The journals were selected in order to analyze bibliographic information. For the purpose of this study, data were collected from the Social Science Citation Index compiled by the online Web of Science (WoS) database, which contain thousands of scholarly publications and bibliographic information with regard to authors, affiliations, and quotations. Previous research has shown that the simultaneous use of other relevant databases does not increase the amount of relevant documents captured due to the duplications that occur among the databases (Harzing & Alakangas, 2016). Thus, this study's literature survey was conducted using only the WoS Core Collection database.

To undertake the literature survey were carried out different search steps following Riera and Iborra (2017). First the documents were retrieved using some related terms. In this case, documents with the generic term “entrepren* education”, appeared as a social science topic (i.e., as keywords in document titles, keywords, and abstracts). Second the “type of document” was specified as the “article” and “review” categories. And finally, the documents were selected from the discipline categories most commonly used to classify journals that cover EE research including the WoS subcategories of “business,” “management,” “economics,” “education educational research,” “psychology educational,” or “education scientific disciplines.” Using these search criteria, the study obtained 325 articles that formed the basis of the bibliometric analysis, and searched for and counted the articles in the database on January 15, 2018. The oldest article in the dataset is the starting point of the analysis. It was an article published in the Journal of Business Venturing, titled “Entrepreneurship education in the nineties” (McMullan & Long, 1987). This article traces the history of EE in the nineties by reviewing propositions from individuals who had begun to use EE as a part of an economic development strategy.

Bibliometric analysis is defined as a part of scientometrics, which utilizes mathematical and statistical methods to analyze scientific activities in a research field (Callon, Courtial, & Laville, 1991; López-Fernández et al., 2016). Bibliometrics provides a general picture of a research field that can be classified by papers, authors, and journals (Merigo & Yang, 2017). Bibliometric methods involve two main approaches: a performance analysis and a graphic mapping of science or bibliometric mapping (Noyons, Moed, & Luwel, 1999). On the one hand, the performance analysis evaluates the impact of citations of the scientific production made by the different actors that interact in a research field. These actors can be countries, universities, departments and, of course, researchers. The most popular indicators are those that consider the number of publications and the number of citations (Yu & Shi, 2015). The number of publications is correlated to the productivity of the author, while the number of citations is correlated to its influence on the scientific community (Merigo & Yang, 2017). On the other hand, science mapping aims to illustrate the structure and dynamics of scientific fields (Zupic & Čater, 2015). It is a spatial representation of how disciplines, fields, specialties, and documents or authors are related to one another (Moral-Muñoz, Cobo, Peis, Arroyo-Morales, & Herrera-Viedma, 2014).

For conducting this study, the Science Mapping Analysis Tool (SciMAT) (Cobo, López-Herrera, Herrera-Viedma, & Herrera, 2012) was used. The software reports performance indicators and a co-word analysis, which is based on the assumption that an article's keywords constitute an appropriate description of its contents. The co-occurrence of two keywords in the same article is an indication of a link between the issues referred to in the article, and reveals the patterns and trends of a specific discipline by measuring the strength of association between the representative terms of relevant publications produced in the same area (Zupic & Čater, 2015).

The thematic networks or clusters generated during the co-word analysis have allowed the synthesis and organization of existing knowledge in this field and the identification of potential avenues for future research. The identification of future research streams or key research topics is systematically carried out using SciMAT; that is the main differential characteristic of this bibliometric software compared to others (i.e. Vosviewer, Biexcel).

Research activity was measured by the number of publications during a particular period. Fig. 1 shows the distribution of EE publications from 1987 to 2017 that were used in the study. The changes in productivity over time allowed the identification of three research stages. The initial stage, which spans 1987-2000, in which 14 articles were published, at the rate of fewer than two articles a year (with the exception of 1995, during which five articles were published). The development stage, which spanned 2001-2007, with 33 articles published at the rate of almost five articles a year. The expansive stage, which spanned 2008-2017, with 198 articles published. The rate of publication of the articles during this last stage increased significantly, and thus, this stage accounted for more than 60% of the total publications over the last 10 years.

Fig. 1.

Evolution of scientific research on EE

Source: Authors’ own research.

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Based on this evidence of productivity, for the rest of the study, the first two periods were combined to form the pre-expansion stage. Thus, this study distinguished between two principal periods: pre-expansion, T1, (1987-2007) and expansion, T2, (2008-2017).

Table 1 shows the 10 most productive and influential journals in the field of EE. Specifically, Table 2 shows the journals with five or more articles and the journals with more than 100 cites. In addition, some bibliometric indicators such as the total number of EE papers (TP) and total number of citations (TC) are presented. Further, an indicator has been included for the articles’ citation frequency from more than 150, 100, 50, 10 citations, to a single citation. Finally, Table 1 shows the number of articles in each period, that is, T1 and T2.

According to Table 1, the journals with the highest number of published articles on the topic of EE were the Journal of Small Business Management (22), International Journal of Entrepreneurial Behavior & Research (21), Academy of Management Learning & Education (19), Journal of Business Venturing (16), Entrepreneurship and Regional Development (15), and International Entrepreneurship and Management Journal (14).

The two most productive journals have published special issues on EE: Journal of Small Business Management (Vol. 51, Issue 3, 2013) and International Journal of Entrepreneurial Behavior & Research (Vol. 24, Issue 3, 2017). This result confirms that EE has increased interest among academics in the expansion stage.

It is necessary to note the importance of the Journal Business Venturing, with twelve of the articles published in the pre-expansion period (T1), and a total of sixteen articles and 1787 cites. In addition, Entrepreneurship Theory and Practice had 1193 cites from three published articles, and Academy of Management Learning & Education had six articles and 575 cites in this period. Table 1 also highlights the Journal of Small Business Management, with twenty articles and 771 cites during the expansion period (T2). The International Journal of Entrepreneurial Behaviour & Research has published twenty one articles in T2but has only 82 cites.

Finally, from an overall perspective, the results show that research on EE has been progressively published in more journals. Specifically, in T1, forty seven articles were published in twenty journals, whereas in T2, 278 articles were published in eighty nine journals.

Table 2 shows the articles with more than 200 citations in the WoS (TC) and the number of cites per article by year (TCY). The nine most influential articles were published in the pre-expansion period and have a significant number of citations (between 468 and 195). Among them, it is necessary to highlight the articles by Kuratko (2005) and Peterman & Kennedy (2003) in Entrepreneurship Theory and Practice, and an article by Souitaris et al. (2007) in the Journal of Business Venturing, that were the most cited articles and had the highest annual citation averages in WoS. Therefore, they could be considered as reference articles in the EE literature. The study by Kuratko (2005) is the most relevant, not only by the number of cites but also for its focus. This study analyzed growth and development in curricula and programs devoted to entrepreneurship and new venture creation. Having been published near the expansion period, this article advanced the change in the EE research focus, from EE as part of an economic development strategy (mcmullan & Long, 1987) to the EE academic perspective (Kuratko, 2005).

Many authors have made fundamental contributions to the development of EE. This section presents and ranks some of these authors, based on the information found in WoS. The measurement includes the number of papers published and the number of citations received, which have been used as measures in previous studies relevant to the field. Following Merigo and Yang (2017), this study implemented a combined method using both number of papers published and number of citations received.

The field of EE is characterized by continuous growth and the participation of a large number of authors. The results indicate that 698 authors contributed to the publication of 325 documents on EE research, which is an average of 2.15 authors per article. The fact that only authors have published more than four articles signifies low concentration in the field. Table 3 shows the list of authors with more than 200 citations. Note that the authors are ordered according to their productivity in the field (TP). In the case of a tie, we considered the total number of citations in the field (TC). Table 3 lists the TC and TP of each author for each period, that is, pre-expansion period (T1) and expansion period (T2).

Kartz is one of the most productive authors with six articles and is the second most cited author with 462 cites. Based on the analysis Kartz could be considered as a reference author in the EE literature. Nevertheless, it is necessary to note the number of cites obtained by Kuratko, Al-Laham, Souitaris, Zerbinati, Peterman and Kennedy with only one article published on this topic.

On the other hand, Fayolle and Kartz were among the most productive authors, each contributing six articles, followed by Linan and Pittaway, who each contributed five articles. In regard to time period, Kartz, Pittaway, Cope, Vesper and Gartner are the most productive in T1, and in T2 Kartz, Fayolle, Pittway, Linan and Van Praag are the most productive.

The topic of EE has attracted significant academic interest and the research focus has evolved over the years. Therefore, this study conducted an analysis of EE's thematic evolution and created a strategic diagram of EE research using article keywords to determine the most notable EE research themes. As we have indicated previously, SciMAT was used to develop the Science Mapping Analysis.

SciMAT is based on the bibliometric analysis methodology defined by Cobo, López-Herrera, Herrera-Viedma, & Herrera (2011) to analyze a research field and detect and visualize its conceptual subdomains (i.e., particular topics/themes or general thematic areas) and its thematic evolution.

Cobo et al. (2011) established stages to analyze a research field in a longitudinal framework. First, for each period of T1 and T2, the corresponding research themes were detected by applying a co-word analysis to the raw data. This was followed by clustering keywords to themes using the simple centers algorithm (Cobo et al., 2011), which locates keyword networks that are strongly linked to each other and that correspond to centers of interest. These themes can be understood as conglomerates of textual information or semantic/conceptual groups of different themes addressed by the research field. The similarity between the keywords is assessed using the equivalence index (Callon et al., 1991) and a theme is labeled using the name of the most significant keyword in the associated thematic network. The graphic representation of a theme is a sphere whose size is proportional to the number of associated documents (Cobo et al., 2011). To obtain a spatial representation of the thematic clusters of EE, the original keywords assigned by authors were used. A de-duplicating process was applied to improve data quality by grouping words representing the same concept (consulting with peer of the authors of this article).

After that step, this study analyzed the evolution of the research themes (Cobo et al., 2012). Their evolution over the whole time period is then measured as the overlapping of clusters from two consecutive periods. For this purpose, the inclusion index is used to detect conceptual nexuses between research themes in different periods and to identify the thematic areas in a research field (Cobo et al., 2015). Fig. 2 shows the bibliometric map of the thematic evolution over the two time periods. Each theme is represented in the chart by a circle and the lines between circles represent thematic networks.

Fig. 2.

Thematic evolution in the EE research field (1987–2017)

Source: Authors’ own research.

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As Fig. 2 shows, EE was a unique research theme during 1987-2007 (T1). However, related topics, including entrepreneurship learning, entrepreneurship intention, higher education, and provocation appeared during 2008-2017 (T2). The solid lines characterize the connection between periods when the linked themes shared the same keywords or the label of one theme was a part of another. A dotted line indicates that the themes shared keywords that did not indicate the main research topic of the previous theme (Moral-Muñoz et al., 2014). Entrepreneurship education had solid connections to higher education and entrepreneurship learning. However, entrepreneurship intention only shared keywords with entrepreneurship education. Provocation was a new theme that emerged during the second period (T2).

The next step is visualizing the research themes. In this phase, the themes are visualized by means of two different visualization instruments: strategic diagram and thematic network (Cobo et al., 2015). The diagram shows each theme as characterized by two measures: centrality and density. Centrality measures the degree of interaction between a network and other networks; in other words, the strength of external ties to other themes. Density measures the strength of internal ties among all keywords describing a research theme and it can be understood as a measure of the theme's development (Murgado-Armenteros, Gutiérrez-Salcedo, Torres-Ruiz, & Cobo, 2015)

There are four kinds of themes (Callon et al., 1991; Courtial & Michelet, 1994; Cahlik, 2000) classified by the quadrant in which they appear in the strategic diagram (Cobo et al., 2011). Themes in the upper-right quadrant are known as motor themes, because they exhibit strong centrality and high density. These themes are well developed and important for the structure of a research field. Themes in the upper-left quadrant are very specialized and possess a peripheral character. These themes are considered to have marginal importance to the field since they have well-developed internal ties but unimportant external ties. Themes in the lower-left quadrant may represent emerging or disappearing themes, because they are weakly developed and marginal. These themes possess low density and low centrality. Accordingly, they have not demonstrated significant research interest. Themes in the lower-right quadrant represent transversal, general, and basic topics. These themes are important to the research field but are not well developed.

3.5.1Pre-expansive period (1987-2007) (T1)

According to the thematic evolution presented in Fig. 2, it was a unique research theme in this period, so the strategic diagram only could have this theme represented. Thus, the research during this period pivoted on the EE theme.

Based on the analysis of the thematic network of the EE theme, the keyword network associated with this theme indicates that EE was the most studied theme according to the number of documents (Fig. 3). The topics of this theme involved two principal interrelated facts. The first fact involved the teaching-learning process (i.e., entrepreneurial learning and Higher Education) and its methodology (i.e., experiential learning) (Pittaway & Cope, 2007a; Pittaway & Cope, 2007b). The second fact involved the importance of studying the economic effects of the educational process (i.e., entrepreneurship) and new business innovators (i.e., start-ups) emerging from universities (Parker, 2006; Rasmussen & Sorheim, 2006).

Fig. 3.

Cluster network of EE themes (1987–2007)

Source: Authors’ own research.

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Nevertheless, at the end of the first period, Pittaway and Cope (2007a) conducted a systematic review of EE evidence and detected a need for the reorientation of EE research to include the demand for enterprise skills. They also noted the need to understand how different models of entrepreneurship achieve different objectives.

3.5.2Expansive period (2008-2017) (T2)

The four themes identified in the second period of thematic evolution (Fig. 2) are presented in a strategic diagram in Fig. 4, and each theme in the strategic diagram has an associated keyword network (Fig. 5A–D).

Fig. 4.

EE research 2007-2018

Source: Authors’ own research.

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Fig. 5.

Keyword Networks in EE research (2008-2017)

Source: Authors’ own research.

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The strategic diagram presents four themes (Fig. 4). Analysis of the position of the themes in the strategic diagram and their associated keyword networks reveals that entrepreneurial learning has been the field's motor theme. It is related to EE in its keyword network (Fig. 5A). Although it remains the most studied subject based on the number of relevant documents, it lost its position of network centrality due to new research perspectives related to the teaching-learning process. That is, students rather than teachers became the main agents in the educational process. The rest of the topics that use the same network keywords (i.e., experience, small firms, guess, and growth) consider the same perspective when studying the influence of “learning by doing” in the learning process, including concepts such as experience, social knowledge networks, and the different contexts of entrepreneurship action (Fig. 5A), that are motivations to obtain the necessary student engagement in EE. Representative papers in this thematic network were those by Von Graevenitz, Harhoff, and Weber (2010), Pittaway, Rodriguez-Falcon, Aiyegbayo, and King (2011), Lourenço and Jayawarna (2011), and Middleton and Donnellon (2014).

As Fig. 2 indicates, although some related keywords had previously been addressed entrepreneurial intention is a theme that more fully emerged during this period. It is not sufficiently developed for consideration as a motor theme in the field (Fig. 4), but it is related to other relevant topics (Fig. 5B) in economic development strategy (e.g., entrepreneurship and entrepreneurial skills). Other important pathways of research such as national culture or personal traits are linked to the theory of planned behavior (TBP). The TBP, proposed by Azjen (1985), has been applied to studies on the relationships among beliefs, attitudes or behavioral intentions, and behaviors (Sanchez, 2010; Zhang, Duysters, & Cloodt, 2014). Moreover, it clearly represents an opportunity for further research, based on each of these perspectives (Miranda, Chamorro-Mera, & Rubio, 2017).

Provocation is a theme that emerges during this period without any relationships with other thematic networks (Fig. 2). Its position in the strategic diagram indicates that it does not contain any external links to other subjects in the field (Fig. 4). However, Fig. 5C indicates that the keyword network related to this new theme is consistent with the new trends in educational innovation methodologies, which focus on the development of transversal competencies such as provocation, affect, and imagination. This is not confined to the transmission of knowledge. These new trends have developed new practices of scholarship in entrepreneurship studies (Hjorth, 2011; Steyaert, Hjorth, & Gartner, 2011).

Finally, higher education and its thematic network demonstrated the position of the university in the formation of EE knowledge (i.e., entrepreneurial knowledge) and EE action (i.e., technology transfer) (Fig. 5D). This is because universities are entities related to these interrelated and mutually complementary functions (Bramwell & Wolfe, 2008; Guenther & Wagner, 2008). It is worth highlighting that the article by Kartz, Roberts, Strom, and Freilich (2014) provided a history of the development of cross-campus entrepreneurship education (CEEE) in the United States and the European Union. It identified different approaches to EE in the context of higher education.