Mapping the evolution of entrepreneurship as a field of research (1990–2013): A scientometric analysis
Mapping the evolution of entrepreneurship as a field of research (1990±2013): A scientometric analysis
Yanto Chandra 0 1
0 Department of Public Policy, City University of Hong Kong , Hong Kong SAR , China
1 Editor: Sergi Lozano, Institut Català de Paleoecologia Humana i EvolucioÂ Social (IPHES) , SPAIN
This article applies scientometric techniques to study the evolution of the field of entrepreneurship between 1990 and 2013. Using a combination of topic mapping, author and journal co-citation analyses, and overlay visualization of new and hot topics in the field, this article makes important contribution to the entrepreneurship research by identifying 46 topics in the 24-year history of entrepreneurship research and demonstrates how they appear, disappear, reappear and stabilize over time. It also identifies five topics that are persistent across the 24-year study period±±institutions and institutional entrepreneurship, innovation and technology management, policy and development, entrepreneurial process and opportunity, and new ventures±±which I labeled as The Pentagon of Entrepreneurship. Overall, the analyses revealed patterns of convergence and divergence and the diversity of topics, specialization, and interdisciplinary engagement in entrepreneurship research, thus offering the latest insights on the state of the art of the field.
Entrepreneurship is a highly dynamic and fast growing scholarly field of research with a long
intellectual tradition. Its intellectual roots can be traced back to the work of economists such as
], Smith [
], Knight [
], and Schumpeter [
], who laid the foundations by defining
entrepreneurship and its relationship with innovation, economic growth and uncertainty.
After a rather sluggish growth for decades, entrepreneurship research gained some
momentum with an emphasis on the person-centric approach, which attributes psychological traits
and people's characteristics as predictors of entrepreneurship [
]. As it evolved, the field
experienced a behavioral turn, with growing emphasis on what entrepreneurs really do;
particularly why and how they recognize, evaluate, and exploit opportunities . Some scholars
argued that the field had become fragmented, and criticized that the field became a broad label
under which a `potpourri' of research was housed [
]. Other scholars concluded that the field
was highly permeable, relied heavily on major management journals, and lacked boundaries
and new theories [
Like in the sciences and other fields of social sciences, there is a tradition among
entrepreneurship scholars to pause to take stock of what has been done in the past and reflect on
Funding: This article was supported by City
University of Hong Kong's Faculty StartUp Grant,
Project Number: 7200449.
the future. Mapping and tracking the evolution of entrepreneurship research is central to
our understanding of the institutionalization of entrepreneurship, assess its legitimacy, and
identify alternate histories and future opportunities. The collective success of the science of
entrepreneurship is vital, as it helps entrepreneurs, policy makers and global institutions
understand the drivers, obstacles and rules that affect value creation, economic growth,
resource allocation and policy agenda that shape societal well-being. A number of scholars
have attempted to examine the domain of entrepreneurship field, map its intellectual structure,
and assess its evolution (see [
9, 10, 11, 12, 13, 14
]). Unfortunately, the studies depict conflicting
findings with some scholars concluding a maturing [
] and converging pattern  while
others suggest lack of maturity and diverging patterns in the entrepreneurship research [
]. Although these studies made a significant contribution to what we know about
entrepreneurship as a field, they tend to be based on older bibliographic materials (i.e., up to 2009),
and used a single analytical approach, i.e., primarily co-citation relations analysis. Therefore,
these do not represent well the more recent development in entrepreneurship research.
Moreover, co-citation analysis is only one of the techniques used in scientometrics; it can be
enhanced by newer techniques in scientometrics including topic mapping and overlay
visualization analyses to deepen our understanding of the field.
Scientometrics, or also known as `science mapping' [
16, 17, 18
], is often used in conjunction
with information visualization [
] and text mining [
] to study a large body of
bibliographic materials, as well as measuring various kinds of scientific activities, including
investments in research and personnel. Scientometricians have combined various techniques from
scientometrics, information visualization and text mining to study the evolution of various
fields of sciences, from biology , chemistry/nanotechnology [
], informetrics and
], to cognitive science . For instance, Oldham and colleagues [
scientometrics to visually map synthetic organisms, cells and genomes that inform global
policy debates on the governance of synthetic biology, and that help promote independent and
transparent monitoring of developments in synthetic biology. Leydesdorff and Goldstone [
used scientometrics to map the emergence, branching and merging of the field of cognitive
science as an interdisciplinary field among psychology, linguistics, computer science, philosophy
and the neurosciences, and demonstrated how it differs with the progression of artificial
intelligence. However, these novel techniques and approaches have largely been confined to their
own fields, with little or no interaction with entrepreneurship research. To address the
knowledge gap, this article adopts the best practices from the recent advances in scientometrics to
answer two questions: first, How has the entrepreneurship as a field of research changed over
time?, and second, What are the latest trends in terms of new and highly cited topics in the field?
By applying three analytical tools in scientometrics±±topic mapping, co-citation, and
overlay visualization analyses±±on bibliometric data from Web of Science and focusing on micro
(i.e., word), meso (i.e., article) and macro (i.e., journal) levels of analysis, I identify 46 topics in
the history of entrepreneurship (1990±2013), and demonstrate how they appear, disappear,
reappear and stabilize over time. I also identify five topics that are persistent across the 24-year
study period, that I labeled here as The Pentagon of Entrepreneurship: institutions; innovation
and technology; policy and development; entrepreneurial process and opportunity; and new
ventures. This study complements previous bibliometric studies of entrepreneurship research by
revealing that the literature in the field has converged and diverged as demonstrated by the
stabilization of certain topics and identification of communities of scholars; and the diversity of
topics, specialization and interdisciplinary engagement. To my knowledge, this is the first paper
that offers topic mapping and overlay visualization analyses to map the evolution of
entrepreneurship research in a single study. In the next section, I describe the methodology and
data, and discuss what the findings mean and their implications.
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Methods and materials
Scientometrics is a body of tools and techniques to integrate knowledge in a given field or
body of literature using quantitative analysis and statistics to describe patterns of publication.
It allows researchers to conduct `science mapping' [
] to synthesize research findings,
evaluate the research and publication performance of individuals and institutions, and to reveal the
(intellectual, network, conceptual) structure and dynamics of scientific fields. Recent advances
in scientometrics include information visualization and text mining techniques [
17, 25, 27
that help researchers dig deeper into the bibliographic materials and visualizing them to
enhance analysis. In this article, I used three complementary scientometrics techniques to
examine the evaluation of entrepreneurship as a field of research. This approach follows
scientometricians' call for the use of multi-methods in scientometrics analysis±±or so-called
method triangulation. For instance, Wen and colleagues  applied three scientometrics
techniques in their scientometrics research and argued that the use of triangulation ªproduces
a more comprehensive picture than each method applied individually. The outcomes from the
three different approaches can be associated with each other and systematically interpreted to
provide insights into the complex multidisciplinary structure of a fieldº (p.724). Other scholars
such as Lundberg and colleagues  argued that ªtriangulation of data sources and methods
can strengthen the validity in a study by enabling comparisons of different descriptions and
explanations of the phenomenonº (p. 586). Some scholars applied triangulation by combining
different scientometrics techniques and software, such as Vantage Point versus NetDraw
versus VOSviewer [
] or citation relations versus shared author keywords versus title word-cited
reference co-occurrence ; using different types of data, such as funding information and
co-authorship data [
]; as well as using one analysis as a baseline to show contrast with other
analysis or `overlay mapping' [
] under study.
First, I extracted the latent topics embedded in the bibliographic materials of interest and
their evolution, using topic mapping technique. Topic mapping analysis applies statistical
procedures to turn latent (or hidden, invisible) topics in large bibliographic materials into explicit
visuals that show the clusters of topics and the connections among them. Topic mapping
(or topic community clustering) analysis is an emerging technique used in text mining and
]. Topic modeling relies on the dissimilarities between two probability
distributions: that is, the distribution of a semantic unit over the set of all topics, and the
distribution of all semantic units together over the set of all topics . When the two distributions
are very dissimilar, it means that a semantic unit is likely to represent a domain-specific
concept; but if the distributions are very similar, it means that a semantic unit does not represent a
specific concept. The relationship among terms is counted by the number of times they
cooccur across all articles. Thus, the larger the number of articles in which two terms co-occur,
the stronger is the relationship between the two terms. Based on the relationships of terms,
terms are grouped together into clusters and a map is constructed. This concept is called
visualization of similarities or VOS [
] and is a variant of the community detection algorithms
developed by Clauset and colleagues  and Newman and Girvan's [
] modularity measures
of community structures.
To perform topic mapping, I started by using natural language processing (NLP)
techniques to parse the titles and abstracts of the 3693 articles included in this study (see the Data
Preparation section). This process yielded a list of all the nouns and sequences of nouns and
adjectives that occurred in the articles. Following van Eck and colleagues' [
VOSviewer procedures, only noun phrases that occurred in at least 10 articles were considered
in the analysis. I developed a thesaurus to filter out ªnoiseº information, such as general noun
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words (e.g., ªstudyº, ªimplicationsº, ªintroductionº) and articles (e.g., ªtheº, ªaº, ªanº), modal
words (e.g., ªcanº, ªwillº, ªshouldº), pronouns (e.g., ªIº, ªweº, ªtheyº), and publishing-related
words (e.g., ªElsevierº, ªPalgraveº, ªcopyrightº). I also converted all plural nouns into singular
nouns. From here, I created co-occurrence networks, and selected the most relevant terms or
words (i.e. noun phrases) (see Fig 1 for the research design) and generated the topics from the
based on their similarities.
I subsequently validated the topic mapping results using co-citation analyses at the author
and journal levels and using overlay visualization analyses. Co-citation analysis is a statistical
technique that can transform latent relationships among authors and or journals into explicit
visual outputs in the form of co-citation clusters, to ease data interpretation. Co-citation
analysis is one of the most popular techniques used in the bibliometric study in various business
disciplines, from strategic management [
], business ethics [
] to international business [
The idea behind co-citation analysis is that the articles of scholars who are frequently co-cited
are likely to represent similar or related concepts [
]. I used co-citation analysis [
provide further insights and validate the topic mapping results above. To do this, I created a
co-citation matrix and used Van Eck's [
] clustering technique (see Fig 1) to identify the
clusters of closely related publications as ªtopicsº. Using Van Eck's Java-based VOSviewer [
] techniques, I conducted co-citation relations of articles with a minimum of 20 citations.
The purpose of using the ª20-citations thresholdº was to reduce clutter in the data visualization
and this was found to provide cleaner and less cluttered visuals compared to using lower
citation threshold (e.g., at 5, 10 or 15 citations). The co-citation analysis was conducted at the
author and journal levels. The former calculates co-citation based on the relations of authors'
of articles, while the latter on journal sources' relations. The author- and journal-based
co-citation relations served to offer richer insights into the intellectual structure of entrepreneurship.
Overlay visualization analysis detects the latest topics (ªnew topicsº) and the topics that
appeared in highly cited journals (ªhot topicsº), which enables researchers to portray the
trajectory of a research field. Overlay visualization is one of the most cutting-edge techniques
used in scientometrics and information visualization [
] to display publication trends. It
provides a ªvisual historyº of a field of research. Based on a thorough review of the literature,
this technique has not been used in prior bibliometric study in entrepreneurship. Using Van
Eck's Java-based VOSviewer techniques [
], I plotted a base map based on the
relationships between a type of element (e.g., terms relations that form clusters of topics), and then
overlay each data point with additional numerical information that adds value for
interpretation (e.g., age of publication, citation impact, etc.). In this study, I used two types of overlay
visualization to depict publication trends: time and citation.
Although triangulation of methods is critical to achieve rigor and consistency in a
scientometric study, the three-pronged approach used in this article (i.e., topic mapping, co-citation
and overlay visualization) has not been used in the previous scientometric research on
entrepreneurship. The three scientometrics techniques used in this article was driven by their
complementarity where additional insights and validity are gained by comparing different
]. Specifically, topic mapping provides a synthesis of the themes using words
used in the published articles, co-citation analysis offers insights on the relationships among
authors and journals as a proxy to identify research themes and networks, while overlay
visualization analysis generates the newest and hot topics±thus the combination of the three
analytical techniques provides a more well-rounded view of the findings at the word, co-citation, year
and citation rate levels and allows the verification of findings generated by each technique
(than using a single analytical technique). More details of how each of the analytical techniques
was used will be discussed in more details in the Findings section.
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To perform scientometric analysis of entrepreneurship as a field of research, I used data from
the Clarivate Analytics' Web of Science (WoS) Core Collection database. This database is
commonly used in scientometrics to study the progress and evaluation of various scientific fields
]. The data collection took place in June 2014, and therefore the database included data
up to the end of 2013. Before the actual data collection on WoS, I conducted preliminary
observation of the database. I found that there were few entrepreneurship papers published in
journal articles prior to 1990, with the period of 1990±1995 yielding only 121 articles. I am also
mindful of the fact that topic mapping analysis, one of the key analytical techniques used in
this study, produces better results with larger bibliometric collections. Thus, 1990 was chosen
as the cut-off point. A Web of Science search using ªentrep º keyword (following [
screening for ªarticlesº only for the Business OR Management subject categories within the
1990±2013 period produced 3693 publications for analysis. Although engineering, science and
arts/humanities literature also contains research on entrepreneurship, to date the field of
entrepreneurship remains a core area of research in the business and management domains.
Therefore, the bibliographic samples were focused on the ªbusiness OR managementº
categories to make a contribution to the domains that gave birth to entrepreneurship. Only journal
articles were chosen because journal articles are ªcertified knowledgeº . Next, I parsed the
publications' Abstract & Title into a whole 1990±2013 corpus and four separate sub corpora
for finer analysis: 1990±1995, 1996±2001, 2002±2007, and 2008±2013. Although there is no
strict formula on the range or intervals of bibliometric data used in a scientometric analysis
(some scholars use 10-, 7- or 5-year intervals), this data parsing was reasonable and eased the
detection of changes in the publication trends. The design for the study is summarized in the
As shown in Fig 1, the distribution of entrepreneurship articles published across the four
periods is as follows: 1990±1995 (n = 121 articles), 1996±2001 (n = 262), 2002±2007 (n = 866), and
2008±2013 (n = 2444). As can be seen in the distribution of the bibliographic materials, there
is a sudden explosion in the number of entrepreneurship articles in recent years and the largest
increase in publication took place in the last two periods.
Some of the most highly cited publications in this analysis are (as of the date of data
collection): 1) Shane and Venkataraman's [
] ªThe Promiseº, an Academy of Management Review
paper (Rank #1: 1578 citations); 2) Shane's [
] ªPrior Knowledgeº, an Organization Science
paper (Rank #2: 805 citations); 3) Zott and Amit's [
] ªE-businessº, an Strategic Management
Journal paper (Rank #7: 587 citations).
To provide an overall picture of the evolution of entrepreneurship as a field of research
(1990±2013), I conducted three complementary analyses: topic mapping, co-citation and
overlay visualization analyses.
Topic mapping analysis
I began the analysis by performing topic mapping using Van Eck's Java-based VOSviewer
18, 37, 43, 45
] using the four periods of bibliometric data, and I then sought to
give a label to all topic clusters that emerged in each interval according to the terms and
phrases that were prominent in each period and depict the number of terms (i.e., words) in
each topic cluster and calculated their share out of all terms in each period. Another scholar
with expertise in scientometrics and entrepreneurship played a ªdevil's advocateº role to
reexamine the topic labels to ensure that all labels make logical sense. Each term had appeared in
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at least 10 publications/articles. I offered a summary of the overall topic clusters and their
share (using VOSviewer techniques) over the four periods in Table 1. The breakdown of the
topic clusters in the Table 1 is shown in S1±S6 Figs.
In the first period, 1990±1995, four topic clusters emerged (see Table 1 and S1 Fig). Topic
mapping analysis (S1 Fig) revealed four topic clusters of entrepreneurship research: 1)
personcentric (key terms: ªpersonº, ªabilityº; red circles); 2) performance (key terms: ªperformanceº,
ªenvironmentº; green circles); 3) new venture (key terms: ªnew ventureº, ªmanagementº; blue
circles); and 4) innovation and technology (key terms: ªinnovationº, ªtechnologyº; yellow
circles). During this period, entrepreneurship research was dominated by person-centric topics
(accounting for nearly half of the articles analyzed), followed by performance and new venture
The second period, 1996±2001, was marked by various publications that attempted to
define the field, and by self-reflective papers [
]. The topics in this period had increased to
11 clusters (see Table 1 and S2 Fig): 1) person-centric (red circles); 2) performance (green
circles); 3) new venture (blue circles); 4) innovation and technology (yellow circles); 5) opportunity
and entrepreneurial process (purple circles); 6) failure (light blue circles); 7) strategy and
capability (navy blue circles); 8) experience and knowledge (amber circles); 9) network (orange
circles); 10) culture (pink circles); and 11) small business (brown circles). During this period the
person-centric approach in entrepreneurship continued to occupy the largest share (see
Table 1), but there was a sudden growth in innovation and technology management topics, and
the rise of entrepreneurial process and opportunity topics. Other emerging topics were
entrepreneurial failure, strategy, culture, and network.
The third period, 2002±2007, was characterized by a sudden explosion in the number of
topics and the number of articles published. There were several interesting patterns observed.
Topic mapping analysis revealed 28 clusters (see Table 1 and S3 Fig). The person-centric topic
decreased substantially, while entrepreneurial process and opportunity, SMEs, new venture
creation and performance increased substantially. The topics strategy and innovation and technology
management seemed to stabilize. This period saw the emergence of 19 additional topics that
were not strongly present in the first and second periods (as shown by their topic cluster
number and color code): 12) institutional entrepreneurship (jade blue circles); 13) discourse and
narrative (hot pink circles); 14) community and society (dark purple circles); 15) ethic (light purple
circles); 16) internationalization and international entrepreneurship (grey circles circles); 17)
marketing and market orientation (emerald green circles); 18) entrepreneurial orientation
(baby blue circles); 19) venture capital (lime green circles); 20) entrepreneurial behavior (plum
circles); 21) decision making and risk (copper brown circles); 22) investment and financing
(moss green circles); 23) human capital (bright purple circles); 24) competition (light brown
circles); 25) IPO and firm sale (lavender circles); 26) policy and development (bright green
circles); 27) family business (purple circles); 28) entrepreneurship education (light purple circles);
29) self-employment and women entrepreneurship (grey circles); and 30) ownership and
stakeholders (bright purple circles).
The fourth period, 2008±2013, revealed that some topics disappeared and some became a
part of other topics (based on their proximity on the 2-dimension map), and some new topics
emerged. In total, there were 32 cluster topics in this period, out of a total of 46 that existed
between 1990 and 2013 (see Table 1 and Fig 2 and Fig 3). The person-centric topic has
stabilized and captured a persistent share, albeit with a greater number of articles. There were
sudden increases on these topic clusters: entrepreneurial process and opportunity, innovation and
technology management, new venture creation, strategy, entrepreneurial orientation,
internationalization and international entrepreneurship, human capital, family business, and female
entrepreneurship and gender. But the most notable increase was with the emergence of institutional
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experience and knowledge experience, knowledge
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entrepreneurship (which captured a 10% share of all terms), innovation and technology
management, and policy and development. A number of very new topics that emerged in this period
were: social entrepreneurship, business ethics, corporate social responsibility, leadership, poverty,
norm and tradition, and employment and job creation. Topics that seemed to weaken included
competition, failure, and decision making and risk.
I conducted co-citation analysis at the author and journal levels, as described further below.
Author-based co-citation analysis. I conducted co-citation analysis of authors who were
co-cited across the four periods to offer a more holistic interpretation of the evolution of the
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Fig 2. Topic clusters of entrepreneurship research 2008±2013 (n = 2444), top half.
field. In the analysis, I included only articles that had 20 or higher citations and included the
name of the ªfirst authorº only to avoid overly cluttered maps, following the procedures
suggested by Rodrigues et al. [
] and Waltman and Van Eck [
]. Fig 4 shows the co-citations
for the period of 2008±2013, while S4±S6 Figs show the co-citations for the other three periods.
Larger node and node labels reflect higher citations (and vice versa), while different color and
adjacent nodes depict the clusters of topic themes that emerge.
The results show that two major clusters of author co-citation relations emerged in the
1990±1995 period (see S4 Fig): entrepreneurship-psycholo gy (Gartner, Cooper, Birley,
Brockhaus, Aldrich, and Hannan; green circles) and strategy-general management (Burgelman,
Kanter, Porter, Mintzberg, Drucker, Miller, McMillan; red circles). These co-citation clusters
resembled the four clusters identified in the topic mapping analysis above, although they were
less refined (see S1 Fig and column 7 of Table 1).
Next, author co-citation relations in the 1996±2001 period revealed four co-citation clusters
(see S5 Fig): entrepreneurship-innovati on-psychology (Gartner, Cooper, MacMillan, Timmons
and the group; red circles); strategy-innovation (Covin, Zahra, Dess, Miller, Mintzberg,
Burgelman, Stevenson; green circles); strategy-economics (Porter, Eisenhardt, Williamson, Tushman,
Barney and the group; blue circles); and organization-technology-innova tion (Carroll, Hannan,
Acs, Westhead, Storey, Rothwell, Shane and the group; yellow circles). In this period, new
highly cited scholars emerged: Covin, Zahra, Miller, Williamson, Eisenhardt, Venkataraman,
and Shane. These co-citation clusters resembled the 11 clusters identified in the topic mapping
analysis above, although they were less refined (see S2 Fig and column 6 of Table 1).
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Fig 3. Topic clusters of entrepreneurship research 2008±2013 (n = 2444), bottom half.
The author co-citation relations in the 2002±2007 period revealed six co-citation clusters
(see S6 Fig): entrepreneurship-psychology (Shane, Gartner, Cooper, Johannisson, Busenitz,
Davidsson, Baron, McClelland, Venkataraman, Sarasvathy and the group; red circles);
economics—innovation (Audretsch, Baumol, Evans, Reynolds, Acs, Storey, Kirzner, Teece, Nelson and
the group; blue circles); institutions—network—techno logy—innovation—sociology (Aldrich,
Dimaggio, Greenwood, Burt, Powell, Garud, Hannan, Tushman, Van de Ven and the group;
yellow circles); international—entrepreneurship (McDougall, Oviatt, Knight, Johanson,
Cavusgil; purple circles), strategy—technology—organization (Zahra, Miller, Covin, Lumpkin, Porter,
Eisenhardt, Barney, Burgelman, Von Hippel, Alvarez and the group; green circles); and
venture capital—finance—family business—cognition (Westhead, Shepherd, Wright, Jensen,
Chrisman, Sharma, Ensley and the group; light blue circles). Among the most highly cited scholars
in this period were Shane, Zahra, Aldrich, Gartner, Eisenhardt, Audretsch, McDougall, Oviatt,
Baron, and Dimaggio. Those who emerged in this period but did not feature in the prior two
periods was McDougall. These co-citation clusters resembled the 28 clusters identified in the
topic mapping analysis above, although they were less refined (see S3 Fig and column 5 of
The author co-citation relations in the 2008±2013 period, as shown in Fig 4, revealed very
dense clustering patterns compared to the previous three periods (see S4±S6 Figs). Nine
cocitation clusters emerged in this period: innovation—technol ogy—venture—capital—institution
(Klepper, Cohen, Lerner, Nelson, Zucker, Stuart and the group; purple circles); economics—
innovation—networks (Audretsch, Acs, Parker, Baumol, Fritsch, Arenius, Bosma, Minniti,
North, and the group; yellow circles); entrepreneurship—psyc hology—cognition—sociology—
women (Shane, Gartner, Aldrich, Davidsson, Baron, Bandura, Sarasvathy, Baker, Shepherd,
Busenitz, Kirzner, Brush, Krueger, and the group; blue circles); institution—organization—
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Fig 4. Author co-citation clusters in entrepreneurship research 2008±2013 (by first author; citation 20).
innovation—sociology—network (Dimaggio, Greenwood, Weick, Garud, Powell, Johannisson,
Suddaby, Dorado, and the group; green circles); social entrepreneurship—narr ative—education
(Mair, Steyaert, Hjorth, Austin, Nicholls, Tracey, Dees, Jones, Chell, Peredo, and the group;
also green circles), family business—strategy (Chrisman, Sharma, Sirmon, Schulze, and the
group; teal blue circles); strategy—networks—capabilities—ex ploration (Zahra, Eisenhardt,
Miller, Covin, Lumpkin, Teece, Barney, Kogut, McGrath and the group; red circles); marketing
(Slater, Kohli, Narver, Day, Atuahene-Gima, Zhou, and the group; also red circles); and
international—entrepreneurship (McDougall, Oviatt, Coviello, Johanson, Dunning, Jones, Knight,
Madsen, Cavusgil, and the group; light blue circles).
As shown above, results from co-citation analysis at the author level alone provided rather
crude clusters of topics and offered overlapping topics, and the results can be rather difficult to
interpret and depend on the researcher's subjectivity in classifying and labeling them into
topics. Nevertheless, they did show themes that offered support to the topic mapping results.
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Fig 5. Journal-based co-citation clusters in entrepreneurship research 1990±2013 (by journal sources; citation 20).
Journal-based co-citation analysis. To add more depth to the analysis, I conducted
cocitation analysis based on journal sources of articles that had 20 or more citations (n = 945
articles) for the entire 1990±2013 period, using Van Eck's Java-based VOSviewer techniques [
]. Results are shown in Fig 5. The figure depicts a rather diverse and complex journal
cocitation clusters: entrepreneurship —psychology (Journal of Business Venturing,
Entrepreneurship Theory and Practice, Journal of Applied Psychology, Journal of Personality and Social
Psychology; blue circles); management—organizations (Academy of Management Journal,
Academy of Management Review, Administrative Science Quarterly, Organization Science;
green circles); family business (Family Business Review; brown circles); economics—finance
(Small Business Economics, American Economic Review, Entrepreneurship and Regional
Development, Journal of Finance; red circles); technology—innovation (Research Policy,
Management Science, Technovation, R&D Management; purple circles); strategy—management
(Strategic Management Journal, Journal of Management, Harvard Business Review; yellow
circles); international business—entrepreneursh ip (Journal of International Business Studies,
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International Business Review, Journal of International Marketing; light blue circles); and
marketing—innovation (Journal of Marketing, Journal of Product Innovation Management,
Industrial Marketing Management; yellow circles). The patterns of journal co-citations were
not surprising, and resembled the more refined topic mapping results shown in Table 1.
Overlay visualization analysis of new and hot topics. First, to produce ªnew topicsº
using the time-based overlay visualization, I plotted the entire topic mapping's terms (or
words) and clusters from 1990±2013, and I overlaid the base map with numerical information
to depict new topics (and later, hot topics) in entrepreneurship research. I chose the year 2008
as the average midpoint at 1.0 of the scale (green). To visualize the new topics, the terms that
appeared in the topic clusters were matched with the corresponding year of the article where
the terms appeared. Newer topics were visualized using color ranging from yellow (relatively
new) to red (the newest), while older topics were visualized from green (relatively old) to blue
(the oldest), based on a normalized scale of 0±2. Thus, terms that were used more towards
2013 were shown in orange to red; while terms that were used more towards 1990 were shown
in light to dark blue. This produces a color-based visualization of newer versus older
publications. The result is shown in Fig 6, and the classification of topic clusters refers to the Table 1.
Increasing trends in publications related to the following ªnew topicsº were observed:
institutional entrepreneurship, institutional logic, institutional theory (topic cluster #12), social
entrepreneurship, narrative, discourse (topic cluster #31), poverty (topic cluster #35), business ethics
(topic cluster #32), family business (topic cluster #27), internationalization and international
entrepreneurship (topic cluster #16), and global entrepreneurship monitor and use of panel data
Next, to produce ªhot topicsº using the citation-based overlay visualization, I plotted the
terms (following [
]) with colored circles to reflect the average citation impact for the term.
To visualize the hot topics (i.e., topics that appear in highly cited articles), I matched the terms
that appeared in the topic clusters with the citation score of the article where the terms
appeared. I corrected for the age of publications by dividing each publication's number of
citations by the average number of citations of all publications that appeared in the same year.
This yielded a publication's normalized score. Thus, a score of 1 means that a publication's
number of citation equals the average of all publications that appeared in the same field in the
same year. The normalized citation scores of all publications in which the terms occurred were
then averaged, after which a color scale that ranged from blue (0; the coldest) to green
(midpoint of 1.0; relatively cold) and yellow (1; relatively hot), to red (2; the hottest) was used to
plot the terms. Therefore, terms with a low average citation impact were marked blue, while
terms with a high average citation impact were red. This produces a color-based visualization
of hot (highly cited) versus cold (less cited) publications. The result is shown in Fig 7, and the
classification of topic clusters refers to the Table 1. The ªhot topicsº included: institutional
work, institutional logic, institutional entrepreneurship (topic cluster #12), opportunity discovery
and recognition (topic cluster #5), international new venture, international business study (topic
cluster #16), entrepreneurial orientation, innovativeness (topic cluster # 18), cognition, emotion,
identity (a mix of topic clusters #1 and #34), top management team (topic cluster #46), strategic
alliance (overlap of topic clusters #4 and #11), performance and profitability (topic cluster #2),
and depth case study and conceptualization (unclassified cluster).
Discussion and conclusion
Scientometric analysis provides an interesting and revealing window to understand the
evolution and visual history of scholarly work. Based on the rich insights generated by this study,
many stories can be told: the emergence, decline and shift in topics that are important in the
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Fig 6. Overlay map of ªnew topicsº in entrepreneurship research (1990±2013). The closer two terms are to each other, the stronger their relations. A
normalized scale of 0±2 was used to indicate the newness of publications. Year 2008 was used as the mid-point (score 1). Terms that are used more towards
2013 are shown in orange to red, while terms that are used more towards 1990 are shown in light to dark blue. Each term occurs in at least 10 publications.
field of entrepreneurship, the shift in the groups of scholars co-cited as a group, journals that
are frequently co-cited as a group, as well as overlooked opportunities and possibly politics in
publishing entrepreneurship articles. Given the conflicting findings among previous
bibliometric analysis of entrepreneurship research (i.e., entrepreneurship research is ªmaturingº
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Fig 7. Overlay map of overall ªhot topicsº in entrepreneurship research (1990±2013). The closer two terms are to each other, the stronger their
relations. The size and color of a term indicates, respectively, the number of publications in which the term occurs and the average citation impact of these
publications. A normalized scale of 0±2 was used to indicate the average citation impact of publications. Blue indicates a low citation impact, green a normal
citation impact, and red a high citation impact. Each term occurs in at least 10 publications.
], ªdiverging and lacking maturityº [
], ªconvergingº ) and the lack of analysis
of newer bibliometric data, this study offers a more recent picture of the development of
entrepreneurship research that extends and enriches prior bibliometric studies of the field by
including multiple units of analysis (i.e., micro = word/term, meso = articles/author, and
macro = journal sources) and using a larger, richer and newer dataset (n = 3693; 1990±2013),
and focusing on articles only (i.e. articles = certified knowledge). In addition, this article offers
important methodological contribution to the study of entrepreneurship by introducing three
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new scientometrics techniques (i.e., topic mapping, time-based and citation-based overlay
visualization) as a way of advancing the field and enhancing the validity of the study [28±31].
In fact, this is the first article that offers topic mapping and overlay visualization analyses to
map the evolution of entrepreneurship research in a single study.
Several key insights emerged from this study that have not been reported or found in prior
research, which constitute the contributions in this paper (see Table 2 for a summary of the
findings and the observations).
First, this study revealed pluralistic topics (i.e., 46 topics) that existed in entrepreneurship
research between 1990 and 2013. These included person-centric issues on entrepreneurship,
performance, and new venture creation, to family business and top management team (see
Table 1). Importantly, in the last period of the study (2008±2013) research in entrepreneurship
`exploded' in terms of the number of journal publications as well as the range of topics
published (see Table 1). Some topics were less studied or published than others (e.g., failure,
competition, decision making and risk); some topics declined for a while and then grew again (e.g.,
person-centric approach to entrepreneurship and new venture creation); and some others
emerged and became more mainstream (e.g., institutional aspect of entrepreneurship). Thus
the progression of entrepreneurship as a scientific field is not linear, but highly dynamic; and
marked by pluralistic topics that appeared in business and management journals.
Second, this study discovered five topics that were persistent across the 24 years (1990±
2013) of the entrepreneurship research (see Table 1, Fig 2 and Fig 3). These were: institutions
and institutional entrepreneurship; innovation and technology management; policy and
development; entrepreneurial process and opportunity; and new ventures. These five major topics could
be labeled The Pentagon of Entrepreneurship; they are inter-related and form the building
blocks of entrepreneurship. Thus my findings support Shane's [
] reflection, and Busenitz
and colleagues' [
] findings, that there has been a consensus around ªopportunityº and ªnew
venturesº in entrepreneurship research, yet also reveal the rise of other topics not reported by
these scholars such as institutional work, innovation and technology management, and policy
The findings in this study also support the reflection by Welter and colleagues [
research in entrepreneurship has so far focused on `wealth creation, high growth, and
technology firms' but question some of their findings (i.e., `women/gender' issue as an understudied
domain) where in fact `women and gender issue' has emerged as a frequently studied and
published domain in entrepreneurship (see topic cluster #29 in Table 1). The findings also
suggested that entrepreneurship research continues to draw from and are published in a diverse
number of other disciplines (see Fig 4) and journals (see Fig 5), including psychology,
sociology, economics, strategy, international business, and policy and development studies, thus
confirming some of the observations of GreÂgoire and colleagues that the field has diverging
]. This might be driven by scholars publishing strategies that include bringing their
`non-entrepreneurship' expertise to contribute to entrepreneurship research (e.g., institutional
work in entrepreneurship, the cognition/emotion in entrepreneurship) or employing ªnovelty
seeking strategiesº by importing or hybridizing other fields to inform entrepreneurship
]. This also suggests that entrepreneurship research ªlacks a distinctive characterº
in that there has been little development of ªin houseº concepts, theories, perspectives and
methods that can be exported to other fields. For example, ªeffectuationº [
] and ªbricolageº
] are great examples of ªin houseº concepts that were successfully exported and or adapted
to other fields in the social sciences but more efforts are needed to develop them to strengthen
the identity of entrepreneurship as a field.
It appears that by employing an ªentrepreneurialº lens as a frame, researchers can position
almost any research questions and topics to fit in the entrepreneurship field. The convenience
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1) An explotion in the number of topics occurred in 2002±2007 (i.e., 28
topic clusters); but the largest explosion was in 2008±2013 period (i.e., 46
topic clusters) (see S1C Fig, Fig 2A and 2B)
Pluralistic topics; some topics weakened, or
became a part of other topics, some topics were
New topics overlay
Hot (highly cited) topics
2) 46 topics existed between 1990±2013 (see Table 1, Fig 2A and 2B)
3) Topics significantly weakened in 2002±2007 (i.e., "experience and
knowledge", "culture") and in 2008±2013 (i.e., "failure", "community and
society", "decision making and risk", "competition", "ownership and
stakeholders") (see Table 1)
4) Topics that became a part of other topics (i.e., 9 topics in 2008±2013)
(see Table 1)
5) Topics with most significant increase in 2008±2013: "institutional
entrepreneurship", "innovation and technology management", "policy and
development" (see Table 1, Fig 2A and 2B)
6) Newly emerged topics in 2008±2013: "social entrepreneurship",
"business ethics", "corporate social responsibility", "leadership", "poverty,
norm and tradition", "employment and job creation" (see Table 1)
7) 5 topics that stabilized across four periods: "institutional
entrepreneurship", "innovation and technology management", "policy and
development", "entrepreneurial process and opportunity", "new venture
An explotion in the author co-citation clusters occurred in 2008±2013, with
9 total clusters: "innovation-technology-venture-capital-institution",
"institution-organization-innovationsociology-network", "social entrepreneurship-narrative-education", "family
"marketing", "international-entrepreneurship" (see Fig 3, S1D, S1E and
A diverse and complex journal co-citation clustering in X topics"
"entrepreneurship-psychology", "management-organizations", "family
business", "economics-finance", "technology-innovation",
"strategymanagement", "international business-entrepreneurship",
"marketinginnovation" (see Fig 4)
7 new topics emerged (2008 as the average mid point): "institutional
entrepreneurship, institutional logic, institutional theory", "social
entrepreneurship, narrative, discourse", "poverty", "business ethics",
"family business", "internationalization, international entrepreneurship",
"global entrepreneurship monitor, use of panel data" (see Fig 5)
9 new hot topics: "institutional work, institutional logic, institutional
entrepreneurship", "opportunity discovery and recognition", "international
new venture, international business", "entrepreneurial orientation,
innovativeness", "cognition, emotion, identity", "top management team",
"strategic alliance", "performance, profitability", "depth case study,
conceptualization" (see Fig 6)
Stabilization and persistence of five topics
Diversity of author's co-citation clusters
indicating diverse research themes
Pluralistic journal co-citation clusters indicating
pluralistic research themes
Divergence of new topics
Divergence of hot topics
of extending various disciplines to entrepreneurship coupled with niche-driven research
strategy has occurred and may continue to affect the progression of the field. For example,
neuroscientists and geneticists have extended their work to entrepreneurship, calling it
], while a marriage between operations research and
entrepreneurship has given birth to `operational entrepreneurship' , and the mix of ethical, social and
commercial logic has led to the birth of `social entrepreneurship' [
]. In a nutshell, we are
witnessing of a ªnon-paradigmaticº growth of entrepreneurship research as it continues to
draw on and mix with other fields to explain and predict entrepreneurial phenomena.
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Overall, this study reflects a growing specialization and interdisciplinarity as the field
matures. It offers support for what Cornelius and colleagues [
] call the signs of maturity of a
field by demonstrating that entrepreneurship research has develop a stable range of topics, an
identifiable community of researchers, and increase in specialization in the field. This study
also supports what Gartner and colleagues  call a highly fragmented field as scholars bring
their own disciplines into the entrepreneurship field. Therefore, the evolution of
entrepreneurship as a field of research is not one that is neat or linear but is both convergent and divergent,
with a growing consensus on certain topics and the identification of communities of scholars
as the field matures and a diversification and interdisciplinarity on the topics and
heterogeneity of communities of scholars.
The ªstruggle for citationº, where scholars compete for recognition from their peers,
coupled with the ªinnovation tournamentº through tough journal review processes [
] may have
influenced which articles, theories or groups of scholars get cited. Why certain articles, authors
or theories become highly cited is beyond the focus of this study. But these processes have
given rise to a number of topics that have become highly cited (ªhot topicsº), including
institutional work, opportunity discovery and recognition, entrepreneurial orientation,
cognition/emotion and identity, international new venture, top management team, strategic alliance, and
performance and profitability. This may reflect the areas on which leading and highly
influential scholars have been and/or are focusing, while indicating the types of topics that the
collective community of entrepreneurship scholars and journal editors and reviewers find
Consequently, with the emergence of different communities in the field of
entrepreneurship as a result of the `arrival' of scholars from other disciplines, cross-disciplinary research can
be better promoted as a more fruitful avenue to push research in entrepreneurship forward
and to break new grounds in the field. As this study shows, research on the everydayness of
entrepreneurship (i.e., those classified as non-heroic, non-messianic and non-growth- or
technology- oriented) remains scant in the literature. On this note, studying entrepreneurship in
specific contexts may offer new ways of advancing the entrepreneurship field. As this
scientometric study shows, entrepreneurship research has not given sufficient attention on a number
of specific contexts as a way of advancing the field. One of these is the spirituality and religiosity
aspects in entrepreneurship [
]. While most entrepreneurship research takes a secular
perspective, the role of spiritual and religious values and other normative values such as ideology
] could be new and interesting perspectives and contexts to study entrepreneurial cognition
and action. In the context of developing economies (e.g., Africa) and Asia (e.g., China or
Malaysia or Mexico), for instance, it is public knowledge that entrepreneurial decision making
are often shaped by Confucianism, Islam, Christianity or even folk beliefs as well as local
politics. This research direction may attract scholars from the field of religion and politics and or
collaborations between scholars in entrepreneurship and religion and politics to form a
crossdisciplinary research with entrepreneurship.
The other is sustainable entrepreneurship [64±66]. Sustainability is another relatively new
context to entrepreneurship, and involves economic and non-economic aspects as well as
collective action and political will in facilitating sustainable business practices±thus providing
new theoretical perspectives to advance our understanding of entrepreneurship as
ecologically-friendly behavior. Future research can promote more cross-disciplinary research that
brings in scholars and expertise from environmental, energy and material science and fuse
them with entrepreneurship. The impact of entrepreneurial activities on the Earth's geology
and ecosystems (e.g., use of chemicals in modern agriculture enterprises), entrepreneurial
efforts to promote versus discourage sustainable entrepreneurship (e.g., environmental
activism turned sustainable ventures; fast fashion industry or artificial intelligence-driven
19 / 24
enterprises as a driver of unsustainable world), to the relationship between new geopolitical
orders and sustainable enterprises (e.g., China's One Belt One Road and its influences on
economic and environmental sustainability in various parts of the world).
Next, studies of developing and less/least developed market as a context remain marginalized
in mainstream research on entrepreneurship. Many of the `jamu' or `sari sari' or floating
market entrepreneurs (a common phenomenon in Indonesia, Philippines, and Thailand
]) do not pursue wealth creation but treat entrepreneurship as a means of survival
and pursuing a simple lifestyle. We know little about these phenomena but they could enrich
and extend entrepreneurship theories. In short, I argue that `context is king' in future
entrepreneurship research. Last but not least, the dearth of `replication studies' in entrepreneurship
found in this scientometrics study suggests that more research is needed to test and re-test
prevailing assumptions and generalizations in the field.
Finally, as a caveat, I acknowledge that the present study reflects only one of many
alternative interpretations of the development of the field of entrepreneurship. The research design in
this study may have excluded certain or some publications from certain journals (e.g.,
engineering, law, public policy journals). Thus, future research can examine the bibliographic
materials of such entrepreneurship-specific journals as the Journal of Business Venturing,
Entrepreneurship Theory and Practice, and the Strategic Entrepreneurship Journal from their
first issue until the most recent issues, to see how they collectively ªtell a storyº about the
evolution of the field. A cross journal comparison of them will show how the topic evolution,
cocitation relations, or new and hot topics may differ across each journal. Another alternative
would be to study how entrepreneurship research evolves within a specific area or a `local
analysis', such as business and management, versus the global categories or `global analysis' that
include science, engineering, arts and other social sciences fields. This would offer a richer
understanding of how scholars in different fields conduct entrepreneurship-related research.
Another possible area of research would be to see the knowledge diffusion of theories, concepts
or ideas (e.g., effectuation, bricolage, entrepreneurial orientation) from
entrepreneurship-specific journals to non-entrepreneurship and the broader management and organization
journals. Future studies could also take a more inclusive approach by including non-journal
outlets, such as popular and textbooks, conference proceedings, and practitioner-oriented
journals, to understand how entrepreneurship diffuses throughout various channels and fields.
Last but not least, future studies could adopt alternative techniques, from topic modeling to
computer-aided text analysis and computational linguistics, to test and explore patterns in the
publications in entrepreneurship.
S1 Fig. Topic clusters 1990±1995 (n = 121).
S2 Fig. Topic clusters 1996±2001 (n = 262).
S3 Fig. Topic clusters 2002±2007 (n = 866).
S4 Fig. Author co-citation 1990±1995.
S5 Fig. Author co-citation 1996±2001.
20 / 24
S6 Fig. Author co-citation 2002±2007.
Conceptualization: Yanto Chandra.
Data curation: Yanto Chandra.
Formal analysis: Yanto Chandra.
Funding acquisition: Yanto Chandra.
Investigation: Yanto Chandra.
Methodology: Yanto Chandra.
Project administration: Yanto Chandra.
Resources: Yanto Chandra.
Software: Yanto Chandra.
Supervision: Yanto Chandra.
Validation: Yanto Chandra.
Visualization: Yanto Chandra.
Writing ± original draft: Yanto Chandra.
Writing ± review & editing: Yanto Chandra.
21 / 24
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