2017). The missing entrepreneurs 2017: Policies for inclusive entrepreneurship. Paris: OECD. https://doi.org/10.1787/9789264283602-en Olaison, L., & Meier Sørensen, B. (2014). The abject of entrepreneurship: Failure, fiasco, fraud. International Journal of Entrepreneurial Behavior & Research, 20(2), 193–211. https://doi.org/10.1108/ijebr-09-2013-0143

88

References

Ozaralli, N., & Rivenburgh, N. K. (2016). Entrepreneurial intention: antecedents to entrepreneurial behavior in the U.S.A. and Turkey. Journal of Global Entrepreneurship Research, 6(1), 1–32. https://doi.org/10.1186/s40497-016-0047-x Peng, Z., Lu, G., & Kang, H. (2012). Entrepreneurial Intentions and Its Influencing Factors: A Survey of the University Students in Xi’an China. Creative Education, 3(8), 95–100. https://doi.org/10.4236/ce.2012.38b021 Peroni, C., Riillo, C., A., F., & Rodrigues, B. (2017). Global Entrepreneurship Monitor Luxembourg 2016/2017. Retrieved from https://statistiques.public.lu/catalogue-public ations/LuxGEM/2017/PDF-GEM-2016-17.pdf Poggesi, S., Mari, M., & De Vita, L. (2017). Women entrepreneurs and work-family conflict: an analysis of the antecedents. International Entrepreneurship and Management Journal, 15(2), 431–454. https://doi.org/10.1007/s11365-017-0484-1 Popowska, M. (2015). Entrepreneurial Education and Attitude among Young Generations in a Gender perspective: the Case of Polish Education System. Erenet Profile,10(2), 8–19. Retrieved from http://www.erenet.org/publications/profile36.pdf Porter, M. E., & Stern, S. (2001). National innovative capacity. In K. Schwab, M.E. Porter, & J.D. Sachs (Eds.), The Global Competitiveness Report 2001–2002 (pp. 102–118). Oxford: Oxford University Press Pugh, D. S. & Hickson, D. J. (1976). Organizational structure: Extensions and replication. Westmead: Saxon House. Radu, M., & Redien-Collot, R. (2008). The Social Representation of Entrepreneurs in the French Press. International Small Business Journal: Researching Entrepreneurship, 26(3), 259–298. https://doi.org/10.1177/0266242608088739 Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., … Chin, N. (2005). Global Entrepreneurship Monitor: Data Collection Design and Implementation 1998?2003. Small Business Economics, 24(3), 205–231. https://doi.org/10.1007/ s11187-005-1980-1 Riger, S. (1992). Epistemological debates, feminist voices: Science, social values, and the study of women. American Psychologist, 47(6), 730–740. https://doi.org/10.1037/0003066x.47.6.730 Ritchie, J., & Lewis, J. (2003). Qualitative Research Practice—A Guide for Social Science Students and Researchers. London: Sage Publications Ltd. Ruth Eikhof, D., Summers, J., & Carter, S. (2013). “Women doing their own thing”: media representations of female entrepreneurship. International Journal of Entrepreneurial Behavior & Research, 19(5), 547–564. https://doi.org/10.1108/ijebr-09-2011-0107 Salmerón Gómez, R., García Pérez, J., López Martín, M. D. M., & García, C. G. (2016). Collinearity diagnostic applied in ridge estimation through the variance inflation factor. Journal of Applied Statistics, 43(10), 1831–1849. https://doi.org/10.1080/02664763. 2015.1120712 Santos, F. J., Roomi, M. A., & Liñán, F. (2014). About Gender Differences and the Social Environment in the Development of Entrepreneurial Intentions. Journal of Small Business Management, 54(1), 49–66. https://doi.org/10.1111/jsbm.12129 Sarasvathy, S. D. (2004). The questions we ask and the questions we care about: reformulating some problems in entrepreneurship research. Journal of Business Venturing, 19(5), 707–717. https://doi.org/10.1016/j.jbusvent.2003.09.006

References

89

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business students (8th ed). New York, NY: Prentice Hall. Sayer, A. (2000). Realism et social science. London: Sage. Schjoedt, L., Renko, M., & Shaver, K. G. (2014). Looking into the future: valid multipleand single-item measures in entrepreneurship research. In A. Carsrud & M. Brännback (Eds.), Handbook of Research Methods and Applications in Entrepreneurship and Small Business (pp. 112–135). Cheltenham: Edward Elgar. Schlaegel, C., & Koenig, M. (2013). Determinants of Entrepreneurial Intent: A MetaAnalytic Test and Integration of Competing Models. Entrepreneurship Theory and Practice, 38(2), 291–332. https://doi.org/10.1111/etap.12087 Schmutzler, J., Andonova, V., & Diaz-Serrano, L. (2018). How Context Shapes Entrepreneurial Self-Efficacy as a Driver of Entrepreneurial Intentions: A Multilevel Approach. Entrepreneurship Theory and Practice, 43(5), 880–920. https://doi.org/10.1177/104225 8717753142 Scumpeter, J. A. (1934). The Theory of Economic Development. Cambridge, MA: Harvard University Press Shane, S., & Venkataraman, S. (2000). The Promise of Entrepreneurship as a Field of Research. The Academy of Management Review, 25(1), 217–226. https://doi.org/10. 2307/259271 Shapero, A. & Sokol, L. (1982). The social dimensions of entrepreneurship. In C. Kent, D. Sexton & K. H. Vesper (Eds.), The encyclopedia of entrepreneurship (pp. 72–90). Englewood Cliffs, NJ: Prentice-Hall. Sharma, L. (2018). Entrepreneurial intentions and perceived barriers to entrepreneurship among youth in Uttarakhand state of India. International Journal of Gender and Entrepreneurship, 10(3), 243–269. https://doi.org/10.1108/ijge-02-2018-0009 Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures (5th ed.). Boca Raton, FL: Chapman & Hall/CRC Press. Shinnar, R. S., Giacomin, O., & Janssen, F. (2012). Entrepreneurial Perceptions and Intentions: The Role of Gender and Culture. Entrepreneurship Theory and Practice, 36(3), 465–493. https://doi.org/10.1111/j.1540-6520.2012.00509.x Shneor, R., Metin Camgöz, S., & Bayhan Karapinar, P. (2013). The interaction between culture and sex in the formation of entrepreneurial intentions. Entrepreneurship & Regional Development, 25(9–10), 781–803. https://doi.org/10.1080/08985626.2013. 862973 Shook, C. L., Priem, R. L., & McGee, J. E. (2003). Venture Creation and the Enterprising Individual: A Review and Synthesis. Journal of Management, 29(3), 379–399. https:// doi.org/10.1016/s0149-2063_03_00016-3 Smithson, M., & Verkuilen, J. (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods, 11(1), 54–71. https://doi.org/10.1037/1082-989x.11.1.54 Sousa,F. J. (2010). Chapter 9 Metatheories in research: positivism, postmodernism, and critical realism. In A. G. Woodside (Ed.), Organizational culture, business-to-business relationships, and interfirm networks (pp. 455–503). Bingley: Emerald Group. Stead, V. (2015). Belonging and women entrepreneurs: Women’s navigation of gendered assumptions in entrepreneurial practice. International Small Business Journal, 35(2), 61–77. https://doi.org/10.1177/0266242615594413

90

References

Stevenson, H. H., & Jarillo, J. C. (2007). A Paradigm of Entrepreneurship: Entrepreneurial Management*. In Á. Cuervo, D. Ribeiro, & S. Roig (Eds.), Entrepreneurship, (pp. 155– 170). Heidelberg: Springer. Stevenson, H. H., Roberts, M. J., & Grousbeck, H. I. (1989). New Business Ventures and the Entrepreneur. Homewood, IL: Irwin Stryker, S., & Burke, P. J. (2000). The Past, Present, and Future of an Identity Theory. Social Psychology Quarterly, 63(4), 284–297. https://doi.org/10.2307/2695840 Sweden Institution (2019). Gender equality in Sweden. Retrieved from https://sweden.se/ society/gender-equality-in-sweden/ Thurik, A. R., Carree, M. A., van Stel, A., & Audretsch, D. B. (2008). Does selfemployment reduce unemployment? Journal of Business Venturing, 23(6), 673–686. https://doi.org/10.1016/j.jbusvent.2008.01.007 Trevelyan, R. (2011). Self-efficacy and effort in new venture development. Journal of Management & Organization, 17(1), 2–16. https://doi.org/10.1017/s1833367200001681 Trojnarska, K. & Halabisky, D. (2015). Youth Entrepreneurship Support in Poland: Rapid Policy Assessments of Inclusive Entrepreneurship Policies and Programmes. Retrieved from OECD website: https://www.oecd.org/poland/Rapid-policy-assessment-Pol and-Final.pdf Tumasjan, A., & Braun, R. (2012). In the eye of the beholder: How regulatory focus and self-efficacy interact in influencing opportunity recognition. Journal of Business Venturing, 27(6), 622–636. https://doi.org/10.1016/j.jbusvent.2011.08.001 Ucbasaran, D., Westhead, P., & Wright, M. (2007). Opportunity Identification and Pursuit: Does an Entrepreneur’s Human Capital Matter? Small Business Economics, 30(2), 153– 173. https://doi.org/10.1007/s11187-006-9020-3 Urbano, D., Alvarez, C., & Turró, A. (2013). Organizational resources and intrapreneurial activities: an international study. Management Decision, 51(4), 854–870. https://doi. org/10.1108/00251741311326617 Van der Veen, M. & Wakkee, I. (2006). Understanding the entrepreneurial process. In P. Davidsson (Ed.), New Firm Startups (pp. 27–65). Cheltenham: Edward Elgar. Van Gelderen, M., & Jansen, P. (2006). Autonomy as a start-up motive. Journal of Small Business and Enterprise Development, 13(1), 23–32. https://doi.org/10.1108/146260 00610645289 Van Praag, C. M., & Versloot, P. H. (2007). What is the value of entrepreneurship? A review of recent research. Small Business Economics, 29(4), 351–382. https://doi.org/ 10.1007/s11187-007-9074-x Venkataraman, S. (1997). The distinctive domain of entrepreneurship research. In J. A. Katz (Ed.), Advances in Entrepreneurship Research: Firm Emergence and Growth (pp. 119–138). Greenwich, CT: JAI Press. Wagner, J. (2006). What a Difference a Y makes-Female and Male Nascent Entrepreneurs in Germany. Small Business Economics, 28(1), 1–21. https://doi.org/10.1007/s11187005-0259-x Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82(2), 247–252. https://doi. org/10.1037/0021-9010.82.2.247

References

91

Wennberg, K., Pathak, S., & Autio, E. (2013). How culture moulds the effects of self-efficacy and fear of failure on entrepreneurship. Entrepreneurship & Regional Development, 25(9–10), 756–780. https://doi.org/10.1080/08985626.2013.862975 Wickham, P. (2006). Strategic Entrepreneurship (4th ed). Essex: Pearson Education Ltd. Wilson, F., Kickul, J., & Marlino, D. (2007). Gender, Entrepreneurial Self-Efficacy, and Entrepreneurial Career Intentions: Implications for Entrepreneurship Education. Entrepreneurship Theory and Practice, 31(3), 387–406. https://doi.org/10.1111/j.1540-6520. 2007.00179.x Wu, J., Li, Y., & Zhang, D. (2019). Identifying women’s entrepreneurial barriers and empowering female entrepreneurship worldwide: a fuzzy-set QCA approach. International Entrepreneurship and Management Journal, 15(3), 905–928. https://doi.org/10. 1007/s11365-019-00570-z Wyrwich, M., Stuetzer, M., & Sternberg, R. (2016). Entrepreneurial role models, fear of failure, and institutional approval of entrepreneurship: A tale of two regions. Small Business Economics, 46(3), 467–492. https://doi.org/10.1007/s11187-015-9695-4 Yadav, V., & Unni, J. (2016). Women entrepreneurship: research review and future directions. Journal of Global Entrepreneurship Research, 6(1), 1–18. https://doi.org/10.1186/ s40497-016-0055-x Yang, J. (2013). The Theory of Planned Behavior and Prediction of Entrepreneurial Intention Among Chinese Undergraduates. Social Behavior and Personality: An International Journal, 41(3), 367–376. https://doi.org/10.2224/sbp.2013.41.3.367 Zahra, S., & Dess, G. G. (2001). Entrepreneurship As a Field of Research: Encouraging Dialogue and Debate. Academy of Management Review, 26(1), 8–10. https://doi.org/ 10.5465/amr.2001.4011916 Zamberi Ahmad, S., Roland Xavier, S., & Rahim Abu Bakar, A. (2014). Examining entrepreneurial intention through cognitive approach using Malaysia GEM data. Journal of Organizational Change Management, 27(3), 449–464. https://doi.org/10.1108/jocm-032013-0035