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 et al., 2022; Seo et al., 2024). Furthermore, comprehending the motivational 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 et al., 2015). According to Kautonen et al. (2014), an individual's age plays a significant role in converting entrepreneurial intentions into entrepreneurial actions. However, the negative impact of age is also evident in the formation of entrepreneurial intentions (Shirokova et al., 2016). Age, including gender and personality traits, favors or inhibits engagement in entrepreneurship (Kautonen & Minniti, 2014). Paray & Kumar (2020) stress the importance of predisposition toward willingness 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 et al., 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 instrumental 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 et al., 2024; Karoly & Zissimopoulos, 2003).

According to Becker's (1965) theory of time allocation, each person has a critical threshold age at which their willingness to invest time in initiating new activities 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 et al., 2016). As individuals grow older, they may prefer activities 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 et al., 1999; Minola et al., 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 et al., 2014) and decreases thereafter (Minola et al., 2016; Shaw & Sørensen, 2022; Viljamaa et al., 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, inconclusive, 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 et al. (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 concentrate 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 et al., 2023). However, none of the articles (Table 1) attempt to carry out a comprehensive review and analysis to amalgamate the various aspects and themes related to age and entrepreneurship. 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 incorporated in the literature on age and entrepreneurship?

  • RQ3: What are the probable future research avenues regarding Age and Entrepreneurship?

Table 1 Comparison of previous review studies related to Age and Entrepreneurship
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To find answers to the above RQs, we conducted co-citation analysis of journals, 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 multiple 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 thorough 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 et al., 2014); however, we use bibliometric coupling to identify thematic clusters. Using its unique clustering technique, bibliometric coupling identifies emerging themes and new research trends by examining 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 figuring out which ones to read can aid in developing cooperative networks. Interacting with researchers who publish in these journals can help build relationships with subject matter specialists and could provide avenues for collaborative studies.

This article is designed as follows: The “Methodology” section explains the research methodology used in this study. The “Bibliometric findings” section illustrates bibliometric findings, including clusters from bibliographic coupling and seminal papers on this topic (RQ 1). The “TCCM framework-based review” section elucidates the theories, contexts, characteristics, and methods incorporated in the published studies (RQ 2). The “Last five years” section highlights the merging themes in the last five years. The “Future research directions” section highlights the future research directions. The “Conclusion” section is 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 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 et al., 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 et al., 2023; Naveed et al., 2023; Franceschini et al., 2016). Therefore, our study considers the Scopus database for data extraction and further analysis. Figure 1 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 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.

Fig. 1
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Artice selection flow chart (PRISMA)

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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 different timeframes. Those papers include (Curran & Blackburn, 2001), (Kropp et al., 2008), (Kautonen et al., 2014), and (Zhao et al., 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 relevant 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 document. VOSviewer (Van Eck & Waltman, 2010) is the software used to construct bibliometric 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 substantial interest among academicians and researchers.

Fig. 2
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Number of articles by timeframe

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Co-citation of journals

Co-citation analysis is a unique method for studying the cognitive structure of science (Ahmad et al., 2020a; Chatha et al., 2018). Co-citation analysis involves tracking 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 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 representing 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 Venturing, Journal of Small Business Management, International Small Business Journal, Entrepreneurship Theory and Practice, and Journal of Small Business and Enterprise Development.

Fig. 3
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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 Business Economics. Our review places the Journal of Business Venturing as a prominent 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 significance 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 references, 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 et al., 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 entrepreneurs, and how the interaction of age with other variables has impacted entrepreneurial 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.

Table 2 List of key seminal papers
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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 et al., 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. Figure 4 illustrates the six different clusters led by a node, which is the largest circle. Table 3 provides further details of each cluster and highlights respective Top-10 papers. The following sub-sections further elucidate the clusters.

Fig. 4
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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
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Cluster 1: Entrepreneur’s demographic characteristics and firm performance

This cluster primarily focuses on elucidating the impact of demographic characteristics, 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) concluded that most of the owners of firms with greater annual gross sales than the rest are male or white, Shaw et al. (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 influence. Along similar lines, the most cited paper in this cluster by Shirokova et al., (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) demonstrate 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.

Cluster 2: Age and entrepreneurial orientation

This cluster in the literature focuses on the relationship between age and entrepreneurial 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 et al., 2019), and gender (Baù & Dowling, 2007). Lin & Wang (2019) substantiate 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 and entrepreneurial 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 et al., (2021) theorize that the experience variable contributes positively to different competency groups, while the age variable does not affect the development of entrepreneurial competencies. On the other hand, Obschonka et al., (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 and life satisfaction

This cluster focuses on the traits and attributes responsible for motivating senior individuals towards entrepreneurship and self-employment, who believe in their experience, and resources like financial, human, and social capital (Gielnik et al., 2018; Raymo et al., 2010; Sahut et al., 2015; Saribut et al., 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 et al., 2003). In further development, Kautonen et al., (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 sustainable societies with more economic activities. Literature has adequate evidence of the significant negative association between age and entrepreneurial intent (Kautonen et al., 2011; Sahut et al., 2015).

Cluster 5: Age and risk-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 entrepreneurs, and fear of failure works as an obstacle in transforming intentions into entrepreneurial activities. Gielnik et al., (2018) and Minola et al., (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 entrepreneurial desirability and feasibility with their age, recording the peak at the age of 22, Gielnik et al., (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 et al., 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/training 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 perception 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 antecedents, mediating, and moderating variables (Chaudhary, 2017; Hendieh et al., 2019; Zenebe et al., 2018).

TCCM framework-based review

This section illustrates the TCCM framework (Bhattacharjee et al., 2022; Sharma et al., 2020), which is adapted to review articles on age and entrepreneurship. The structure drafted by Donthu et al., (2021) is adapted to display the overview (Fig. 5). Further details about theories, characteristics, contexts, and methods are detailed in the following sub-sections.

Fig. 5
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Comprehensive overview of the literature on age and entrepreneurship using the TCCM framework

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Theories

Theoretical support is always needed to frame the hypothesis and back the study's findings (Rao et al., 2021). Findings from our review indicate 38 theories that have been used in age-entrepreneurship research. Table 4 shows the list of key theories used and their sample citations. The most dominating theories in the literature are the theory of planned behavior (with 10 articles), Entrepreneurship Theory (Backman et al., 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 et al., 2024).

Table 4 List of theories
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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 entrepreneurial activity (Cowling, 2000). Sahut et al., (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 et al., 2015). An individual's age is crucial in transforming initial intention into actual engagement in a start-up (Commer et al., 2018). Individuals’ age-related self-image is vital in transforming entrepreneurial intention into action (Minola et al., 2016; Moa-Liberty et al., 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 et al., 2015). Age norm positively impacts third-age individuals’ enterprising inclinations (Kautonen et al., 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 et al., (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 et al., 2012).

Contexts

Bhatia et al., (2021) describe the context as a political or economic environment and circumstance under which the study is performed. This review considers countries as the context to categorize the studies under review. Findings (Table 5 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 contexts, education received the maximum attention from researchers (23 articles), closely followed by research focused on multiple sectors (18 articles). All other sectors 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/universities world-over, focusing on student start-ups (Hendieh et al., 2019; Louw et al., 2003), youth entrepreneurship (Gulzar & Fayaz, 2021; Minola et al., 2014), and veteran entrepreneurship (Eltamimi & Sweis, 2021; Kautonen et al., 2008).

Table 5 Data collection – regions
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Table 6 Data collection—type of industry
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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 characteristics is elucidated.

Antecedent and consequent variables

The review and analysis resulted in a diverse set of 72 antecedents from the articles considered and are broadly classified into six categories: demographic antecedents, personality antecedents, skills and training antecedents, motivation antecedents, family/household antecedents, and country antecedents. Furthermore, the review and analysis of consequent variables resulted in three key categories of 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.

Fig. 6
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Overview of the first consequents variable group and mapping of the corresponding antecedents

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Fig. 7
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Overview of the first consequents variable group and mapping of the corresponding antecedents (continued)

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Fig. 8
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Overview of the second consequents group and mapping of the corresponding antecedents

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Fig. 9
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Overview of the third consequents group and mapping of the corresponding antecedents

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Our analysis shows that 64 articles investigated the relationship between six antecedent 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 & Ramroop, 2015; Palalic et al., 2020; Shaw & Sørensen, 2022). While 50 studies out of these considered age and other demographic-related antecedents together, 14 studies, such as Kautonen et al., (2011, 2015) and Paray & Kumar, 2020), considered only age as an antecedent in their research. The next dominant antecedent group is personality-related (18 articles), which consisted of antecedents such as proactiveness, risk-taking, innovativeness, attitude, passion, confidence, fear of failure, etc., (Babcock, 2021; Chang et al., 2022; Micozzi & Lucarelli, 2016; Sahut et al., 2015; Wyrwich, 2013). The third antecedent group is skills and training-related (12 articles), which consisted of expertise, entrepreneurial education/training, creativity, etc. (Ayalew & Zeleke, 2018; McCrae et al., 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; Hendieh et al., 2019; Kljucnikov et al., 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 et al., 2021; Wyrwich, 2013). The sixth antecedent group is motivation-related which included motivation-related antecedents (5 articles), which included self-motivation, life satisfaction, the possibility of higher earnings, etc. (Seo et al., 2024; Staniewski & Awruk, 2015; Teixeira & Silva, 2012).

Our analysis shows that 35 articles investigated the relationship between 4 antecedent 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 et al., 2019; Palalic et al., 2020; Shaw & Sørensen, 2022). While 26 studies out of these investigated age and other demographic-related antecedents together, 9 studies such as Brieger et al. (2021), Cox et al. (2017), and Prasad et al. (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 et al., 2012; K. Shaw & Sørensen, 2022). This is followed by the family / household-related antecedent group (5 articles), which included retired households, entrepreneurial households, generations in a firm, and authoritative parenting (Kellermanns et al., 2008; Obschonka et al., 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 (Preisendörfer et al., 2012; Viljamaa et al., 2022).

Our analysis shows that 18 articles investigated the relationship between 4 antecedent groups and the last dominant consequent group—Entrepreneurial Orientation/Behavior Success. All the articles explored demographic-related antecedents, which included Age, Gender, Education, Income, Ethnicity/Nationality, and Marital Status (Gumusburun Ayalp, 2022; Orihuela-Gallardo et al., 2018; Palalic et al., 2020). While 15 studies out of these investigated ages and other demographic-related antecedents together, 3 studies—Rolison et al., (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 et al., 2014) and family/household-related antecedents (Chaudhary, 2017; Sharma & Sahni, 2020).

Mediating variables

The analysis reveals that only 12 of the total studies (174 articles) have explored the effects of mediating variables (Table 7). The mediating variables are broadly classified into three categories: personality-related, motivation-related, and skills & training-related.

Table 7 List of mediating variables investigated by authors
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Studies have significantly explained the mediating mechanisms by focusing on entrepreneurs' personality traits such as intuition, creativity, personal control, attitude, risk willingness, and self-efficacy (Athayde, 2009; Cheraghi et al., 2019; Cox et al., 2017; Kautonen et al., 2011; Newman et al., 2018). The second group of mediating variables explores entrepreneurs’ motivation, such as achievement (Athayde, 2009), travel (Saribut et al., 2017), future perspective (Gielnik et al., 2018), and start-up intention (Commer et al., 2018). The last group of studies focuses on skills and training-related characteristics of entrepreneurs, such as experience (Gielnik et al., 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 (Table 8). These moderating variables fall under four broad categories: demographic, personality, family/household, and country, with the first two categories being prominent. Demographic characteristics that were explored are age (Bamundo & Kopelman, 1980; Hubner et al., 2021; Kimosop et al., 2016; Newman et al., 2018; Wolfe & Patel, 2022), occupation level (Bamundo & Kopelman, 1980), education (Bamundo & Kopelman, 1980; Chatterjee et al., 2022; Kimosop et al., 2016; Pawitan et al., 2018; Zenebe et al., 2018), and gender (Baù et al., 2017; Chatterjee et al., 2022; Hubner et al., 2021; Shirokova et al., 2016; Wolfe & Patel, 2022; Zenebe et al., 2018). The personality characteristics that were explored include agreeableness (Obschonka et al., 2011), mental health (Gielnik et al., 2012), uncertainty avoidance (Minola et al., 2016), risk-taking (Wolfe & Patel, 2016), fear of failure (Commer et al., 2018; Lin & Wang, 2019), and innovativeness (Frešer et al., 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 et al., 2011), culture (Ashourizadeh & Schøtt, 2013), Economic Development (Marín et al., 2019), Labor Market Situations (Bayon & Lamotte, 2020), and Happiness Index (Eijdenberg & Thompson, 2020).

Table 8 List of moderating variables investigated by authors
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Methods

Methods include how data was collected and analyzed for empirical investigations (Donthu et al., 2021). This study involves an examination of 174 articles and characterizes them accordingly. Furthermore, almost all studies (98%) adopted the survey technique, with only 3 studies adopting experimental techniques (Table 9). 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 (Table 10).

Table 9 Research design
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Table 10 Data sources
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Table 11 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 secondary 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 (Table 12).

Table 11 Sampling method 1 (primary data)
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Table 12 Secondary data sources
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An interesting insight regarding data analysis is that all the articles utilized quantitative methods only. Table 5 illustrates different data analysis methods incorporated. Among all the quantitative data analysis methods, regression and correlation top the list, with 70% of the studies incorporating these two methods (Table 13). Other methods, such as the T-test, Chi-Square Test, ANOVA, and structural Equation Modeling, were used across the remaining 30% of the papers under review.

Table 13 Statistical methods
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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 et al., 2021; Commer et al., 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, entrepreneurial 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.

Fig. 10
figure 10

Key themes emerging from the literature published in the last five years

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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 entrepreneurship (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 et al., 2021). Demographic factors such as age, gender, and education strongly affect the adoption of digital platforms (Chatterjee et al., 2022).

Among the new themes discussed in the literature are the entrepreneur's values, 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 et al., 2018). Much has been discussed about entrepreneurial intentions and orientation, but recent studies (Falcão et al., 2022; Frešer et al., 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 contribute 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 individuals' creative abilities in shaping the compatibility of entrepreneurs in innovative 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 framework 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 individual's identity, researchers can analyze how age diversity affects team performance, conflict resolution, and organizational collaborative learning.

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 willingness 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 relevant. This includes exploring the factors that influence entrepreneurs' decisions to exit their ventures, the timing of their exits, and the impact of entrepreneurship 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 adaptability across different life stages.

  • Examining the social consequences of age-entrepreneurship: how entrepreneurs of different age groups contribute to job creation, economic development, community 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 education (Fracasso & Jiang, 2022; Paray & Kumar, 2020), manufacturing (Newman et al., 2018), construction (Debbage & Bowen, 2018), food (Abdoli et al., 2012), and beverages (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 entrepreneurial education like gamification and simulation techniques; using an online game can increase students’ motivation, engagement, and learning. The effectiveness 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 entrepreneurs would surely add value to the literature.

  • Many industries have a feeble representation in the literature; like in India, agriculture 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 underdeveloped 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 democratic and monarchy governments would be useful in receiving insights on the impact of the entrepreneurial ecosystem and corruption on the age and entrepreneurship 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 failure, 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 significantly in recent years. Using a systematic review, this study facilitates researchers 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 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 studies 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 et al., 2023). With the knowledge about the most frequently used independent, dependent, mediating, moderating, and control variables in age and entrepreneurship 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 navigating 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 entrepreneurs 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 imperative 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 multiple languages could be integrated into future studies. Third, we limited our sample to fields such as economics, econometrics, finance, business, management, and social sciences. Future researchers could consider including other areas as well.