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International Entrepreneurship and Management Journal (2024) 20:1451–1486
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Age andentrepreneurship: Mapping thescientific coverage
andfuture research directions
RaihanTaquiSyed1 · DharmendraSingh2 · NisarAhmad3 · IrfanButt4
Accepted: 26 February 2024 / Published online: 1 April 2024
© The Author(s) 2024
Abstract
Researchers’ interest in studying the relationship between age and entrepreneurship
has mushroomed in the last decade. While over a hundred articles are published and
indexed in the Scopus database alone with varying and fragmented results, there has
been a lack of effort in reviewing, integrating, and classifying the literature. This
article offers a framework-based systematic review of 174 articles to comprehend
the relationship and influencing factors related to an individual’s age and entrepre-
neurship. Bibliographic coupling is used to identify the prominent clusters in the
literature on this topic and the most influential articles. Also, the TCCM review
framework is adopted to provide a comprehensive insight into dominant theories
applied, contexts (geographic regions and industries) incorporated, characteristics
(antecedents, consequences, mediating and moderating variables, and their relation-
ships) investigated, and research methods employed in age and entrepreneurship
research over the last fifteen (2007–2022). Though the literature covers an array
of industries, to better understand the age-entrepreneurship correlation, we need to
investigate the new-age technologically driven business sectors further to expand
our knowledge. Furthermore, we detect that the Theory of Planned Behavior mostly
dominates the literature, with other theories trivially employed. Finally, we apply
the TCCM framework to suggest fertile areas for future research.
Keywords Age· Entrepreneur· TCCM· Bibliographic coupling· Systematic
literature review
Introduction
Entrepreneurship research has witnessed increased attention on the relationship
between age and the entrepreneurial behavior of an individual (Kautonen & Minniti,
2014). Also, investigating the differences in entrepreneurial career/self-employment
decisions across various age groups has garnered increasing interest from researchers
(Morrar etal., 2022; Seo etal., 2024). Furthermore, comprehending the motivational
Extended author information available on the last page of the article
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factors influencing self-employment decisions has shown to be particularly useful, as
the factors are fundamental determinants of individuals’ mindset and intent (Cowling,
2000; Kautonen etal., 2015). According to Kautonen etal. (2014), an individual’s
age plays a significant role in converting entrepreneurial intentions into entrepreneur-
ial actions. However, the negative impact of age is also evident in the formation of
entrepreneurial intentions (Shirokova etal., 2016). Age, including gender and person-
ality traits, favors or inhibits engagement in entrepreneurship (Kautonen & Minniti,
2014). Paray & Kumar (2020) stress the importance of predisposition toward will-
ingness to take risks, proactivity, innovation, and self-efficacy as crucial attributes
of entrepreneurial intention. Findings have proven the positive relationship between
risk aversion and age, which could impact the inclination to pursue an entrepreneurial
career path (Bayon & Lamotte, 2020; Hernández etal., 2019). Younger individuals,
who tend to be more enthusiastic, dynamic, and ambitious, may be more inclined to
engage in entrepreneurial pursuits as they have a relatively higher present value of
future income streams (Alvarez-Sousa, 2019). On the other hand, older individuals’
larger network of social contacts and extensive professional experience are instru-
mental in successfully transforming entrepreneurial intentions into entrepreneurial
ventures (Curran & Blackburn, 2001; Kautonen & Minniti, 2014). Older individuals
are better equipped in terms of social and human capital to navigate the early-stage
uncertainties in their ventures successfully (Seo etal., 2024; Karoly & Zissimopou-
los, 2003).
According to Becker’s (1965) theory of time allocation, each person has a criti-
cal threshold age at which their willingness to invest time in initiating new activi-
ties declines due to the increasing opportunity cost of time as they grow older
(Galenson, 2009). This theory also applies to entrepreneurs and their networking
activities, leading to their social capital. Consequently, an individual’s motivation
for starting new ventures decreases over their lifespan (Ashourizadeh & Schøtt,
2013; Kozubíková etal., 2016). As individuals grow older, they may prefer activi-
ties that offer immediate rewards, such as paid work or leisure time, in retirement
(Curran & Blackburn, 2001). However, this preference may also be influenced by
the social context in which older individuals operate, particularly cultural views of
aging (McCrae etal., 1999; Minola etal., 2016). Studies have confirmed an inverted
U-shaped relationship between age and entrepreneurship (Paray & Kumar, 2020).
The chances of an individual becoming an entrepreneur increase with age up to the
late 40s (Kautonen etal., 2014) and decreases thereafter (Minola etal., 2016; Shaw
& Sørensen, 2022; Viljamaa etal., 2022). It is evident that age and entrepreneurship
have acquired prominence as a research concept, especially in the last ten years, with
over a hundred articles indexed in the Scopus database alone with varying, inconclu-
sive, and fragmented results, as exemplified above.
The significance of this topic is evident from the fact that a meta-analysis on
this topic was recently published by Liao etal. (2022). Though systematic literature
reviews and meta-analyses are research methods utilized in synthesizing previous
studies, they have unique advantages and functions. While meta-analyses concen-
trate on quantifying the body of knowledge using a specific statistical analysis and
are limited to studies that report correlation coefficients, systematic reviews offer a
more comprehensive and broader picture of the state of research (Syed etal., 2023).
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However, none of the articles (Table1) attempt to carry out a comprehensive review
and analysis to amalgamate the various aspects and themes related to age and entre-
preneurship. At this junction, it is necessary to integrate and classify the literature to
prevent its further fragmentation and recognize future research avenues. Hence, this
paper probes the following research questions:
RQ1: How has literature on age and entrepreneurship developed?
RQ2: Which theories, contexts, characteristics, and methods have been incorpo-
rated in the literature on age and entrepreneurship?
RQ3: What are the probable future research avenues regarding Age and Entrepre-
neurship?
To find answers to the above RQs, we conducted co-citation analysis of jour-
nals, bibliographic coupling of documents, identified seminal papers in the area, and
most importantly, used the TCCM framework to organize the literature to present an
all-encompassing and integrative depiction of research in this area. By using mul-
tiple analyses, the first study to do so in this domain, we have provided a deeper
and broader understanding of the research topic from different perspectives. A thor-
ough analysis points out gaps in the body of knowledge, opening the door for more
studies. Furthermore, thematic analysis has been performed by the earlier literature
reviews in this area (Ratten, 2019; Minola etal., 2014); however, we use biblio-
metric coupling to identify thematic clusters. Using its unique clustering technique,
bibliometric coupling identifies emerging themes and new research trends by exam-
ining the relationship between documents. The co-citation of journals identified
clusters of journals belonging to different arenas in this field, such as management,
psychology, and small business. This helps researchers clearly see the underlying
disciplines in this field. Scholars frequently congregate around key journals, and fig-
uring out which ones to read can aid in developing cooperative networks. Interacting
with researchers who publish in these journals can help build relationships with sub-
ject matter specialists and could provide avenues for collaborative studies.
This article is designed as follows: The “Methodology” sectionexplains the research
methodology used in this study. The “Bibliometric findings” sectionillustrates bibliometric
findings, including clusters from bibliographic coupling and seminal papers on this topic (RQ
1). The “TCCM framework-based review”sectionelucidates the theories, contexts, charac-
teristics, and methods incorporated in the published studies (RQ 2). The “Last five years”
sectionhighlights the merging themes in the last five years. The “Future research directions”
sectionhighlights the future research directions. The “Conclusion” sectionis the concluding
section, which briefly outlines the current research’s contributions and limitations.
Methodology
Article selection process
The Scopus database is used to identify the relevant published articles for this study.
Scholarly research can also be accessed through other significant databases such
as Google Scholar and Web of Science (WoS). Every database has benefits and
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Table 1 Comparison of previous review studies related to Age and Entrepreneurship
Basis of Comparison Minola etal. (2014) Liao etal. (2022) Ratten (2019) Zhao etal. (2021) Our Study
Focus of Study Differences Between
Young and Old People in
Entrepreneurship
Antecedents,
Mediators, And
Moderators of
Entrepreneurial
Intention
Older Entrepreneurship Age and
Entrepreneurial
Success
Age and Entrepreneurship
Author Keywords Youth Entrepreneurship;
Age and Entrepreneurship;
Entrepreneurship Policies;
Young Entrepreneurs;
Enterprising Individuals
Entrepreneurial
Knowledge, Self-
Efficacy, Social
Norms, Risk-Taking,
Entrepreneurial
Intention
Minority Entrepreneurship,
Older Entrepreneurship
Age, Entrepreneur,
Gender, Diversity
Performance
Career Success
Meta-Analysis
Age, Entrepreneur, TCCM,
Bibliographic Coupling,
Systematic Literature Review
Time Period 1986 to 2013 Till 2020 Till July 2017 Till 2018 Till 2022
Database Scopus Not mentioned EBSCOhost, Emerald,
Proquest, ScienceDirect,
Scopus, Web of Science and
Wiley
EBSCO, Proquest Scopus
Articles Analyzed 21 89 46 102 154
Methodology Systematic Literature Review Meta-Analysis Systematic Literature Review Meta-Analysis Systematic Literature Review
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drawbacks of its own. For example, Google Scholar is the most extensive database
with generous citation counts. However, there is a compromise on the quality since
Google Scholar not only takes the citation counts from published articles but also
from working papers and other less-quality publications, e.g., conference proceedings
and book chapters (Ahmad et al., 2020a; b; Asatullaeva et al., 2021; Harzing &
Alakangas, 2016). On the other hand, the WoS is regarded as a high-quality database
that selects citation counts from the papers that have only been published in the
WoS journals index (Franceschini etal., 2016; Harzing & Alakangas, 2016). As a
result, it is more reliable and of higher quality. Between the two (Scopus & WoS),
the Scopus database is a better option for carrying out review work on business/
management topics (Máté et al., 2024; Hussain et al., 2023. Franceschini et al.,
2016). Furthermore, Scopus is a comprehensive database of research publications
that contain titles, abstracts, keywords, and other extensive publications and citation
information on thousands of peer-reviewed journals (Sreenivasan & Suresh,2023;
Asatullaeva et al., 2021). Moreover, the Scopus database covers more indexed
publications in the field of arts-based management (Santos etal., 2023; Naveed etal.,
2023; Franceschini etal., 2016). Therefore, our study considers the Scopus database
for data extraction and further analysis. Figure1 presents the article selection process
following Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA). The search process begins with appropriate keywords to identify the
articles through title, abstract, and author-provided keywords. Based on an initial
assessment of the research field and research questions, the keywords/strings are
chosen (Xiao & Wu, 2021). Hence, the following words are used in the title, abstract,
and author-provided keywords of the documents in Scopus: Age, entrepreneur*
startup, "start-up," and self-employment. This search has identified 1,198 documents
Records identified from:
SCOPUS Database
Number of Records (n = 1,198)
Search Key usedin TITLE,
ABSTRACT andAUTHORKEY
Words:
(Age AND (entrepreneur* OR startup
OR "start-up" OR "self-employment"))
OR AUTHKEY(Age AND
(entrepreneur* OR startup OR "start-up"
OR "self-employment"))
31st December 2023
Limitedto JournalArticle
(
n=685
)
Excluded
Conference papers,proceedings,
119
Errata, editorials,Note,
Books, Books Chapter54
(
n=197
)
LimitedtoEnglish Language
(
n=640
)
Other Languages excluded:
(n =45)
Study Number of cited
references
Curran & Blackburn (2001)
Walker &Webster (2007)
Kautonen et.al. (2014)
Minola et.al. (2016)
Zhao et. al. (2021)
37
56
34
146
73
Total references 346
Relevant articles identified by
reading the title and abstract.
(n =41)
Irrelevant articles excluded:
(n =174)
Final Sample Characteristics
(Timeframe 1971 –2023)
Final Sample Number of Journals 122Authors per document 2.80 Total Citation 7,265
(n =174)Number of Authors 453Authorsupplied Keywords 562Average citationsper document 41.75
Identification of studies via databases Identification of studiesvia Previous studies reference list(manually)
noitacifitnedI
Screening
Duplicates andnon-Scopus articles
identifiedand removedfromthe
abovesources
(
n=215
)
Articles excluded.
(n =131)
Manual Screening:
Thetitle andabstractwereskimmed
to remove the irrelevant articles
basedonawell-defined inclusion
andexclusion criteria (n =143).
Additional articles identified.
(n =31
irrelevant articles removed.
(n = 497)
Restricted to Subject Area
Economics,Econometrics and
Finance
Business, Management
Social Science
(
n=882
)
Records excluded.
(n = 316)
Fig. 1 Artice selection flow chart (PRISMA)
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in the Scopus database. Several filters have been applied to filter out the irrelevant
documents. First, the subject area has been restricted to economics, econometrics,
finance, business, management, and social science according to Scopus’s subject
classification. This resulted in the removal of 316 irrelevant documents. In the next
step, only journal articles are included in the search. Consequently, 197 conference
papers, proceedings, errata, editorials, books, and book chapters have been excluded
from the sample. The journal articles are further restricted to the English language,
which left with a sample of 640 documents. Next, the articles have been manually
screened by two independent researchers to manually skim the title and abstract to
remove the irrelevant articles. The relevancy criterion focused on the entrepreneur’s
age and excluded all articles involving the firm’s age only. After this process, 174
articles were found relevant. The final set of papers does not include 9 conceptual, 2
Systematic Literature Review (SLR), and 2 Meta-Analysis papers.
To ensure that our sample was not missing any essential articles, we thoroughly
searched the reference list of some important papers on this topic published in dif-
ferent timeframes. Those papers include (Curran & Blackburn, 2001), (Kropp
etal., 2008), (Kautonen etal., 2014), and (Zhao etal., 2022). 346 articles have been
cited in these five papers. Duplicate and non-Scopus cited references have been
removed, leaving us with 215 articles. The title and abstract of these papers were
skimmed through two independent research for relevant articles, resulting in 41 rel-
evant articles. However, 10 of them were already included in our sample. Therefore,
31 missing articles have been added to our final sample, consisting of 174 papers.
These papers have been published in 108 different journals from 1971 to 2022. 393
researchers have authored these papers and provided 497 different author keywords.
These documents have been cited by 6,511 papers, with 42.28 citations per docu-
ment. VOSviewer (Van Eck & Waltman, 2010) is the software used to construct bib-
liometric maps (Singh & Malik, 2022).
Bibliometric findings
Publication timeframe
Our study includes all articles published on age and entrepreneurship since the
1970s. However, Fig.2 illustrates that the publication trend over the last 10 years
has increased substantially, indicating that this research topic has garnered substan-
tial interest among academicians and researchers.
Co‑citation ofjournals
Co-citation analysis is a unique method for studying the cognitive structure of sci-
ence (Ahmad etal., 2020a; Chatha etal., 2018). Co-citation analysis involves track-
ing pairs of papers cited in the source articles (Ahmad et al., 2020a). When the
same pairs of documents are co-cited by many authors, research clusters begin to
form (Ferreira, 2018). After analyzing the data, we identified the top 40 outlets
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with a threshold of 25 citations and categorized them into four different clusters
based on the scope of publication (Fig.3). The first cluster contains 14 outlets rep-
resenting the broad theme of small business and entrepreneurship. The top outlets
in the first cluster based on the link strength are the Journal of Business Ventur-
ing, Journal of Small Business Management, International Small Business Journal,
Entrepreneurship Theory and Practice, and Journal of Small Business and Enter-
prise Development.
16
25
39
94
0
10
20
30
40
50
60
70
80
90
100
Prior 2007 2008 - 2012 2013 - 20172018 - 2023
Number of Publicaons
Tiimeframe
Fig. 2 Number of articles by timeframe
Fig. 3 Co-citation of Journals (Min 25 citations)
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The network graph shows the Journal of Business Venturing in the center, with
the highest link strength among the top 40 journals. The other two journals in
terms of link strength are Entrepreneurship Theory and Practice, and Small Busi-
ness Economics. Our review places the Journal of Business Venturing as a promi-
nent outlet for publishing research on age and entrepreneurship. The four clusters
in the co-citation map of journals indicate that the topic has diversity and signifi-
cance for various publication outlets.
Seminal papers
We used VOS Viewer for co-citation analysis by feeding 174 papers and identifying
9033 cited references. With a threshold of a minimum of 5 citations for cited refer-
ences, we received 19 papers and a further filter of 10 for ‘total link strength’ (refers
to the number of links of a given reference with other references) gave us 13 most
cited papers in the given domain of age and entrepreneurship. The selection of 13
articles was based on the thresholds implemented by authors, as there is no standard
method to reduce the data (Syed etal., 2022).
The co-citation analysis aided us in extracting 13 seminal papers from the given
field (Table 2). The highest number of seminal papers are from the Journal of
Business Venturing (3 papers) followed by Small Business Economics (2 papers).
Authors for all the seminal papers are associated with universities from the USA
(United States of America), the UK (United Kingdom), or other European countries
like Sweden. The seminal papers focused on the issues related to entrepreneurial
preferences, motivation, and intentions for prime-age entrepreneurs and senior entre-
preneurs, and how the interaction of age with other variables has impacted entrepre-
neurial behavior and success. The seminal papers also highlight the importance of
perceived age, personality traits, and other demographic variables on the outcomes
like venture growth, self-employment, and entrepreneurship.
We have expanded on the work of seminal papers, which has been discussed in
further detail (in the following section) with the help of six clusters attained by using
bibliographic coupling in VOSviewer. Bibliographic coupling facilitates studying
the recent developments of a given field as it includes recent publications that are
not covered in co-citation analysis (Rojas-Lamorena etal., 2022).
Bibliographic coupling
(Ferreira, 2018) describes bibliographic coupling as “…the extent to which two
articles are related by virtue of them both referencing the same another article”. In
other words, bibliographic coupling occurs when two articles mention a common
third article in their bibliographies. Figure4 illustrates the six different clusters led
by a node, which is the largest circle. Table3 provides further details of each cluster
and highlights respective Top-10 papers. The following sub-sections further eluci-
date the clusters.
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Table 2 List of key seminal papers
Citation Paper Title Journal
Lévesqueand Minniti (2006) The Effect of Aging on Entrepreneurial Behavior Journal of Business Venturing
Kautonen etal. (2014) Ageing and Entrepreneurial Preferences Small Business Economics
Davidsson and Honig (2003) The Role of Social and Human Capital Among Nascent Entrepreneurs Journal of Business Venturing
Ajzen (1991) The Theory of Planned Behavior Organizational Behavior and Human Decision Processes
Shane etal. (2003) Entrepreneurial Motivation Human Resource Management Review
Lévesqueand Minniti (2011) Age Matters: How Demographics Influence Aggregate Entrepreneurship Strategic Entrepreneurship Journal
Verheul etal. (2012) Explaining Preferences and Actual Involvement in Self-Employment:
Gender and The Entrepreneurial Personality
Journal of Economic Psychology
Kautonen etal. (2011) Entrepreneurial Intentions in The Third Age: The Impact of Perceived Age
Norms
Small Business Economics
Van Auken etal. (2006) The Influence of Role Models on Entrepreneurial Intentions Journal of Developmental Entrepreneurship
Shane and Venkataraman (2000) The Promise of Entrepreneurship as a Field of Research Academy of Management Review
Kolvereid and Isaksen (2006) New Business Start-Up and Subsequent Entry into Self-Employment Journal of Business Venturing
Baum and Locke (2004) The Relationship of Entrepreneurial Traits, Skill, And Motivation to
Subsequent Venture Growth
Journal of Applied Psychology
Bird (1988) Implementing Entrepreneurial Ideas: The Case for Intention Academy of Management Review
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Cluster 1: Entrepreneur’s demographic characteristics andfirm performance
This cluster primarily focuses on elucidating the impact of demographic char-
acteristics, including age and gender, on entrepreneurial motivation and firm
performance. Within this cluster, the top cited articles investigate the extent to
which gender plays a role. While the study by Barbieri & Mshenga (2008) con-
cluded that most of the owners of firms with greater annual gross sales than the
rest are male or white, Shaw etal. (2009) demonstrated that gender has a scant
influence on entrepreneurial capital, which in turn impact the firm. Their study
concluded that variables such as age and experience have much greater influ-
ence. Along similar lines, the most cited paper in this cluster by Shirokova etal.,
(2016) ascribe the translation of entrepreneurial intentions into entrepreneurial
action to other characteristics too, such as family entrepreneurial background,
age, and uncertainty avoidance. However, Laure Humbert and Drew (2010) dem-
onstrate that there is a strong gender effect on some motivational factors but posit
that gender itself needs to be examined along with other social factors in order to
understand differences in motivations. The findings of their work indicate that
marital status, being a parent, and/or age, are helpful in explaining differences in
pathways into entrepreneurship for men and women.
Fig. 4 Illustration of clusters through bibliographic coupling
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Table 3 List of top-10 papers in each of the six clusters obtained through bibliographic coupling
Cluster 1
(Entrepreneur’s Demographic
Characteristics and Firm
Performance)
Total Citations Cluster 2
(Age and Entrepreneurial Orientation)
Total Citations Cluster 3
(Age and Entrepreneurial Competencies)
Total Citations
Shirokova etal. (2016) 217 Kellermanns etal. (2008) 285 Forbes (2005) 263
Runyan etal. (2008) 150 Kautonen etal. (2008) 253 Gielnik etal. (2012) 228
Gray (2002) 147 Bamundo and Kopelman (1980) 185 Kropp etal. (2008) 251
Shaw etal. (2009) 74 Baù etal. (2017) 163 Obschonka etal. (2011) 230
Yetim (2008) 55 Thorgren etal. (2016) 150 Wyrwich (2013) 219
Louw etal. (2003) 52 Zhang and Acs (2018) 140 Diánez-González etal. (2016) 186
Bisk (2002) 49 Thorgren etal. (2014) 137 Paray and Kumar (2020) 126
Laure Humbert and Drew (2010) 47 Lin and Wang (2019) 74 Mas-Verdú etal. (2009) 243
Lafuente and Vaillant (2013) 41 Ashourizadeh and Schøtt (2013) 65 Ferreras-Garcia etal. (2021) 110
Staniewski (2015) 26 Marín etal. (2019) 64 Alvarez-Sousa (2019) 143
Cluster 4
(Senior entrepreneurship and life
satisfaction)
Total Citations Cluster 5
(Age and Risk-taking Propensity)
Total Citations Cluster 6
(Youth vs Aging Entrepreneurship)
Total Citations
Zissimopoulos and Karoly (2007) 160 Josef and Back (2018) 132 Athayde (2009) 218
Kautonen and Minniti (2014) 131 Minola etal. (2016) 86 Wright etal. (1998) 191
Kautonen etal. (2017) 108 Gielnik etal. (2018) 75 Kautonen etal. (2011) 169
Bönte etal. (2009) 84 Rolison etal. (2012) 68 Chaudhary (2017) 61
Walsh and O’Shea (2008) 56 Kautonen etal. (2015) 56 Ayalew and Zeleke (2018) 38
Cowling (2000) 45 Mayr etal. (2021) 52 Tornikoski and Kautonen(2009) 20
Raymo etal. (2010) 28 Brieger etal. (2021) 27 Revell-Love andRevell-Love (2016) 13
Sahut etal. (2015) 16 Zacher etal. (2012) 20 Chatterjee etal. (2022) 11
Heim (2015) 13 Spicka (2020) 9 Hendieh etal. (2019) 9
Backman and Karlsson(2018) 11 Javed etal. (2018) 9 Zenebe etal. (2018) 9
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Cluster 2: Age andentrepreneurial orientation
This cluster in the literature focuses on the relationship between age and entrepre-
neurial orientation, which has been investigated from various angles but still has
been inconclusive (Zhang & Acs, 2018). The cluster introduces different variables
which influence the age-entrepreneurial orientation relationship, such as family
firms (Kellermanns et al., 2008), hybrid entrepreneurship (Thorgren et al., 2016),
entrepreneurial failure (Baù & Dowling,2007; Lin & Wang, 2019), education level
(Marín etal., 2019), and gender (Baù & Dowling, 2007). Lin & Wang (2019) sub-
stantiate that the older the serial entrepreneur, the longer the time takes to start a
venture again. Also, the larger the failure loss, the slower the re-venture speed.
Cluster 3: Age andentrepreneurial competencies
This cluster focuses on the linkage between age and entrepreneurial competency
building. Numerous studies have covered this topic along with other dependent
and independent variables. Ferreras-Garcia etal., (2021) theorize that the experi-
ence variable contributes positively to different competency groups, while the age
variable does not affect the development of entrepreneurial competencies. On the
other hand, Obschonka etal., (2011) posit, through analysis of their findings, that
early entrepreneurial competence is related to the availability of entrepreneurial
role models and authoritative parenting during their adolescence. Furthermore,
Alvarez-Sousa (2019) argues that ‘necessity entrepreneurship’ results from various
other independent variables, including entrepreneurial competency building through
entrepreneurship education.
Cluster 4: Senior entrepreneurship andlife satisfaction
This cluster focuses on the traits and attributes responsible for motivating senior indi-
viduals towards entrepreneurship and self-employment, who believe in their expe-
rience, and resources like financial, human, and social capital (Gielnik etal., 2018;
Raymo etal., 2010; Sahut etal., 2015; Saribut etal.,2017; Soebagio & Burhanudin,
2020). Senior entrepreneurs are very vigilant about their health conditions while
making this decision making. The perceived age of an individual matters more than
his chronological age, feeling younger than his actual age has a positive impact on
an older individual’s engagement in entrepreneurial activities (Kautonen & Minniti,
2014; Louw etal., 2003). In further development, Kautonen etal., (2017) investigated
the shift from organizational employment to self-employment for older individuals in
terms of income and life satisfaction. They established that for individuals switching
to entrepreneurship resulted in a reduction of average income but a significant increase
in the quality of life. Thus, self-employment at the late career stage helps build sus-
tainable societies with more economic activities. Literature has adequate evidence of
the significant negative association between age and entrepreneurial intent (Kautonen
etal., 2011; Sahut etal., 2015).
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Cluster 5: Age andrisk‑taking propensity
The fifth cluster comprises of ten papers mainly representing the risk propensity of
entrepreneurs across life spans. This cluster also highlights how the age-based self-
image of entrepreneurs helps transform intention into behavior for senior entrepre-
neurs, and fear of failure works as an obstacle in transforming intentions into entre-
preneurial activities. Gielnik etal., (2018) and Minola etal., (2016) are the top two
cited papers from this cluster. While the work of the latter established an inverted
U-shape curvilinear association between the variations in an individual’s entrepre-
neurial desirability and feasibility with their age, recording the peak at the age of 22,
Gielnik etal., (2018) did not find any inverted U-shape association between age and
entrepreneurial activity. Biological age as a predictor of entrepreneurial behavior can
be complemented by age-based self-image for a person having a positive perception
of a particular entrepreneurial activity relating to his age (Kautonen etal., 2014).
Cluster 6: Youth vs aging entrepreneurship
This cluster consists of articles that consider the two ends of the age spectrum and
investigate various aspects associated with the divergent age groups. Athayde (2009)
contextually focused on the USA and concluded that young Black pupils were more
positive about self-employment and displayed greater enterprise potential than either
White or Asian pupils. Furthermore, it was also highlighted that a family background
of self-employment positively influenced pupils’ intentions to become self-employed.
Ayalew and Zeleke (2018) underscored the impact of entrepreneurial education/train-
ing and entrepreneurial attitudes on students’ self-employment intention within the
African context. On the other hand, Tornikoski and Kautonen (2009) accentuate that
the entrepreneurial intentions of older individuals are mostly influenced by their per-
ception of how easy or difficult they think starting up a business would be. Similarly,
Kautonen et al., (2011) validate the above findings and posit that entrepreneurial
intention among older individuals is partially mediated by whether the individual
has a positive attitude toward entrepreneurship, by how the individuals perceive their
ability to start and run a business, and by the extent of support from their family and
friends. Overall, articles in this cluster provide a comprehensive view of the anteced-
ents, mediating, and moderating variables (Chaudhary, 2017; Hendiehet al., 2019;
Zenebe etal., 2018).
TCCM framework‑based review
This section illustrates the TCCM framework (Bhattacharjee etal., 2022; Sharma
etal., 2020), which is adapted to review articles on age and entrepreneurship. The
structure drafted by Donthu etal., (2021) is adapted to display the overview (Fig.5).
Further details about theories, characteristics, contexts, and methods are detailed in
the following sub-sections.
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Theories
Theoretical support is always needed to frame the hypothesis and back the study’s
findings (Rao etal., 2021). Findings from our review indicate 38 theories that have
been used in age-entrepreneurship research. Table4 shows the list of key theories used
and their sample citations. The most dominating theories in the literature are the the-
ory of planned behavior (with 10 articles), Entrepreneurship Theory (Backman etal.,
2021), Life span developmental theory (Obschonka et al., 2011), Cognitive Theory
(Abdullah Alnemer, 2021), with each having two articles. In the theory of planned
behavior (TPB), three constructs have been held responsible for shaping the intent to
engage in a particular behavior: (a) attitude towards the behavior, (b) subjective norms,
and (c) perceived behavioral control (Seo etal., 2024).
In the literature, TPB has been prominently used to model the relationship between
an entrepreneur’s age and entrepreneurial intention, as the entrepreneurial intention is
deemed to be the best predictor of the decision-making related to initiating any entre-
preneurial activity (Cowling, 2000). Sahut etal., (2015) studied the direct and indirect
effect of chronological age on entrepreneurial intentions. They concluded that prime-
age entrepreneurs and third-age entrepreneurs’ social norms have less influence on
entrepreneurial intention. Entrepreneurial intention depends on perceived behavioral
control, then on attitude, and less on social norms (Kautonen etal., 2015). An indi-
vidual’s age is crucial in transforming initial intention into actual engagement in a
start-up (Commer etal., 2018). Individuals’ age-related self-image is vital in trans-
forming entrepreneurial intention into action (Minola etal., 2016; Moa-Liberty etal.,
2016). Aging is a psychological term rather than biological, and age-based self-image
positively moderates the relationship between the intention to start a business and
actual entrepreneurial behavior (Kautonen etal., 2015). Age norm positively impacts
third-age individuals’ enterprising inclinations (Kautonen etal., 2011).
According to self-efficacy theory (Bandura, 1977), individuals’ belief in their
capabilities influences their motivation, behavior, and performance. Chen et al.
(1998) discovered a positive association between entrepreneurial self-efficacy and
the inclination to pursue entrepreneurship. (Moa-Liberty et al., 2016) established
a positive correlation between self-efficacy and entrepreneurial intentions. Kropp
etal., (2008) established a strong relationship between entrepreneurial orientation
and self-efficacy. Individuals with high entrepreneurial self-efficacy are more likely
to initiate their own ventures. They strongly believe in their abilities to identify and
seize opportunities, manage challenges, and create successful businesses (Gielnik
etal., 2012).
Contexts
Bhatia etal., (2021) describe the context as a political or economic environment
and circumstance under which the study is performed. This review considers coun-
tries as the context to categorize the studies under review. Findings (Table5 and 6)
indicate that most of the studies (69 articles, 45%) under review were carried out in
the European context, followed by Asia and North America. In terms of sectors as
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contexts, education received the maximum attention from researchers (23 articles),
closely followed by research focused on multiple sectors (18 articles). All other sec-
tors show an output of articles in the single digit only. The greater interest garnered
by the service sector is primarily because of education / higher education, which
could be due to the various initiatives implemented across schools and colleges/
CONTEXT
Region Country Sector Industry
Europe USA Finland Services Education Retail
Asia UK India Manufacturing Construction Trade
North America Spain China Agriculture Transport Tourism
Africa Germany South Africa
Latin America Sweden
CHARACTERISTICS THEORY
Antecedent Moderator Mediator Consequence Theory of planned behavior
Demographic Demographic Demographic Entrepreneurship / Self-employment Goal-setting theory
Personality Personality Personality Entrepreneurial Success / Well-being Theory of time allocation
Skills & Training Industry Motivation Entrepreneurial Orientation / Behavior Conservation of resources theory
Motivation Country Upper echelons theory
Family/Household Entrepreneurship theory
Country Generational theory
Lifespan developmental theory
METHODOLOGY
Research Design Respondents Primary Data Sampling Method Data Analysis
Survey Entrepreneurs Random Regression
Experiment Founders Stratified Correlation
Managers Convenience T- test
Employees Purposive Chi-Square Test
Students ANOVA
Other individuals Structural Equation Modelling
Data Sources Secondary Sources Primary Data Collection Primary Data Collection
Primary GEM Database 0-100 300-399 Self-administered
Secondary European country 100-199 500-999 Online/Email
US Government 200-299 1000-1999 Face-to-face
Mail
Fig. 5 Comprehensive overview of the literature on age and entrepreneurship using the TCCM frame-
work
Table 4 List of theories Theories No of Articles
Theory of Planned Behavior 10
Theory of Time Allocation 2
Cognitive Theory 2
Galenson’s Theory of Creativity 2
Generational Theory 2
Upper Echelons Theory 2
Entrepreneurship Theory 2
Lifespan Developmental Theory 2
Attribution Theory 1
Capital Theory 1
Self-Efficacy Theory 1
Social Learning Theory 1
Goal-Setting Theory 1
Self-Determination Theory 1
Demographic Theory 1
Leadership Theory 1
Life Course Theory 1
Personality Trait Theory 1
Resource Based Theory 1
Social Network Theory 1
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universities world-over, focusing on student start-ups (Hendiehet al., 2019; Louw
etal., 2003), youth entrepreneurship (Gulzar & Fayaz, 2021; Minola etal., 2014),
and veteran entrepreneurship (Eltamimi & Sweis, 2021; Kautonen etal., 2008).
Characteristics
This section focuses on the antecedents (independent variables) and consequents
(dependent variables), along with mediator and moderator variables used in the
studies under review. This comprises of recognizing the age-related antecedents
influencing individuals’ decisions to pursue/not to pursue an entrepreneurial
career path and probing the direct or indirect consequences. Also, the mediator
and moderator variables are probed further to understand the relationship between
age and entrepreneurship better. In the following sub-sections, each of the charac-
teristics is elucidated.
Antecedent andconsequent variables
The review and analysis resulted in a diverse set of 72 antecedents from the arti-
cles considered and are broadly classified into six categories: demographic ante-
cedents, personality antecedents, skills and training antecedents, motivation ante-
cedents, family/household antecedents, and country antecedents. Furthermore,
the review and analysis of consequent variables resulted in three key categories of
Table 5 Data collection –
regions Region Total
Europe 69
Asia 28
North America 26
Africa 12
World 10
Multiple 6
Latin America 2
Oceania 1
Total 154
Table 6 Data collection—type
of industry Row Labels Total
Service 46
Manufacturing 5
Agriculture 2
Multiple 18
Not given 83
Total 154
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consequences, namely, entrepreneurship/self-employment, entrepreneurial success/
well-being, and entrepreneurship orientation/behavior. These three categories are
mapped with the corresponding antecedents, as shown in Figs.6, 7, 8, 9.
Our analysis shows that 64 articles investigated the relationship between six ante-
cedent groups and the most dominant consequent group—entrepreneurial career/
self-employment. The demographic-related antecedents in these studies included
age, gender, education, income, ethnicity/nationality, and marital status (Mahadea
Fig. 6 Overview of the first consequents variable group and mapping of the corresponding antecedents
Fig. 7 Overview of the first consequents variable group and mapping of the corresponding antecedents
(continued)
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& Ramroop, 2015; Palalic etal., 2020; Shaw & Sørensen, 2022). While 50 studies
out of these considered age and other demographic-related antecedents together, 14
studies, such as Kautonen etal., (2011, 2015) and Paray & Kumar, 2020), consid-
ered only age as an antecedent in their research. The next dominant antecedent group
is personality-related (18 articles), which consisted of antecedents such as proac-
tiveness, risk-taking, innovativeness, attitude, passion, confidence, fear of failure,
etc., (Babcock, 2021; Chang etal., 2022; Micozzi & Lucarelli, 2016; Sahut etal.,
2015; Wyrwich, 2013). The third antecedent group is skills and training-related (12
Fig. 8 Overview of the second consequents group and mapping of the corresponding antecedents
Fig. 9 Overview of the third consequents group and mapping of the corresponding antecedents
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articles), which consisted of expertise, entrepreneurial education/training, creativ-
ity, etc. (Ayalew & Zeleke, 2018; McCrae etal., 1999; Teixeira & Silva, 2012). The
fourth antecedent group is family/household-related (11 articles), which includes
Family support, Family expectation, Education of parents, Household economic
situation, Self-employed parents, etc. (Alvarez-Sousa, 2019; Hendiehet al., 2019;
Kljucnikov etal., 2018). The fifth antecedent group is country-related (6 articles),
which includes political environment, economic condition, government policies/
support, unemployment rate, per capita income, etc. (Alvarez-Sousa, 2019; Debbage
& Bowen, 2018; Ferreras-Garcia etal., 2021; Wyrwich, 2013). The sixth anteced-
ent group is motivation-related which included motivation-related antecedents (5
articles), which included self-motivation, life satisfaction, the possibility of higher
earnings, etc. (Seo etal., 2024; Staniewski & Awruk, 2015; Teixeira & Silva, 2012).
Our analysis shows that 35 articles investigated the relationship between 4 ante-
cedent groups and the second dominant consequent group—Entrepreneurial Success /
Well-Being. All the articles explored demographic-related antecedents, which included
Age, Gender, Education, Income, Ethnicity/Nationality, and Marital Status (Mahadea
& Ramroop, 2015; Soomro etal., 2019; Palalic etal., 2020; Shaw & Sørensen, 2022).
While 26 studies out of these investigated age and other demographic-related ante-
cedents together, 9 studies such as Brieger etal. (2021), Cox etal. (2017), and Prasad
etal. (2015) considered only age as an antecedent in their research. The next dominant
antecedent group is personality-related (7 articles), which consisted of antecedents
such as Attitude, Risk Propensity, and Entrepreneurial characteristics/traits (Fracasso
& Jiang, 2022; Preisendörfer etal., 2012; K. Shaw & Sørensen, 2022). This is fol-
lowed by the family / household-related antecedent group (5 articles), which included
retired households, entrepreneurial households, generations in a firm, and authorita-
tive parenting (Kellermanns etal., 2008; Obschonka etal., 2011; S. Sharma & Sahni,
2020). 3 articles investigated skills and training-related antecedents and motivation-
related antecedents. Only 2 articles focused on motivation-related antecedents (Pre-
isendörfer etal., 2012; Viljamaa etal., 2022).
Our analysis shows that 18 articles investigated the relationship between 4 ante-
cedent groups and the last dominant consequent group—Entrepreneurial Orienta-
tion/Behavior Success. All the articles explored demographic-related antecedents,
which included Age, Gender, Education, Income, Ethnicity/Nationality, and Mari-
tal Status (Gumusburun Ayalp, 2022; Orihuela-Gallardo etal., 2018; Palalic etal.,
2020). While 15 studies out of these investigated ages and other demographic-related
antecedents together, 3 studies—Rolison etal., (2012); Vroom & Pahl (1971); and
Walsh & O’Shea (2008) considered only age as an antecedent in their research. 3
articles investigated skills and personality-related antecedents (Chatterjee et al.,
2022; Chaudhary, 2017; Gumusburun Ayalp, 2022). Only 2 articles each researched
motivation-related antecedents (Chaudhary, 2017; Kautonen etal., 2014) and fam-
ily/household-related antecedents (Chaudhary, 2017; Sharma & Sahni, 2020).
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Mediating variables
The analysis reveals that only 12 of the total studies (174 articles) have explored the effects
of mediating variables (Table7). The mediating variables are broadly classified into three
categories: personality-related, motivation-related, and skills & training-related.
Studies have significantly explained the mediating mechanisms by focusing on
entrepreneurs’ personality traits such as intuition, creativity, personal control, atti-
tude, risk willingness, and self-efficacy (Athayde, 2009; Cheraghi etal., 2019; Cox
etal., 2017; Kautonen etal., 2011; Newman etal., 2018). The second group of medi-
ating variables explores entrepreneurs’ motivation, such as achievement (Athayde,
2009), travel (Saribut etal., 2017), future perspective (Gielnik etal., 2018), and
start-up intention (Commer etal., 2018). The last group of studies focuses on skills
and training-related characteristics of entrepreneurs, such as experience (Gielnik
etal., 2018; Soebagio & Burhanudin, 2020).
Moderating variables
Analysis of moderating variables shows that only 30 of the total 174 articles (all
quantitative) assessed the moderating effects (Table8). These moderating variables
fall under four broad categories: demographic, personality, family/household, and
country, with the first two categories being prominent. Demographic characteris-
tics that were explored are age (Bamundo & Kopelman, 1980; Hubner etal., 2021;
Kimosop etal., 2016; Newman etal., 2018; Wolfe & Patel, 2022), occupation level
(Bamundo & Kopelman, 1980), education (Bamundo & Kopelman, 1980; Chatterjee
etal., 2022; Kimosop etal., 2016; Pawitan etal., 2018; Zenebe et al., 2018), and
gender (Baù etal., 2017; Chatterjee et al., 2022; Hubner et al., 2021; Shirokova
Table 7 List of mediating variables investigated by authors
Mediator Reference
Personality: Intuition, Creativity, Control
Motivation: Achievement Athayde (2009)
Personality: Entrepreneurial Attitude, Perceived Behavioral Control Kautonen etal. (2011)
Motivation: Focus on Opportunities Gielnik etal. (2012)
Personality: Conflicts Diánez-González etal.
(2016)
Motivation: Travel Saribut etal. (2017)
Skills & Training: Experience
Motivation: Future perspective Gielnik etal. (2018)
Personality: Self Efficacy, Resilience Newman etal. (2018)
Motivation: Start-up intention Javed etal. (2018)
Personality: Social Salience Sproul etal. (2019)
Skills & Training: Experience Vidayana and
Adiningrum(2020)
Skills & Training: Professional competencies Ferreras-Garcia etal. (2021)
Personality: Risk-willingness, Self-efficacy Wickstrøm etal. (2022)
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etal., 2016; Wolfe & Patel, 2022; Zenebe etal., 2018). The personality character-
istics that were explored include agreeableness (Obschonka etal., 2011), mental
health (Gielnik etal., 2012), uncertainty avoidance (Minola etal., 2016), risk-taking
(Wolfe & Patel, 2016), fear of failure (Commer etal., 2018; Lin & Wang, 2019),
and innovativeness (Frešer etal., 2020). Family/household characteristics that were
studied include family background (Shirokova et al., 2016) and family support
(Lin & Wang, 2019). According to these two studies, the background and support
strengthen the relationship between age and entrepreneurship by further motivating
individuals to embark on an entrepreneurial journey, irrespective of the outcome.
The last category, country-related characteristics that were explored included the
regional level of entrepreneurship (Kautonen etal., 2011), culture (Ashourizadeh &
Table 8 List of moderating variables investigated by authors
Moderators Reference
Demographic: Age, Education Bamundo and Kopelman (1980)
Country: Regional level of entrepreneurship Kautonen etal. (2011)
Personality: Agreeableness, Openness, Neuroticism, Extraversion,
Conscientiousness Obschonka etal. (2011)
Personality: Mental Health Gielnik etal. (2012)
Country: Culture Ashourizadeh and Schøtt (2013)
Demographic: Age, Experience, Education Kimosop etal. (2016)
Personality: Uncertainty avoidance Minola etal. (2016)
Demographic: Age, Gender
Personality: Uncertainty Avoidance
Family/Household: Family Background
Shirokova etal. (2016)
Demographic: Age, Gender
Personality: Risk taking Wolfe and Patel (2016)
Demographic: Gender Baù etal. (2017)
Demographic: Elderly Group Saribut etal. (2017)
Demographic: Age Gielnik etal. (2018)
Demographic: Age Newman etal. (2018)
Demographic: Age, Education Pawitan etal. (2018)
Demographic: Age-Based Self-Image
Personality: Fear of Failure Javed etal. (2018)
Demographic: Age, Gender, Education Zenebe etal. (2018)
Personality: Failure Loss
Family/Household: Family Support Lin and Wang (2019)
Country: Economic Development Marín etal. (2019)
Country: Labor Market Situations Bayon and Lamotte (2020)
Country: Happiness Index Eijdenberg and Thompson (2020)
Personality: Entrepreneurs’ Level of Innovativeness Frešer etal. (2020)
Demographic: Age, Gender Hubner etal. (2021)
Demographic: Age Bayraktar and Jiménez (2022)
Demographic: Age, Gender, Education Chatterjee etal. (2022)
Demographic: Life expectancy Wickstrøm etal. (2022)
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Schøtt, 2013), Economic Development (Marín etal., 2019), Labor Market Situations
(Bayon & Lamotte, 2020), and Happiness Index (Eijdenberg & Thompson, 2020).
Methods
Methods include how data was collected and analyzed for empirical investigations
(Donthu etal., 2021). This study involves an examination of 174 articles and charac-
terizes them accordingly. Furthermore, almost all studies (98%) adopted the survey
technique, with only 3 studies adopting experimental techniques (Table9). Primary
data sources were used by nearly two-thirds of the studies (98 articles), 50 articles
used secondary data sources, and 6 articles used both (Table10).
Table11 exhibits the different data sampling methods used in the articles under
review. It is observed that both primary and secondary methods were adopted in
data collection, with the former predominantly used (across 98 studies). The second-
ary sources of data included the Global Entrepreneurship Monitor (GEM) database,
European country data, USA Government data, EU Surveys, World Bank, etc., with
the first two sources incorporated in almost half of the studies (Table12).
An interesting insight regarding data analysis is that all the articles utilized quan-
titative methods only. Table5 illustrates different data analysis methods incorpo-
rated. Among all the quantitative data analysis methods, regression and correlation
top the list, with 70% of the studies incorporating these two methods (Table13).
Other methods, such as the T-test, Chi-Square Test, ANOVA, and structural Equa-
tion Modeling, were used across the remaining 30% of the papers under review.
Last five years
This section will supplement the clusters based on bibliographic coupling by
expanding on the research focus of the 74 latest publications in the last five years,
2018–2022. Out of these 74 papers, a few papers (Brieger etal., 2021; Commer
Table 9 Research design Research Design Total (No) Total (%)
Survey 151 98%
Experiment 3 2%
Total 154 100%
Table 10 Data sources Data Source Total (No) Total (%)
Primary 98 64%
Secondary 50 32%
Both 6 4%
Total 154 100%
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etal., 2018) are also mentioned in the bibliographic coupling-based clusters due to
their high citations, but in most cases, the recently published papers are unable to
get attention in the SLR papers. Therefore, this section can be treated as an extended
discussion of the recent themes as compared to the six clusters discussed in the
“Bibliographic coupling” section. The classification of the recently published papers
in this area discloses 23 research themes in Fig.10. All the 23 themes mentioned in
the section are about the age of the entrepreneurs as a prime antecedent or age as a
part of demographic variables. Significant themes like entrepreneurship, entrepre-
neurial intentions, and entrepreneurs’ attributes impacting firm performance have
been the consistent choice of the researchers in the past as well in the last five years.
Entrepreneurs always bear a certain degree of risk to start a new venture, but
the risk is higher in the case of innovative ventures. Innovative entrepreneurship is
crucial for the country’s growth, but older individuals with rich experience and an
inclination towards entrepreneurship are reluctant to choose risky, innovative entre-
preneurship (Bayon & Lamotte, 2020). Another critical area that needs attention is
the factors affecting re-venturing, where the age of the entrepreneurs, experience,
failure loss, and family support are the crucial factors affecting re-venture speed
(Lin & Wang, 2019). Digital entrepreneurship has been trending in the last 5 years,
especially during and after Covid-19. Digital platforms are helpful in innovative
advertising, social media presence, and maintaining links between suppliers and
buyers (Biclesanu etal., 2021). Demographic factors such as age, gender, and edu-
cation strongly affect the adoption of digital platforms (Chatterjee etal., 2022).
Table 11 Sampling method 1
(primary data) Data Source Total (No) Total (%)
Global Entrepreneurship Monitor
(GEM) database
14 25%
European country data 13 23%
USA Government data 6 11%
EU Surveys 5 9%
World Bank 3 5%
Others 15 27%
Total 56 100%
Table 12 Secondary data
sources Sampling Method Total (No) Total (%)
Random 28 25%
Stratified 9 8%
Convenience 7 6%
Purposive 5 5%
Not Given 62 56%
Total 111 100%
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Table 13 Statistical methods Statistical Total (no) Total (%)
Regression 108 38%
Correlation 90 32%
T-Te st 20 7%
Chi-Square Test 19 7%
Others 16 6%
AN OVA 14 5%
Structural Equation Modeling 9 3%
Descriptive 8 3%
Total 284 100%
Among the new themes discussed in the literature are the entrepreneur’s val-
ues, international orientation, and entrepreneurial intentions of immigrants.
Foncubierta-Rodríguez (2022) highlighted how happiness at work is related to
personal values and the governance style of the entrepreneur. Further, the failure
or insolvency of the SMEs (small and medium enterprises) also depends on the
success of the business and, up to some extent, on the financial expertise of the
entrepreneur, which may depend upon the age, background, and experience of
the entrepreneur (Kljucnikov etal., 2018). Much has been discussed about entre-
preneurial intentions and orientation, but recent studies (Falcão et al., 2022;
Frešer etal., 2020) have added value to the literature by linking the age and
entrepreneurial intentions of immigrants with their international orientation.
Future research directions
The future research agenda has been drafted based on the extensive review of the
literature covered in the previous sections and is classified in accordance with the
TCCM framework.
Theory
The Theory of Planned Behavior mostly dominates the literature on age and
entrepreneurship research, with other theories trivially employed. Future research
can apply the research framework based on integrating existing theories. Also,
researchers could apply new theories in their research as these theories can con-
tribute to a deeper understanding of the challenges and opportunities faced by
entrepreneurs at different life stages:
• Applying age-innovation fit: Applying Kirton’s adaptive-innovation theory
(1976) and examining the influence of an entrepreneur’s age and contextual
elements, such as technological advancements, to explain variations in indi-
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viduals’ creative abilities in shaping the compatibility of entrepreneurs in inno-
vative ventures.
• Applying entrepreneurial ecosystem theory: By implementing systems theory
(Von Bertalanffy,1972) to examine the influence of age-inclusive ecosystems on
entrepreneurial achievements, expansion, and economic development.
• Applying age-based network dynamics: Researchers can utilize the study frame-
work grounded in social network theory proposed by Scott (1992) to examine the
interplay between age-related characteristics and social support for entrepreneurs
at various phases of life, particularly in the context of social entrepreneurship.
• Applying age-diversity and entrepreneurial performance: By applying the self-
categorization theory (Turner,1989), which examines several facets of an indi-
vidual’s identity, researchers can analyze how age diversity affects team perfor-
mance, conflict resolution, and organizational collaborative learning.
2
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2
1
1
1
2
1
4
1
1
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2
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0123456789
Creativityand innovation
Digital Entrepreneurship
Entrepreneurial Competencies
Entrepreneurial Intent of Immigrants
Entrepreneurial Intentions
Entrepreneurial Orientation
Entrepreneurial Success
Entrepreneurial Traits and Family Stress
Entrepreneur's Attributes and Firm Performance
Entrepreneurship
Entrepreneurship and Well Being
Failed Entrepreneurs' Reentry
Fear of Failure
International Orientation
LeadershipStyles
Organizational Attractiveness
Peronal Values
Risk-takingand Creativity
Risk-taking propensity
Self Employemnt
Self-employmentincome
SocialValue
Solvency problemsofSMEs
2018 2019 2020 2021 2022
Fig. 10 Key themes emerging from the literature published in the last five years
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Characteristics
Exploring new antecedents and consequent variables in age and entrepreneurship
research can enrich our understanding of the factors that shape entrepreneurial
behavior and outcomes across different age groups.
• Investigating factors such as digital literacy, technology acceptance, and the will-
ingness to adopt emerging technologies at different age stages can shed light on
the role of technological adaptation as an antecedent to entrepreneurship.
• Studying exit strategies as consequent variables in age-entrepreneurship is rel-
evant. This includes exploring the factors that influence entrepreneurs’ decisions
to exit their ventures, the timing of their exits, and the impact of entrepreneur-
ship on post-exit experiences.
• Knowledge Transfer as a consequent variable: Measuring the impact of age-
entrepreneurship on passing knowledge, experience, and entrepreneurial mindset
to future generations.
• Understanding the factors contributing to entrepreneurs’ resilience and adapt-
ability across different life stages.
• Examining the social consequences of age-entrepreneurship: how entrepreneurs
of different age groups contribute to job creation, economic development, com-
munity engagement, and addressing societal challenges.
• Retirement Transitions: With more individuals pursuing entrepreneurship after
retirement, studying the antecedents related to retirement transitions.
Context
In the current literature on age and entrepreneurship, the dominant industries are educa-
tion (Fracasso & Jiang, 2022; Paray & Kumar, 2020), manufacturing (Newman etal.,
2018), construction (Debbage & Bowen, 2018), food (Abdoli etal., 2012), and bev-
erages (Munawar, 2019). The studies are primarily executed in the USA and Europe,
focusing on primary entrepreneurial traits and orientation.
• Future researchers could select research on new pedagogical tools in entrepre-
neurial education like gamification and simulation techniques; using an online
game can increase students’ motivation, engagement, and learning. The effec-
tiveness of these tools in terms of student engagement, giving real-life scenarios,
could be tested with the help of experimental research.
• Another potential research area could be an entrepreneurial orientation for green
products and green technologies.
• Research is suggested to assess the age-entrepreneurship dynamics in the context
of emerging tech or ICT start-ups, especially booming fintech start-ups.
• Digital technology supported various functions of SMEs during COVID-19; a
comparative longitudinal study between SMEs owned by old and young entre-
preneurs would surely add value to the literature.
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• Many industries have a feeble representation in the literature; like in India, agri-
culture and pharmaceutical are the two dominant sectors, but they have a meager
presence in the extant literature. There is a wide scope of research on industries
like entertainment, finance, gaming, and marketing.
• More research is required from Asian countries and other developing and under-
developed countries.
• Culture is an important and complex construct, and future research may benefit from
cross-culture comparisons moderated by age and political and economic environments.
• A cross-country study with a set of developed and developing countries or demo-
cratic and monarchy governments would be useful in receiving insights on the
impact of the entrepreneurial ecosystem and corruption on the age and entrepre-
neurship relationship.
Methods
The existing literature has been dominated by empirical studies based on primary
data collected by survey instruments, and a lack of qualitative research studies has
been noted. The scope for future research regarding methods is summarized below:
• Qualitative research designs like phenomenological research on two sets of
entrepreneurs (old and young) during crises like COVID-19, re-entry after fail-
ure, and risk related to specific ventures.
• The literature lacks systematic literature reviews based on theories (like action
theory) related to the age-entrepreneurship relationship. There is also a wide
scope of a review based on meta-analysis.
• There is a need to critically analyze the implementation of current theories on
entrepreneurship in diverse cultures and unexplored geographical regions.
• Researchers can consider mixed method approaches as they are mighty, and the
literature has limited usage of this method. Researchers may apply comparative
sentiment analysis on ecosystems and modes of financing in cross-country studies.
• Researchers may apply text and image analytics on digital and crowdfunding
platforms to measure the impact of entrepreneurs’ age and experience.
• Studies based on experimental and longitudinal research are also limited in
this domain.
• Adopting more advanced data analysis techniques in different settings is also
encouraged, as that will add value to the existing literature.
Conclusion
The volume of published research on entrepreneurship and age has increased sig-
nificantly in recent years. Using a systematic review, this study facilitates research-
ers and practitioners in navigating this vast amount of information by providing a
comprehensive and synthesized overview of existing knowledge on this topic. To
the best of our knowledge, no prior study has reviewed the literature on age and
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1 3
entrepreneurship in recent years. The latest review on this topic, by Ratten (2019),
focused on a narrow topic of older entrepreneurship and covered literature until
2017, a total of 46 studies. Our study, with a total sample of 174, includes 94 stud-
ies published since 2018. Ours is the first study in this domain to organize literature
using the TCCM framework. TCCM is one of the most widely used frameworks
due to its ability to present a comprehensive view of research in a versatile manner
(Paul etal., 2023). With the knowledge about the most frequently used independent,
dependent, mediating, moderating, and control variables in age and entrepreneur-
ship and how they have evolved over time, the researchers can see the bidirectional
relationship between variables and choose appropriate constructs to design future
research to move the field forward. We have categorized antecedents into six groups,
which can aid researchers in developing clear and testable hypotheses. When navi-
gating the entrepreneurial landscape, individuals and organizations can benefit from
actionable insights provided by well-defined independent and dependent variables.
Furthermore, identifying widely utilized theories in this field advances the body of
knowledge in age and entrepreneurship. Researchers can produce a more logical
and cohesive body of work by building upon and improving upon existing theories.
Understanding the dominant theories might be helpful for practitioners and entre-
preneurs as this information can help make strategic decisions and provide insights
into how age affects the processes and results of entrepreneurship. Comprehending
the methodologies employed in investigating entrepreneurship and age is impera-
tive for advancing the discipline. The information about respondents, primary and
secondary data sources, data collection methods, and analyses can help researchers
design more rigorous and relevant studies.
In conclusion, this work provides a distinct and straightforward overview of age
and entrepreneurship research using bibliographic coupling and TCCM framework-
based review. However, it does have a couple of limitations, like any study. First,
only the Scopus database was considered to retrieve published papers on this topic.
Further research could be undertaken by examining published papers in databases
such as ProQuest, EBSCO, Web of Science, Open Access Journals, etc. Second,
only journal articles published in English were considered. Publications from mul-
tiple languages could be integrated into future studies. Third, we limited our sam-
ple to fields such as economics, econometrics, finance, business, management, and
social sciences. Future researchers could consider including other areas as well.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis-
sion directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps
and institutional affiliations.
* Raihan Taqui Syed
rtsyed@uaeu.ac.ae
1 Entrepreneurship Faculty, College ofBusiness andEconomics, Human Capital Research Center,
United Arab Emirates University, AlAin,AbuDhabi, UAE
2 Department ofBusiness & Economics, Modern College ofBusiness & Science, Muscat, Oman
3 College ofEconomics andPolitical Science, Sultan Qaboos University, Muscat, Oman
4 Ted Rogers School ofManagement, Toronto Metropolitan University, Toronto, ON, Canada
Authors and Affiliations
RaihanTaquiSyed1 · DharmendraSingh2 · NisarAhmad3 · IrfanButt4
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