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Research Based on The Global Entrepreneurship Monitor Data:

An Author Co-Citation Analysis

Claudia Alvarez

Autonomous University of Barcelona Business Economics Department, Spain and

University of Medellin, School of Economics and Management, Colombia Building B 08193 Bellaterra (Cerdanyola del Vallés) - Barcelona

Tel. 935811209 / Fax. 935812555 claudiapatricia.alvarez@uab.es

David Urbano

Autonomous University of Barcelona Business Economics Department, Spain

José Ernesto Amorós

Universidad del Desarrollo School Business and Economics, Chile

Abstract

Global Entrepreneurship Monitor (GEM) Project covers one of the most relevant gaps in the research on entrepreneurship, such as, the lack of international data on entrepreneurial activity and their analysis. Thus, the aim of this paper is to identify the intellectual structure of the research based on the GEM data through co-citation analysis. More specifically, this study delineates the subfields that constitute the research based on the GEM data, determine the relationship between the subfields and identify main authors in domains of research. Our analysis of GEM-based peer-reviewed works included on SSCI has revealed that GEM is increasingly being incorporated into high quality scholarship. If well there exists a type of “endogamy on the co-citations and journals (mainly Small Business Economics) more recent publication of GEM-based articles in different journals by scholars within and outside the GEM consortium are a good example of the evolution of GEM-based research.

Keywords: Bibliometrics, co-citation, Global Entrepreneurship Monitor.

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1. Introduction

Global Entrepreneurship Monitor (GEM) covers one of the most relevant gaps in the research on entrepreneurship, such as, the lack of international data on entrepreneurial activity and their analysis (Reynolds et al., 2005; Bosma et al., 2010). The GEM project, created by researchers at Babson College (USA) and London Business School (UK) in 1999, provides internationally comparable data on entrepreneurial activity (Reynolds et al. 1999, 2005). Specifically, the purpose of the GEM project is to analyze the level of entrepreneurial activity across countries and over time, exploring the relationship between entrepreneurship and economic growth. Since its inception one of the main contributions of the GEM project is the development of harmonized, consistent, and internationally yearly-based comparable measures of entrepreneurial activity. To produce this data GEM adopts two main principles that differentiate its measures from other international databases: the first guiding principle is that entrepreneurship is a process, and the second is that entrepreneurial activities are undertaken by individuals

1. Both principles have been explicitly involved on the work of scholar

research that use GEM measures like a main source of empirical data and also on the conceptualization of entrepreneurship like a one source of economic development (Davidsson and Wiklund, 2001

2; Bosma et al., 2009; Bosma and Levie, 2010).

This research complements a recent study by Álvarez et al. (2010), that analyzes the content and evolution of research based on the GEM project. Also, this study is aligned with the research of Acs et al. (2009), Álvarez and Urbano (2011), Amorós et al. (2011), and Urbano et al. (2010), which study the scientific articles that use GEM framework, concepts and measures to produce advances on academic entrepreneurship knowledge. Thus, the aim of this paper is to identify the intellectual structure of the research based on the GEM data through co-citation analysis. Concretely, this paper delineates the subfields that constitute the research based on the GEM project, determining the relationship between the subfields and identifying the main authors in domains of research.

The technique of co-citation analysis was introduced into science studies by Small (1973) and Small and Griffith (1974). Co-citation analysis shows that different patterns of knowledge combination exist in different management journals (Tsai and Wu, 2010: 441). Co-citation analysis is a bibliometric technique that uses reference data to identify the connections among a group of articles. Co-citation occurs when two earlier articles are cited together by a later article. This event suggests that the later article has combined knowledge from the two earlier articles. Thus, co-citation reflects how prior works relate to each other as they both influence the same later article (Tsai and Wu, 2010: 441)

Co-citation analysis have been used in several fields by example information systems (Culnan, 1987), organization behavior (Culnan et al., 1990), economics (Cahlik, 2000; McCain, 1991), consumer research (Hoffman and Holbrook, 1993) information science (White and McCain, 1998), neuroscience (Braun et al, 2001), research on semiconductors (Tsay et al., 2003), strategic management (Ramos-Rodríguez and Ruiz-Navarro, 2004 and Nerur et al., 2008), international management (Acedo and Casillas, 2005), family business (Casillas and Acedo, 2007), among others. Specifically in the field of entrepreneurship, the special issue of Entrepreneurship Theory and Practice (Volume 30, Issue 3; 2006) focuses on how entrepreneurship scholarship is organized by entrepreneurship scholars using bibliometric techniques to find linkages among published entrepreneurship scholars (Gartner et al, 2006). However, there are still few studies that center their bibliometric analysis on papers using GEM data.

After this brief introduction, the structure of the article is as follows. First, the methodology used is described. Second, we present the results of this study. Finally, we highlight the most relevant conclusions of the research.

1This differentiates GEM Project from other sources of data that analyze the formal registration of new firms. For

an explanation about these differences see Bosma et al., 2009 p. 12 “Main distinctions between GEM Adult population Survey Data and Business Registration Data”.

2 Davidsson and Wiklund´s work entitle “Levels of analysis in entrepreneurship research: Current research practice

and suggestions for the future” was one of the first peer-reviewed papers to mention GEM as a potential source of data for future research on entrepreneurship topics.

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2. Methodology

As we mentioned before, the methodology used in this research is based on the bibliometric techniques of citation and co-citation analysis focusing on articles as the units of analysis. We used all the citations that are included in the Social Science Citation Index, SSCI, based on the journals included on the Journal Citation Report (JCR)

3. Then, we identify influential authors whose scholarly

works have been frequently cited and the intellectual structure of the research based on the GEM data field and its evolution over time are then assessed in terms of the relationships between these authors.

The author co-citation analysis (ACA) considers the bibliographic references cited in research documents, as an indicator of the sources of information used and the ideas or approaches connected or integrated in their researches. Co-citation is a measure of the association between pairs of frequently-cited documents. In turn, the frequency of co-citation is a measure of the proximity between authors. The strength of co-citation is defined as the number of times that two documents have been cited together: it provides a natural and quantitative way to group or cluster the cited documents. Co-citation identifies relationships between papers which are regarded as important by authors in the specialty, but which are not identified by such techniques as bibliographic coupling (when two papers cite one or more papers in common) or direct citation (the citing of an earlier paper by a later paper) (Small and Griffith, 1974). The intellectual map is thus a representation of ideational interactions among authors established through their frequency of co-citations and the overall distribution of co-citations that they share with one another (McCain, 1990; White and McCain, 1998).

According to McCain (1990: 434) an author co-citation analysis includes six steps, thus: selection of authors, retrieval of co-citation frequencies, compilation of a raw citation matrix, conversion to correlation matrix, multivariate analysis of correlation matrix, interpretation and validation.

Several ACA studies use a combination of subjective and objective measures to identify the key authors. Early research on the content and evolution of research based on the GEM Project (Alvarez et al. 2010) identified 74 articles which used GEM data in their empirical sections. This list was reduced according to the most cited article in SSCI, specifically we consider articles with 5 or more citations and we excluding book reviews and conferences papers

4. Thus, we use 22 articles with 492

citations in SSCI. Table 1 provides an alphabetical listing of the articles thus identified.

Data are obtained from counting the number of times any two selected articles are cited together in an article. ACA suggests that the conceptual similarity in the articles would increase the likelihood of their being cited together (McCain,1990) then frequency of co-citation is a measure of the proximity between authors. The principal diagonals were determined according White and Griffith‟s (1981) who propose a value based on the division of the three highest values for that row (or column) by two. The next step in mapping co-cited authors is the conversion of the raw data matrix to a matrix proximity values, using the Pearson correlation coefficient.

3 Both SCCI and JRC are part of Thomson Reuters Web of Knowledge

SM (formerly ISI Web of Knowledge) that is

a unified research platform to find, analyze, and share information in the sciences, social sciences, arts, and humanities. More information at: http://wokinfo.com/

4 Specifically, we consider citation in Science Citation Index Expanded (SCIE), Social Science Citation Index

(SSCI), and Arts & Humanities Citation Index (A&HCI), and exclude citation in Conference Proceedings Citation Index - Science (CPCI-S); Conference Proceedings Citation Index - Social Science & Humanities (CPCI-SSH).

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Table 1. Alphabetical list of articles selected for analysis

Author(s) ISI

citation Author(s)

ISI citation

Acs and Szerb (2007) 14 Levie (2007) 12

Acs and Varga (2005) 18 Levie and Autio (2008) 11

Acs Desai and Hessels (2008) 9 Reynolds et al. (2005) 67

Acs et al. (2007) 11 Rocha and Sternberg (2005) 17

Arenius and De Clercq (2005) 31 Sternberg and Litzenberger (2004) 20

Arenius and Minniti (2005) 40 Sternberg and Wennekers (2005) 21

Bowen and De Clercq (2008) 19 van Stel, Carree and Thurik (2005) 43

De Clercq and Arenius (2006) 11 van Stel, Storey and Thurik (2007) 19

Koellinger (2008) 6 Verheul et al. (2006) 12

Koellinger, Minniti and Schade (2007) 22 Wennekers et al. (2005) 43

Langowitz and Minniti (2007) 11 Wong Ho and Autio (2005) 35

Total 492

According to McCaine (1990), we use and compare three approaches to multivariate analysis to display the inter-author relationships in our correlation matrix: cluster analysis, multidimensional scaling, and factor (principal components) analysis.

3. Results

Initially, we show in Table 2 the citations frequency by articles selected. We highlight three important trends. First, the articles whose importance in research based on the GEM data do not have many variations, for example Reynolds et al. (2005), Rocha and Sternberg (2005), and Sternberg and Litzenberger (2004). Second, the articles that have increased the number of citations in recent years, for example Arenius and De Clercq (2005), Arenius and Minniti (2005), Bowen and De Clercq (2008), and Koellinger, Minniti and Schade (2007). Finally, the papers whose relevance seems to diminish based on the number of citations, for example Sternberg and Wennekers (2005), and Wennekers, et al.

(2005).

Also, Table 3 presents the citation frequency by journals. The results indicate that the number of citations per year is determined basically by Small Business Economics (81%). This journal has a key role on the drive of GEM-based research basically by result of the three special issues published (Sternberg and Wennekers, 2005; Acs and Szerb, 2007; Acs, Desai and Hessels, 2008).

The intellectual structure of research based on GEM data

Cluster analysis is used to provide insights into the intellectual structure of a research field (McCaine, 1990). Applying this technique in our research, the main findings reveal six clusters of articles (see Table 4).

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Table 2. Citation frequency by articles and years

Articles 2005 2006 2007 2008 2009 2010 2011 Total

No %

Acs and Szerb (2007)

6 2 1 5 14 3%

Acs and Varga (2005)

2 4 5 2 2 3 18 4%

Acs Desai and Hessels (2008)

1

4 4 9 2%

Acs, et al. (2007)

6 3

2 11 2%

Arenius and De Clercq (2005)

2 2 6 5 10 6 31 6%

Arenius and Minniti (2005)

4 5 6 8 11 6 40 8%

Bowen and De Clercq (2008)

2 4 9 4 19 4%

De Clercq and Arenius (2006)

3 3 3 2 11 2%

Koellinger (2008)

2 1 3 6 1%

Koellinger, Minniti and Schade (2007)

4 6 9 3 22 4%

Langowitz and Minniti (2007)

1 1 3 5 1 11 2%

Levie (2007)

1 4 4 3 12 2%

Levie and Autio (2008)

2 2 2 5 11 2%

Reynolds et al. (2005)

2 16 14 12 10 13 67 14%

Rocha and Sternberg (2005)

1 2 4 3 5 2 17 3%

Sternberg and Litzenberger (2004) 1 3 3 4 2 3 4 20 4%

Sternberg and Wennekers (2005)

0 8 4 4 2 3 21 4%

van Stel, Carree and Thurik (2005)

1 8 13 5 9 7 43 9%

van Stel, Storey and Thurik (2007)

5 4 4 6 19 4%

Verheul, van Stel and Thurik (2006)

3 3 4 1 1 12 2%

Wennekers et al. (2005)

1 10 16 4 4 8 43 9%

Wong, Ho and Autio (2005)

5 5 5 15 5 35 7%

Total 1 16 67 111 87 114 96 492 100%

Mean 1.0 1.8 5.6 5.3 4.1 5.4 4.4 22.4

Standard deviation

1.2 4.3 4.2 2.4 4.0 2.7 15.0

Table 3. Citation frequency by journals and years

Cited journal 2005 2006 2007 2008 2009 2010 2011 Total

No %

Entrepreneurship and Regional Development

3 3 4 1 1 12 2%

Entrepreneurship Theory and Practice

1 1 3 5 1 11 2%

European Planning Studies 1 3 3 4 2 3 4 20 4%

International Small Business Journal

3 3 3 2 11 2%

Journal of Economic Psychology

4 6 9 3 22 4%

Journal of International Business Studies

2 4 9 4 19 4%

Small Business Economics

13 60 94 65 84 81 397 81%

Total 1 16 67 111 87 114 96 492 100%

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Table 4. Clusters of research based on the GEM data

Cluster A

Acs and Szerb 2007

Cluster C

Arenius and Minniti 2005

Acs and Varga 2005 Koellinger Minniti and Schade 2007

Acs Desai Hessels 2008 Levie 2007

Acs O'Gorman Szerb Terjesen 2007 Reynolds et al. 2005

Koellinger 2008

Cluster D

Rocha and Sternberg 2005

Langowitz and Minniti 2007 Sternberg and Litzenberger 2004

Levie and Autio 2008 Sternberg and Wennekers 2005

van Stel Storey and Thurik 2007 Wong Ho and Autio 2005

Verheul, van Stel and Thurik 2006 Cluster E

van Stel Carree and Thurik 2005

Cluster B

Arenius and De Clercq 2005 Wennekers et al. 2005

Bowen and De Clercq 2008

De Clercq and Arenius 2006

A two-dimensional map of the correlation matrix visually reflects the underlying structure of the data. For constructing distance-based maps, multidimensional scaling is commonly used in the field of bibliometrics, but an alternative to multidimensional scaling is the VOS mapping technique (van Eck and Waltman, 2010). The construction of a map consists of three steps: 1) a similarity matrix is calculated based on the co-occurrence matrix, 2) a map is constructed by applying the VOS mapping technique to the similarity matrix, and 3) the map is translated, rotated, and reflected (van Eck and Waltman, 2010). Figure 1 shows a two-dimensional map of the research based on GEM, derived from the same correlation matrix used to generate the clustering solution in Table 4. In the map, point reveals the proximity in the author co-citation matrix, thus, similar authors will be close on the map. Articles with many links to others will be placed near the center of the map, for example, Reynolds et al. (2005), van Stel, Carree and Thurik (2005) and Wennekers et al. (2005). Articles as Koellinger, Minniti and Schade (2007), which are near to extremes are related through co-citation to fewer neighbors.

Defining the field of entrepreneurship: underlying themes

To reveal the themes that characterize and define research based on GEM data, factor analysis was used to identify those authors making significant contributions to a theme. Only authors with loadings greater than ±0.7 are likely to be useful in interpreting the factor (McCain, 1990). We use a principal components analysis, with and orthogonal (varimax) rotation.

Table 5 shows the eight factors with author loadings of 0.50 and higher; 0.70 and above are shown in bold text. Table 5 shows the results of a factor analysis of the research based on the GEM data. As expected, the results were quite similar to the clustering and mapping analyses showed in Table 4 (McCain, 1990).

The groups include: Factor 1, the relationship between entrepreneurial activity and economic development; Factor 2, effects of network, perceptual variables and ethnicity on entrepreneurial activity; Factor 3, effects of environmental factors on entrepreneurial activity; Factor 4, clusters (regional industrial clusters) and entrepreneurial activity; Factor 5, female entrepreneurship.

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Figure 1. Two-dimensional map of the correlation matrix for research based on the GEM data authors

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Table 5. Factor analysis

Factor 1

Factor 2

Acs and Szerb 2007 0.65

Arenius and Minniti 2005 0.95 Acs and Varga 2005 0.85

Arenius and De Clercq 2005 0.93

Acs Desai Hessels 2008 0.39

Bowen and De Clercq 2008 0.54 Acs et al. 2007 0.72

De Clercq and Arenius 2006 0.69

Reynolds et al. 2005 0.79

Koellinger, Minniti and Schade 2007 0.83 Sternberg and Wennekers 2005 0.93

Levie 2007 0.84

van Stel, Carree and Thurik 2005 0.88

Verheul, van Stel and Thurik 2006 0.90

Wennekers et al. 2005 0.92

Wong Ho and Autio 2005 0.78

5 Note: Simplified plot showing ~63% of data points for clarity in figure

A B

C

E

D

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Factor 3 Factor 4

Bowen and De Clercq 2008 0.68 Rocha and Sternberg 2005 0.91 De Clercq and Arenius 2006 0.60 Sternberg and Litzenberger 2004 0.69 Koellinger 2008 0.82 Levie and Autio 2008 0.79 van Stel, Storey and Thurik 2007 0.79

Factor 5

Langowitz and Minniti 2007 0.96

To analyze the intercorrelations among articles, we show the Pearson correlation in Table 6. The results show a high correlation (> 0.8) between the representative articles: Reynolds et al. (2005), van Stel, Carree and Thurik (2005) and Wennekers et al. (2005). Also, we observe high correlations (>0.9) between articles as Arenius and De Clercq (2005) and Arenius and Minniti (2005); Bowen and De Clercq (2008) and De Clercq and Arenius (2006); Acs and Varga (2005) and Sternberg and Wennekers (2005).

4. Conclusions

Our analysis of GEM-based peer-reviewed works included on SSCI has revealed that GEM is increasingly being incorporated into high quality scholarship. The purpose of this study was to identify the intellectual structure of the research based on the GEM data through co-citation analysis. If well there exists a type of “endogamy on the co-citations and journals (mainly Small Business Economics) more recent publication of GEM-based articles in different journals by scholars within and outside the GEM consortium are a good example of the evolution of GEM-based research

6. GEM‟s current efforts

in making the methodology and data more accessible7 to scholars outside GEM consortium will

probably lead to a new enhancement in this research domain. As we explain in the introduction the advantage of GEM data is that different types of entrepreneurial activity (entrepreneurship is a process) as practiced by individuals can be examined, across countries or regions and over time.

Our main findings of the research reveal: (1) the specialty structure of the GEM-based research over 10 years; (2) authors' memberships in one or more sub-topics; (3) the major sub-disciplines of research based on the GEM; (4) the main authors who are in the top in all years; and (5) the evidence on the general nature and state of integration of research based on the GEM.

Then, these results show, first, that research based on the GEM is still fragmented, probably because it is in early stages of development; and second, the difficulty of categorizing all subfields, “problem” that is also shared by the entrepreneurship discipline. Based on these two general findings, we underline some conclusions.

6 A good example is a recent book edited by Maria Minniti that provides a comprehensive multidisciplinary and

multi-topic overview of the current GEM-based scholarship entrepreneurship research (Minniti, 2011).

7 Data-bases are available at GEM´s Web page www.gemconsortium.org

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Table 6. Pearson correlation between articles

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21 22.

1. Acs and Szerb 2007 1.0

2. Acs and Varga 2005 0.8 1.0

3. Acs Desai Hessels 2008 0.6 0.6 1.0

4. Acs et al. 2007 0.8 0.7 0.3 1.0

5. Arenius and De Clercq 2005 0.2 0.3 0.0 0.3 1.0

6. Arenius and Minniti 2005 0.2 0.3 -0.2 0.2 0.9 1.0

7. Bowen and De Clercq 2008 0.4 0.2 0.0 0.5 0.7 0.6 1.0

8. De Clercq and Arenius 2006 0.3 0.3 -0.1 0.5 0.8 0.6 0.9 1.0

9. Koellinger 2008 0.5 0.4 0.4 0.4 0.5 0.5 0.5 0.4 1.0

10. Koellinger, Minniti and Schade 2007 0.1 0.1 -0.2 0.1 0.5 0.7 0.3 0.3 0.2 1.0

11. Langowitz and Minniti 2007 0.3 0.3 0.4 0.2 0.5 0.3 0.0 0.2 0.5 0.2 1.0

12. Levie 2007 0.1 0.3 -0.1 0.2 0.7 0.8 0.3 0.4 0.4 0.5 0.4 1.0

13. Levie and Autio 2008 0.8 0.6 0.6 0.7 0.4 0.3 0.6 0.5 0.7 0.0 0.3 0.2 1.0

14. Reynolds et al. 2005 0.6 0.7 0.3 0.6 0.7 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.5 1.0

15. Rocha and Sternberg 2005 0.1 0.4 0.4 0.1 0.3 0.2 0.0 0.1 0.3 -0.1 0.3 0.2 0.2 0.3 1.0

16. Sternberg and Litzenberger 2004 0.2 0.3 0.0 0.2 0.1 0.3 0.1 0.0 0.1 0.1 -0.1 0.1 0.1 0.3 0.6 1.0

17. Sternberg and Wennekers 2005 0.7 0.9 0.4 0.7 0.4 0.4 0.3 0.3 0.4 0.3 0.2 0.3 0.5 0.8 0.5 0.4 1.0

18. van Stel, Carree and Thurik 2005 0.6 0.7 0.5 0.6 0.4 0.2 0.5 0.5 0.5 0.3 0.1 0.1 0.6 0.8 0.4 0.1 0.8 1.0

19. van Stel, Storey and Thurik 2007 0.6 0.5 0.5 0.5 0.5 0.3 0.6 0.6 0.6 0.2 0.3 0.3 0.8 0.6 0.2 -0.1 0.4 0.7 1.0

20. Verheul, van Stel and Thurik 2006 0.5 0.6 0.3 0.6 0.4 0.3 0.3 0.4 0.4 0.2 0.4 0.3 0.4 0.7 0.3 0.0 0.8 0.8 0.5 1.0

21. Wennekers et al. 2005 0.6 0.7 0.4 0.7 0.5 0.4 0.5 0.6 0.5 0.4 0.2 0.3 0.6 0.8 0.4 0.2 0.9 1.0 0.7 0.8 1.0

22. Wong, Ho and Autio 2005 0.5 0.7 0.5 0.5 0.4 0.3 0.3 0.3 0.5 0.2 0.2 0.2 0.5 0.7 0.7 0.5 0.8 0.8 0.5 0.6 0.8 1.0

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Related to under-represented specific topics it is interesting that the entrepreneurial framework conditions (EFCs) that are identified in the GEM model are under-researched. Very few examples use systematically this source of data (i.e. Levie and Autio, 2008) and few studies use institutional economics as theoretical framework (i.e. Álvarez et al., 2011; Thornton et al., 2011). Another under-researched item includes the discontinuity of businesses. Future analysis could identify what determines specific drivers of business discontinuity, as well as the direct and indirect relationships between discontinuities and new business creation. It is also noteworthy that only one of the most citied papers is related to female entrepreneurship (Langowitz and Minniti, 2007). This sub-topic (and other socio-demographic variables related to entrepreneurship diversity, such as immigrants, ethnic groups or youths) could constitute an important source of future research on the basis that practically all the individuals inquired by GEM give at least basic demographic information.

With the increasing number of developing economies participating on GEM Project, future research should be focused on the relationship between entrepreneurship and the interested specific topics of these countries. It is also expected that GEM data can be used progressively for researching the relationships between institutions, entrepreneurship and development. The increasing number of participant countries could provide more inputs to examine the national levels of entrepreneurial activity. Another research area that is likely to develop studies at the sub-national level. As databases for several years are pooled, more country databases will have sufficient sample sizes to identify regional differences in their entrepreneurial dynamics. Moreover, the availability of several years of data with the use of more advanced statistical methods, such as dynamic time series analysis and multilevel regression techniques should be employed, that acknowledge the micro-macro relations between entrepreneurship and economic growth, will be an unique source of high-quality research. In addition the possibility to combine GEM data with other secondary sources of data could also increase the spectrum for potential research (Acs and Szerb, 2011; Koellinger and Thurik, 2009).

Other interesting result is although GEM database is a source of reference in the field of entrepreneurship research, only recently start to seem publications using GEM data in the top management (but also economics) JCR journals. In this sense, this could be an interesting “niche” for future research. Finally, the GEM project itself will need to constantly innovate both in measures and in methods if it is to remain at the forefront of international research in entrepreneurship and economic development.

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