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Serial entrepreneurs: A review of literature and guidance for future research
--> ISSN 1540-627X
Full content URL: https://doi.org/10.1080/00472778.2021.1969657
Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that asmany as 50% of all entrepreneurs are serial entrepreneurs. Entrepreneurship research shows that most new ventures fail, yet serial entrepreneurs continually exit previous ventures and start new ones. Our study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis. Through our analysis, we identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize. Our perceptual map has identified four key research areas that researchers should focus upon: heuristics in entrepreneurship, entrepreneurial capabilities, the entrepreneurial ecosystem, and technological development and resources.
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Serial entrepreneurs: A review of literature and guidance for future research
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Serial entrepreneurs: a review of literature and guidance for future research
Dabić, Marina and Vlačić, Božidar and Kiessling, Timothy Scott and Caputo, Andrea and Pellegrini, Massimiliano (2021) Serial entrepreneurs: a review of literature and guidance for future research. Journal of Small Business Management . ISSN 0047-2778 (Print) 1540-627X (Online) Published Online First https://dx.doi.org/10.1080/00472778.2021.1969657
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Serial entrepreneurs: A review of literature and guidance for future research
Journal of Small Business Management
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Lorraine Uhlaner
Jeen Wei Ong
Abstract Human capital theory has gained attention in entrepreneurship study. Recently, Westhead, Ucbasaran, and associates' works have enhanced the interest on the effect of human capitals on entrepreneurialism of the entrepreneurs. However, as exploratory efforts, their studies seemed to be covering limited dimensions of human capitals and entrepreneurship.
Siri Terjesen
It remains a question whether serial entrepreneurs typically perform better than their novice counterparts owing to learning by doing effects or mostly because they are a selected sample of higher-than-average ability entrepreneurs. This paper tries to unravel these two effects by exploring a novel empirical strategy based on continuous time duration models with selection. We use a large longitudinal matched employer-employee dataset that allows us to track almost 220,000 individuals who have left their first entrepreneurial experience. Over 35,000 serial entrepreneurs are identified and followed in their second business, in order to evaluate how entrepreneurial experience acquired in the previous business improves persistence by reducing their exit rates. Our results show that serial entrepreneurs are not a random selection of ex-business-owners. The positive association found between prior experience and serial entrepreneurs' survival is mainly due to selection on ability, rather than the result of learning by doing.
Graciela Kuechle
Lebret Herve
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Donato Iacobucci
This article reports the analysis of case studies of portfolio entrepreneurs that suggests that one of the main reasons for the formation and expansion of business groups is the need to create an entrepreneurial team, which is achieved by giving minority shares in the new ventures to others, mainly former employees. This enhances entrepreneurs' ability to grow and diversify the businesses under their control without compromising their ownership control of the overall business group. The article identifies and discusses the different types of entrepreneurial teams developed by portfolio entrepreneurs: joint ventures with established entrepreneurs, employee involvement, and intrapreneurship. The latter two types were specifically interesting in studying situations where there was a dominant entrepreneur and associate entrepreneurs. The article enhances the theoretical and empirical understanding of how growth is achieved in the small firms sector through business group formation, and sheds insights on how entrepreneurial team dynamics operate in multiple business contexts.
Don Scott-Kemmis
Despite a proliferation of research in the field of entrepreneurship, our understanding of entrepreneurial learning remains limited. We do not have systematic answers to many key questions. To what extent does the context of the learning shape that learning? How does the prior experience of an entrepreneur influence what they learn in new ventures? Does the specific role that the entrepreneur plays in a new venture, and the characteristics of the venture team, influence learning? To address this gap, and to progress the broader program of empirical research into entrepreneurial learning, we need to more fully explicate both the context and the content of learning. That is the objective and contribution of this study. We find that prior experience, the “division of (decision-making) labor” and the “knowledge” characteristic of the venture team shape learning. One implication is that future research will need to assess more carefully both the content of new learning from the new venture experience, and the context of learning.
Jose Plehn-Dujowich
The contribution of serial entrepreneurs to entrepreneurial activity is significant: in Europe, 18–30% of entrepreneurs are serial; in the US, their contribution is about one-eighth. Yet, theories of entrepreneurship and industry dynamics presume that all firms are launched by novice entrepreneurs and firm failure is synonymous with exit from entrepreneurship. We propose a theory of serial entrepreneurship in which an entrepreneur has three occupational choices: maintain his business in operation, shut it down to enter the labor market to earn an exogenous wage, or shut it down to launch a new venture while incurring a serial startup cost. In equilibrium, a high-skill entrepreneur shuts down a business of low quality to become a serial entrepreneur, launching and subsequently closing firms until a high quality business is found; a low-skill entrepreneur shuts down a business of low quality to enter the labor market, never to become a serial entrepreneur. A decrease in the wage or serial startup cost, or an increase in the startup capital, enhances the contribution of serial entrepreneurs to entrepreneurial activity and promotes new firm formation (by increasing entrepreneurship and the number of new firms that survive), but its effect on the exit rate of new firms is ambiguous. We show the model is consistent with evidence relating to the impact of an entrepreneur’s characteristics and prior experience in entrepreneurship on the survival of his firm and his entry into and survival in entrepreneurship.
Dhamodaran Lingappan
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Pregled bibliografske jedinice broj: 1145578
Serial entrepreneurs: a review of literature and guidance for future research.
Dabić, Marina; Vlačić, Božidar; Kiessling, Timothy; Caputo, Andrea; Pellegrini, Massimiliano Serial entrepreneurs: A review of literature and guidance for future research // Journal of small business management (2021) doi:10.1080/00472778.2021.1969657 (znanstveni, online first)
CROSBI ID: 1145578 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov Serial entrepreneurs: A review of literature and guidance for future research
Autori Dabić, Marina ; Vlačić, Božidar ; Kiessling, Timothy ; Caputo, Andrea ; Pellegrini, Massimiliano
Vrsta, podvrsta Radovi u časopisima, znanstveni
Izvornik Journal of small business management (2021)
Status rada Online first
Ključne riječi Serial entrepreneur ; habitual entrepreneur ; systematic literature review ; multiple correspondence analysis ; HOMALS
Sažetak Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that as many as 50% of all entrepreneurs are serial entrepreneurs. Entrepreneurship research shows that most new ventures fail, yet serial entrepreneurs continually exit previous ventures and start new ones. Our study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis. Through our analysis, we identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize. Our perceptual map has identified four key research areas that researchers should focus upon: heuristics in entrepreneurship, entrepreneurial capabilities, the entrepreneurial ecosystem, and technological development and resources.
Izvorni jezik Engleski
Znanstvena područja Ekonomija
POVEZANOST RADA
Projekti: EK-H2020-823971 - Open Innovation – Research Translation and Applied Knowledge Exchange in Practice through University-Industry-Cooperation (OpenInnoTrain) (Dabić, Marina, EK - H2020-MSCA-RISE-2018) ( CroRIS )
Ustanove: Ekonomski fakultet, Zagreb
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Serial entrepreneurs: a review of literature and guidance for future research
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Research output : Contribution to journal › Article › peer-review
- Habitual entrepreneur
- Multiple correspondence analysis
- Serial entrepreneur
- Systematic literature review
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Silva, M. , Sousa, R. , Alves, P. , Madsen, A. , Lourenço, A. , Carvalho, A. S. , Andrade, A. , Silva, P. D. , Vlačić, B. , Martins, C. , Gomes, C., Gevrek, Z. E. , Oliveira, F. G. D. , Faria, G. , Marreiros, H. , Pinho, J. , Machado, J. , Araújo, J., Pinto, J. , Rego, A. , Gaspar, J. M. , Corbo, L. , Costa, L. , Fernandes, L. , Sottomayor, M. , Martins, N. , Hernández-Marrero, P. , Gonçalves, R. , Ribeiro, R. , Coelho, S. L. , Pereira, S. M. , Silva, S. , Rodrigues, V. , Sotiros, D. G., Valverde, C. J. L. , Leitão, A. , Julião, J. , Tavares, M. F. F. , Lages, C. R. , Elmashhara, M. G. & Teymourifar, A.
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Serial entrepreneurs : a review of literature and guidance for future research. / Dabić, Marina ; Vlačić, Božidar ; Kiessling, Timothy et al.
T1 - Serial entrepreneurs
T2 - a review of literature and guidance for future research
AU - Dabić, Marina
AU - Vlačić, Božidar
AU - Kiessling, Timothy
AU - Caputo, Andrea
AU - Pellegrini, Massimiliano
N1 - Publisher Copyright: © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2021/9/24
Y1 - 2021/9/24
N2 - Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that as many as 50% of all entrepreneurs are serial entrepreneurs. Entrepreneurship research shows that most new ventures fail, yet serial entrepreneurs continually exit previous ventures and start new ones. Our study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis. Through our analysis, we identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize. Our perceptual map has identified four key research areas that researchers should focus upon: heuristics in entrepreneurship, entrepreneurial capabilities, the entrepreneurial ecosystem, and technological development and resources.
AB - Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that as many as 50% of all entrepreneurs are serial entrepreneurs. Entrepreneurship research shows that most new ventures fail, yet serial entrepreneurs continually exit previous ventures and start new ones. Our study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis. Through our analysis, we identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize. Our perceptual map has identified four key research areas that researchers should focus upon: heuristics in entrepreneurship, entrepreneurial capabilities, the entrepreneurial ecosystem, and technological development and resources.
KW - Habitual entrepreneur
KW - HOMALS
KW - Multiple correspondence analysis
KW - Serial entrepreneur
KW - Systematic literature review
UR - http://www.scopus.com/inward/record.url?scp=85113770987&partnerID=8YFLogxK
U2 - 10.1080/00472778.2021.1969657
DO - 10.1080/00472778.2021.1969657
M3 - Article
AN - SCOPUS:85113770987
SN - 0047-2778
JO - Journal of Small Business Management
JF - Journal of Small Business Management
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Serial entrepreneurs: A review of literature and guidance for future research
Marina dabić, božidar vlačić, timothy kiessling, andrea caputo and massimiliano pellegrini, journal of small business management, highlights:.
This study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis.
Identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize.
The perceptual map has identified four key research areas that researchers should focus upon: heuristics in entrepreneurship, entrepreneurial capabilities, the entrepreneurial ecosystem, and technological development and resources.
Cite this paper:
Marina Dabić, Božidar Vlačić, Timothy Kiessling, Andrea Caputo & Massimiliano Pellegrini (2021) Serial entrepreneurs: A review of literature and guidance for future research, Journal of Small Business Management, DOI: 10.1080/00472778.2021.1969657
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Original research article, research on factors affecting serial entrepreneurial intention: an interpretive structure model.
- 1 School of Management, Nanjing University, Nanjing, China
- 2 School of Business, Hebei Normal University for Nationalities, Hebei, China
Serial entrepreneurship is a very common phenomenon in the world. Research on serial entrepreneurs is the core of understanding entrepreneurship and entrepreneurs, such as, why entrepreneurs insist on starting businesses many times? What affects the sustainability of entrepreneurship? Based on the interpretive structure model of systems engineering, this study constructs a hierarchical model of the factors affecting serial entrepreneurial intention, which proposed the basic conditions, key factors, and paths affecting serial entrepreneurial intention. Based on this, the hierarchical model of factors affecting serial entrepreneurial intention is also tested through a typical serial entrepreneurial case. The results show that: (1) there are 16 factors affecting serial entrepreneurial intention, and each factor plays a role at a specific level; (2) entrepreneurial expectations and identification and evaluation of opportunities are the key factors affecting serial entrepreneurial intention. We can improve the ability of the identification and evaluation of opportunities through entrepreneurial failure learning, and form reasonable entrepreneurial expectations; (3) entrepreneurial cognitive schema and behavioral addiction tendency directly affect entrepreneurs’ identification and evaluation of opportunities; (4) demographic factors, financial conditions, environmental conditions, and entrepreneurial experience are the basic conditions that affect serial entrepreneurial intention indirectly through emotional perception and motivation factors.
Introduction
Serial entrepreneurs are not limited to one entrepreneurial activity. They are representatives of entrepreneurial active groups ( Vaillant and Lafuente, 2019 ), and the practice of serial entrepreneurship is more and more common in all countries. Furthermore, research on serial entrepreneurs is the core of understanding entrepreneurship and entrepreneurs ( MacMillan, 1986 ), especially the sustainability of entrepreneurship. Studies have shown that serial entrepreneurs may be more likely to succeed over time ( Cope, 2005 ) and show a more positive attitude toward entrepreneurial failure ( Politis, 2008 ). However, when entrepreneurial activities succeed or fail, some entrepreneurs choose to end their entrepreneurial career, while others choose to start again. The intention of entrepreneurs to start again is called serial entrepreneurial intentions ( Simmons et al., 2016 ). As Simmons asked, what are the factors that affect entrepreneurs’ choice to start again? The serial entrepreneurship intention has attracted more and more interest in the field of entrepreneurship studies.
The existing studies mainly focus on the factors affecting serial entrepreneurial intentions from three perspectives. The first one is the comparative study, which compares serial entrepreneurship with novice entrepreneurship and portfolio entrepreneurship, to obtain the characteristics of serial entrepreneurship and the factors affecting serial entrepreneurial intention. For example, the ability to recognition of opportunity is more likely to be associated with serial entrepreneurship and portfolio entrepreneurship, and higher opportunity exploration ability is associated with portfolio entrepreneurship rather than serial entrepreneurship and novice entrepreneurship ( Parker, 2014 ). The second perspective is entrepreneurial reentry. After failure of entrepreneurship, entrepreneurs can choose to close enterprises to enter the labor market or to start a new one, and the latter will become serial entrepreneurs. At present, the research on entrepreneurial reentry mainly focuses on distress exits and failure loss, entrepreneurial learning from failure, and failure attribution ( Ucbasaran et al., 2003 ; KoÇAk et al., 2011 ; Lin and Wang, 2018 ; Williams et al., 2020 ). The last one is the antecedent variables affecting serial entrepreneurial intention, mainly including the characteristics of entrepreneurs, traits and entrepreneurial experience, and so on ( Plehn-Dujowich, 2009 ; Spivack et al., 2014 ; Hsu et al., 2017b ; Simmons et al., 2018 ; Williams et al., 2020 ). To sum up, the factors affecting serial entrepreneurial intention are complex, and the achievements of relevant research are rich. However, due to the relatively scattered perspectives, the internal structure of how the complex factors affect serial entrepreneurial intention is unclear, so a definite hierarchical model needs to be established.
This study has sorted out 36 factors that may affect serial entrepreneurial intention based on the literature review. After the analysis and discussion of the expert group, 16 factors are finally formed. Then, using the method of interpretive structure model, the hierarchical structure is obtained, which proposes the mutual relationship and multilevel structure of the factors affecting serial entrepreneurial intention. Moreover, this study further tests the hierarchical structure model of factors affecting serial entrepreneurial intention through a case study.
Literature review
Serial entrepreneurs are more likely to run their businesses more successfully. Success may make entrepreneurs fall into the trap of complacency and perform poorly in subsequent entrepreneurship, whereas if failed entrepreneurs can bear the “sadness” that may prevent them from returning to entrepreneurship, they will learn from failure and improve themselves ( Shepherd, 2003 ). Entrepreneurs with failed entrepreneurial experience are 17% less likely to restart a business than entrepreneurs with successful entrepreneurial experience ( Amaral et al., 2011 ). Existing studies show that the factors influencing serial entrepreneurial intention can be categorized into 16 individual factors, 12 entrepreneurial level factors, and eight environmental factors as presented in Table 1 .
Table 1. Identified factors of serial entrepreneurial intention.
Individual factors
The individual factors affecting serial entrepreneurial intention are mainly studied from two perspectives. First of all, personal traits. Some studies have pointed out that both Sensation-seeking trait disposition (A1) and workaholism trait disposition (A2) will affect serial entrepreneurship ( Carr et al., 2016 ); The psychological, emotional, and physiological aspects of entrepreneurial experience strengthen the behavioral addiction to entrepreneurship (A3), which will promote individuals to repeatedly carry out entrepreneurial activities ( Spivack et al., 2014 ). In addition, age and gender are also important factors affecting serial entrepreneurship. The older the entrepreneur’s age (A4), the slower the speed of restarting ( Lin and Wang, 2018 ). Career stages (A9) are related to the possibility of entrepreneurs’ reentry after failure, the relationship of which is inverted U shaped ( Baù et al., 2017 ). Moreover, males score higher than females on openness factor which may be the most important factor of the big five personality, which differentiates entrepreneurs from other people ( Antoncic et al., 2015 ). Gender moderates the negative relationship between the perceived lack of support barrier and the entrepreneurial intention, which exposes some cross-cultural differences, and that females (relative to males) perceive the lack of support barrier, fear of failure, and lack of competency barriers as more important in entrepreneurial activities ( Shinnar et al., 2012 ). Probability of female entrepreneurs (A5) returning to entrepreneurial activities after failure is less than that of males ( Simmons et al., 2018 ).
Individual psychological perception is another perspective from which many scholars also put forward the factors affecting serial entrepreneurial intention. Some studies have pointed out that the perceived failure loss (A6) has a slightly significant negative impact on the speed of entrepreneurial reentry ( Lin and Wang, 2018 ), however, when the perceived failure loss is very huge, entrepreneurs may be motivated by failure to reenter into entrepreneurial activities ( McGrath, 1999 ). The more individuals actively describe their entrepreneurial experience according to perceived financial gains (A7) or losses from their prior venture, the weaker their subsequent entrepreneurial intention is, and vice versa ( Hsu et al., 2017b ). The individuals who receive positive performance feedback (A8) from prior ventures have strong serial entrepreneurial intentions ( Carr et al., 2016 ). At the same time, studies have shown that individual emotions also have a significant impact on serial entrepreneurial intention. Negative emotion (A11) is not necessarily an obstacle to reentry into entrepreneurial activities as previously thought, the interaction between controllability and emotion is the core of explaining entrepreneurial reentry ( Williams et al., 2020 ). Moreover, it is further found that the interaction between failure attribution and emotional intensity/emotional valence (A10) will affect the way of individual entrepreneurial reentry ( Williams et al., 2020 ). Entrepreneurs with high-risk aversion are more likely to be novice entrepreneurs, while entrepreneurs with low-risk aversion (A12) are more likely to be serial entrepreneurs ( Parker, 2014 ). Entrepreneurial failure will make entrepreneurs anxious (A13). The higher degree of anxiety, the greater the tendency of a person to become a salaried employee after the first entrepreneurial failure. The less anxious he is, the more inclined he is to regard entrepreneurship as a way of life and adhere to it in entrepreneurial behavior ( Zelekha et al., 2018 ). In addition, scholars have pointed out that entrepreneurs’ psychological capital is one of the factors affecting serial entrepreneurship intention. Entrepreneurs with more confidence (A14) can better recover from emotional, cognitive, social, and economic ventures, and are more likely to conduct subsequent ventures ( Hayward et al., 2010 ). As the dimensions of entrepreneurial self-efficacy, financial self-efficacy and marketing self-efficacy are related to entrepreneurial intention. Family business environment may be very important for individuals to develop financial self-efficacy, which affects entrepreneurial intention ( Antoncic et al., 2021 ), and that individuals with higher marketing self-efficacy are more likely to create a firm ( Antoncic et al., 2016 ). Under the same conditions, the higher the entrepreneurial self-efficacy (A15), the higher the subsequent entrepreneurial intention. Moreover, the degree of entrepreneurial self-efficacy will moderate the impact of financial loss after entrepreneurial failure on subsequent entrepreneurial intentions ( Hsu et al., 2017b ). There is also a significant negative correlation between prevention-focused cognition (A16), which is one of the regulatory focuses of entrepreneurs, and serial entrepreneurial intention ( Simmons et al., 2016 ).
Entrepreneurial level factors
The entrepreneurial level factors may directly affect the serial entrepreneurial intention. Firstly, many studies have shown that entrepreneurial experiences can affect the serial entrepreneurial intention, such as the length of venture creation experience (A17), experienced entrepreneurial failure (A18) ( Hsu et al., 2017a ; Lafuente et al., 2018 ). However, domestic scholars also proposed that entrepreneurial failure has a positive impact on serial entrepreneurial intention ( Tian and Cao, 2021 ). Entrepreneurs with entrepreneurial success (A19) are more likely to reenter faster ( Amaral et al., 2011 ). Exit mode is an important factor affecting serial entrepreneurship intentions. If entrepreneurs are the prevention focus, distress exits (A20) reduce the serial entrepreneurial intention of such entrepreneurs ( Simmons et al., 2016 ). In addition, entrepreneurs’ expectations of new venture’s prospects (A21) or current business can affect the motivation of entrepreneurs, which provides incentives for entrepreneurs to reenter entrepreneurial activities ( KoÇAk et al., 2011 ).
Second, entrepreneurial cognition is also an emphasized factor affecting serial entrepreneurial intention. Domestic scholars put forward that entrepreneurial failure affects the willingness to start a business again through entrepreneurs’ learning from failure (A22) ( Tian and Cao, 2021 ), which has been proved to be the “entrepreneurial catalyst” to entrepreneurial reentry ( KoÇAk et al., 2011 ). The learning process generated from past entrepreneurial experiences may affect the entrepreneurial cognitive schema (A23), which may be important for the decision to set up a new company ( Vaillant and Lafuente, 2019 ). Moreover, opportunity identification (A24) and opportunity appraisal (A25) are the key factors affecting entrepreneurs to become a serial entrepreneur, which provide the inducement to reenter into entrepreneurial activities ( KoÇAk et al., 2011 ; Parker, 2014 ; Carbonara et al., 2019 ). Studies also proposed that failure attribution (A28) is one of the factors affecting the way to effectively reenter into entrepreneurship after failure ( Williams et al., 2020 ), and that those entrepreneurs who attribute success to internal factors will become habitual entrepreneurs ( Ucbasaran et al., 2003 ). Furthermore, attribution to internal and controllable factors has a significant positive impact on their serial entrepreneurial intention ( Zhu et al., 2021 ).
Third, existing studies focus on the human capital affecting serial entrepreneurship intention ( Carbonara et al., 2019 ). Relevant studies have further verified that human capital seems to be positively associated with the revival of entrepreneurship, in which entrepreneurial experience (A26) has the strongest impact, and the second is the general human capital ( Stam et al., 2008 ). The past entrepreneurial experience, whether positive or negative, will significantly affect the entrepreneurial reentry ( Vaillant and Lafuente, 2019 ). An entrepreneur with high entrepreneurial skills (A27) will continue to operate if he has enough profits. When the expectation of existing venture’s prospects is negative, he will choose to become a serial entrepreneur ( Plehn-Dujowich, 2009 ).
Environmental factors
Environmental factors are also important factors affecting serial entrepreneurial intention. First of all, the social capital of entrepreneurs. Studies have shown that the strong or weak relationship in structural capital (A30) plays a crucial role in the process of entrepreneurial reentry. Strong relationship can support entrepreneurs from exit to reentry, while weak relationship plays a key role in recognizing and taking advantage of new opportunities. Meanwhile, relational capital (A29) in the form of trust has great benefits in the process of entrepreneurial reentry and can promote interpersonal relations and subsequent business transactions ( KoÇAk et al., 2011 ). Entrepreneurs with family or friend support (A31) seem to adhere to their preference for entrepreneurship without being intimidated by negative entrepreneurial events ( Stam et al., 2008 ). Although family support can provide multiple resources and psychological support for serial entrepreneurs and help entrepreneurs recover from negative entrepreneurial events, the impact of family support on serial entrepreneurial intention is not direct, but mixed ( Lin and Wang, 2018 ).
Second, there are legal factors affecting serial entrepreneurial intention. Studies have shown that a friendly bankruptcy law (A32) can reduce barriers to reentry, which means less time and less cost, and give entrepreneurs a new start by encouraging them to take more risks and set up more new companies ( Lee et al., 2011 ).
Third, social factors can also affect serial entrepreneurial intention. In the environment with low visibility of information about prior failures (A34) and high public stigma of entrepreneurial failure (A33), failed entrepreneurs are more likely to engage in entrepreneurial activities again ( Simmons et al., 2013 ). Some studies have proposed that the labor market rigidity (A35) increases the possibility of individuals’ reentry into entrepreneurial activities, and market volatility (A36) also affects the relationship between entrepreneurial learning from failure and serial entrepreneurial intention ( Zhang and Wang, 2020 ).
To sum up, according to the literature review, there are 16 factors affecting serial entrepreneurial intention after categorization of 36 factors as given in Table 2 .
Table 2. Identified factors of serial entrepreneurial intention.
Materials and methods
Interpretative structural model (ISM) is a kind of structure modeling technique, which was developed by Professor Warfield to analyze the problems related to complex social and economic systems ( Warfield, 1978 ; Muruganantham et al., 2018 ). The ISM refers to a process that transforms unclear and poorly articulated models of systems into visible and well-defined models ( Sushil, 2012 ). This method decomposes the complex system into several sub-system elements, extracts the interaction mechanism between the elements of the complex system with practical experience and knowledge, and finally formed a theoretical construct ( Valmohammadi and Dashti, 2016 ). Compared with the traditional empirical analysis method of influencing factors, the ISM method is characterized by dynamically supplementing the required data according to the research progress. Given its advantages in dynamicity, complementarity and integrity, ISM method has been applied to many studies in the field of management, such as human resource, entrepreneurship, and engineering management ( Mandal and Deshmukh, 1994 ; Wei et al., 2019 ; Liu et al., 2021 ).
The main concepts involved in the paper include general matrix, adjacency matrix, reachability matrix, and the highest-level element set. A general matrix is a rectangular table with m rows and n columns composed of i × j numbers, and the element a ij represents the element in row i and column j.
The adjacency matrix describes the direct relationship between each row and column of factors. For the general system S (F 1 , F 2 ,…, F n ) with n factors, the adjacency matrix is defined as A = [a ij ]n × n, where a ij = 1 (when element F i has a direct effect on F j ) or 0 (when elements F i have no direct effect on F j ).
The reachable matrix is used to represent the direct or indirect relationship between the influencing factors. Using the operational properties of Boolean matrices, the reachable matrix R satisfies the equation: (A + I) k –1 ≠(A + I) k = (A + I) k +1 = R, where A represents the adjacency matrix, I represents the identity matrix, and K represents the number of operations.
Highest-level element set refers to a set of elements that cannot reach other elements except themselves. R(F i ) refers to the reachable set of F i and C(F j ) represents the antecedent set of F j . If R(F i ) = R(F i )∩C(F j ) (where i = j), R(F i ) is placed in a set corresponding to the level and excluded in the analysis of subsequent levels, then R(F i ) is the highest-level element set ( Hussain et al., 2016 ).
This paper uses ISM method to carry out a study on the factors influencing serial entrepreneurial intention, including four steps. This paper firstly identifies the antecedent factors of SEI through literature review. Secondly, an expert group is set up to screen out the important factors from the antecedent factors and determine the relationship between the factors. Thirdly, using statistical software (e.g., MATLAB), we design the relationship structure of each factor and obtain the corresponding reachability matrix. Fourthly, this research carries out hierarchical processing and forms a multilevel conceptual model based on the reachability matrix.
Existing literature has studied the antecedents of serial entrepreneurial intention from multiple perspectives. We firstly identify 16 factors (as shown in Table 2 ) through literature review to help further screen by the expert panel.
In the second step, an expert panel was established to clarify the key factors affecting SEI and interrelation of 16 factors. The panel consists of seven members, including two scholars in the research field of entrepreneurship, three serial entrepreneurs, and two doctoral students. After all the members of the expert panel understand the basic concepts of SEI and the 16 antecedents, they further judged back-to-back whether the 16 factors had an important impact on SEI. The result of the discussion showed that 16 factors were unanimously agreed by more than four members ( Kuo et al., 2010 ).
Thirdly, the relationship between 16 factors was discussed and seven members of the expert panel were asked to conduct a pair-wise comparison of 16 factors. We denoted the 16 factors as F i , where i = 1, 2, ……, 16, as given in Table 2 . The experts were asked to select from one of the following four types when judging the relationship between the factor F i and F j :
• Type V: factor F i influences factor F j directly
• Type A: factor F j influences factor F i directly
• Type X: factor F i influences factor F j each other
• Type O: factor F i and factor F j are mutually unrelated
In the process of judging the relationship between factors, we still adopt the opinions of most experts (more than four members), and the final relationship between the 16 elements presented is unanimously confirmed by all the members, as presented in Table 3 .
Table 3. Pair-wise comparison of 16 factors.
In the fourth step, we used matrix operations to divide the 16 important influencing factors into different levels and thus get a multilevel ISM. A 16 × 16 square matrix was used to express the logical correlation among the important factors affecting SEI based on Table 3 , forming an adjacency matrix A that covers any two or two elements in the whole influencing factors system. In this matrix, a ij refers to the elements in line i and column j of a square matrix (i, j = 1, 2, ……, 16), indicating the relationship between factors F i and F j . “0” in row i and column j represents that factor i has no direct influence on the factor j, while “1” indicates factor i directly influences factor j. The results expressed in 16 × 16 adjacency matrix from Table 3 are presented in Table 4 .
Table 4. Adjacency matrix A of 16 factors.
As the influencing factors of complex systems are not directly related, we use the reachability matrix (R) to obtain the relationship between the direct and indirect effects of one factor on other factors, as well as the transitive representation of each factor. In order to express the transfer relationship between the direct or indirect effects of 16 factors, we need to convert adjacency matrix into reachable matrix.
Element r i can reach r j by the distance of unit 1, and r j can still reach the next influencing factor by the distance of unit 1 in the reachability matrix. We add adjacent matrix A and unit matrix I to get matrix B, which can further get the reachability matrix through Boolean algebraic power operation with the help of software MATLAB. According to the operation rules of transforming adjacent matrix into reachable matrix, we calculate Bn until the calculation satisfies B k –1 = B k (K = 15), which shows direct and indirect relationships among 16 influencing factors of SEI, as is presented in Table 5 .
Table 5. Reachability matrix R of 16 factors.
Based on reachability matrix, this paper sorts out the highest-level element set. When R(Fi) = R(Fi)∩C(Fi), R(Fi) is placed in a set corresponding to the level and excluded in the analysis of subsequent levels. This paper continues to find the new highest-level elements from the remaining reachability matrix, and then finds the highest-level elements contained in each level by analogy. For example, after the first hierarchical process, the element satisfies R(F i ) = R(F i )∩C(F i ) is 6, so {6} is the first level. After that, 14 is found to satisfy the condition after the element containing 6 is removed from the list, so 14 is the second layer. In the same way, this paper divides these 16 factors into six levels, and the final multilevel structure hierarchy is presented in Table 6 . The final hierarchical results were obtained as follows:
Table 6. Interpretive structure model analysis of 16 factors.
• Level 1: 6
• Level 2: 14
• Level 3: 8, 9, 11, 13, 15
• Level 4: 7, 10, 16
• Level 5: 1, 3, 4, 5, 12
• Level 6: 2
Based on the reachability matrix and highest-level element sets, the multi-level structure hierarchy chart of serial entrepreneurship intention is drawn, from which interpretive structure model of key factors affecting serial entrepreneurship intention is obtained (as shown in Figure 1 ). According to the figure, factors affecting serial entrepreneurship intention show a multilevel hierarchical structure with six levels. The specific analysis is summarized as follows:
Figure 1. Interpretive structure model.
Entrepreneurial expectation is the key factor affecting serial entrepreneurial intention, which directly affects serial entrepreneurial intention. The research shows that entrepreneurs’ psychological capital has an indirect impact on serial entrepreneurial intention, and entrepreneurial expectation is the mediator. Entrepreneur can cultivate entrepreneurs’ psychological capital such as self-confidence and entrepreneurial self-efficacy through a variety of social support methods; meanwhile, entrepreneurial learning from failure helps to promote entrepreneurs’ ability of opportunity identification and evaluation, which affects entrepreneurial expectations. Therefore, entrepreneurs should be encouraged to learn from failure and improve their human capital.
Opportunity identification and evaluation play an important role in the formation of serial entrepreneurial intention. Entrepreneurial cognitive schema and behavioral addiction tendency have a direct impact on opportunity identification and evaluation. At different career stages, the entrepreneurial cognitive schema shows differentiated characteristics. Entrepreneurs can enrich entrepreneurial cognitive schema through continuous learning over time. Behavioral addiction tendency has been found as an important driving factor, which is mainly affected by demographic factors.
In addition, this study also shows that demographic factors, financial conditions, environment conditions, and entrepreneurship experience are the basic conditions affecting serial entrepreneurial intention, which indirectly affect serial entrepreneurial intention, and emotional perception and motivation factors are the mediators. To be specific, entrepreneurial experience and financial conditions directly affect entrepreneurs’ emotional perception. Emotional regulation is closely related to how to learn after entrepreneurial failure. The environment conditions and financial conditions will directly affect the motivation of entrepreneurs and indirectly affect the failure attribution.
This research chooses a case of serial entrepreneur to study the factors affecting serial entrepreneurship intention in order to test the model we have obtained above. There are three main reasons for choosing this case. First, the entrepreneur in this case has the intention to start a new business after the success or failure. Second, he is a typical serial entrepreneur with many entrepreneurial experiences. Third, it is convenient to obtain data about this case. The entrepreneur in this case is a typical representative of Chinese internet entrepreneurs, which easily provides a large number of data. Therefore, this case is representative for studying the factors affecting serial entrepreneurship intention. Based on the principle of true and valid data selection and sources, we collected news interviews, published books, company materials, and publicly published academic research results related to serial entrepreneurial behavior to ensure the effectiveness of case analysis.
The entrepreneur in the case, represented as A , has started businesses for six times, all in the internet industry. After many entrepreneurial failures, the internet service company he founded has been listed, which ranks third in Internet industry of China with a market value of more than 140 billion dollars now. Entrepreneurial process can be roughly divided into three stages ( Wang, 2020 ).
In the first stage, A and his partners formed an entrepreneurial team to start their business in China. They established three social networking sites in succession because of the belief that social networking sites (opportunity identification and evaluation) were a valuable and promising opportunity (entrepreneurial expectation). Although they focused on the internet industry, which they were familiar with and identified valuable opportunities, all the three startups failed. A noted that the previous two startups failed because of the emphasis on products and the neglect of promotion (entrepreneurial learning from failure), so they paid more attention to promotion (human capital) in the third startup. From this, we can see that learning from failure promote human capital, which laid the foundation for the next startup. However, the financial return of the third social networking site was not satisfactory (entrepreneurial expectation), resulting in being sold. This failure was so hard for him that he chose employment for a while. As mentioned above, A started businesses for three times in succession because of the good entrepreneurial expectations for the identified opportunities and voluntarily sold the third social networking sites because of the bad expectation for financial return, which interpreted that entrepreneurial expectation is the key factor affecting serial entrepreneurial intention, and that identification and evaluation of opportunity indirectly affect serial entrepreneurship intention through entrepreneurship expectation.
In the second stage, A chose to start a new business again after 1 year’s employment. A looked for opportunities which were promising and focused on the blog after much thought (opportunity identification and evaluation). He built two blog websites in succession in this stage, but he still failed for various reasons. The first blog website was going well at first, but it had to be shut down because of unexpected accident that a large number of sensitive remarks caused by irregular management appeared in blogs. However, the entrepreneurial failure did not make A lose his confidence but showed his maturity to the entrepreneurial team which strengthened the confidence of the entrepreneurial team (psychological capital), and built the second blog website at last. From this, we can see that psychological capital affects serial entrepreneurial intention. As mentioned above, A started businesses five times and focused on an Internet-related entrepreneurship program in the first and second stages, which reflected that he was very persistent in starting businesses and actively looked for entrepreneurial opportunities in the Internet industry (opportunity identification and evaluation). As A said in the interview: “I don’t regard entrepreneurship itself as a special thing. It’s just my lifestyle and I have an extreme adherence to entrepreneurship” (behavioral addiction tendency). So behavioral addiction tendency affects serial entrepreneurial intention through opportunity identification and evaluation.
In the third stage, A constantly studied websites and products and finally found an entrepreneurial opportunity, that is a business website (opportunity identification and evaluation). He determined the path to build a business website based on the experience of previous failures and development mode of internet marketing he summarized (entrepreneurial cognitive schema). He established a group-buying website and achieved great performance. As mentioned above, entrepreneurial cognitive schema affects serial entrepreneurial intention through opportunity identification and evaluation. China Youth Daily once published A ’s words: The entrepreneurs failed because of immature opportunity which was incompatible with the environment 10 years ago. However, it does not mean that this thing should not be done, and it may be successful to do it at another time (entrepreneurial environment), showing that the entrepreneurial environment is a basic factor affecting serial entrepreneurship intention.
To sum up, the discussion of the case is in line with the interpretive structure model constructed in this paper, the key factors which are affecting serial entrepreneurship intention show hierarchical characteristics.
This study sorted out 16 key factors affecting serial entrepreneurship intention. According to the method of interpretive structure model of system engineering, this study constructs a hierarchical model of the factors affecting serial entrepreneurial intention and tests it through case study, which defines the key factors, basic conditions, and paths affecting serial entrepreneurial.
The results of this study show that entrepreneurial expectation is the key factor affecting serial entrepreneurial intention, which directly affects serial entrepreneurial intention. Entrepreneurs may be forced to quit the enterprise due to insolvency, or they may take the initiative to quit the existing enterprise because the performance of the enterprise fails to meet the expectations of entrepreneurs ( Westhead et al., 2005 ; Ucbasaran et al., 2010 ), or because they find new business opportunities ( Hessels et al., 2011 ). When entrepreneurs’ distress exits, they can improve their ability to identify and evaluate opportunities through entrepreneurial recovery and learning from failure, which will help them to form reasonable entrepreneurial expectations. While they choose to exit, they may have serial entrepreneurial intention due to their positive expectation of new business opportunities. Moreover, the exit mode will also affect entrepreneurial expectation through the entrepreneur’s psychological capital. Different from previous studies that focus on the distress exits affecting serial entrepreneurial intention, this study believes that the formation of serial entrepreneurial intention of entrepreneurs who take the initiative to quit is also a topic that needs to be paid attention to. In addition, this study proposes that social capital has a significant impact on entrepreneurs’ psychological capital. The risk and pressure of entrepreneurship are alleviated by social support, which helps to stimulate entrepreneurial resilience of entrepreneurs ( Zhang and Li, 2020 ) and improve their psychological resilience, finally affecting serial entrepreneurial intention.
This study also shows that identification and evaluation of opportunity play an important role in the formation of serial entrepreneurial intention. Entrepreneurial cognitive schema and behavioral addiction tendency have a direct effect on identification and evaluation of opportunity, which in turn affects serial entrepreneurial intention. Vaillant and Lafuente (2019) proposed that the learning process generated in the past entrepreneurial experience may affect entrepreneurial cognitive schema, which is very important for an entrepreneur to reenter into new entrepreneurship and become a serial entrepreneur. The findings of this study not only further explain the path of entrepreneurial experience affecting serial entrepreneurial intention but also indicate that there is a cognitive mechanism behind opportunity identification. In addition, entrepreneurs who have the behavioral addiction tendency will think compulsively and look for innovation and opportunities continuously ( Spivack et al., 2014 ), to become serial entrepreneurs. Furthermore, demographic factors directly affect behavioral addiction tendency, which indicates that behavioral addiction tendency is related to physiological factors to a certain extent.
In addition, demographic factors, financial conditions, environmental conditions, and entrepreneurial experience are the basic conditions that affect serial entrepreneurial intention, which work indirectly mainly through emotional perception and motivation factors. The findings in this study help us better understand the persistence of entrepreneurial spirit and analyze the formation process of serial entrepreneurial intention.
Implications
This study has three main aspects in theoretical contribution. Firstly, the ISM model clearly interprets the internal relationship and hierarchical structure of the factors affecting serial entrepreneurial intention and makes contributions to understanding serial entrepreneurship intention in depth. Although the existing literature has integrated the factors affecting serial entrepreneurship intention ( Tipu, 2020 ), which has not constructed the internal relationship and hierarchical structure of the influencing factors. As Zhao et al. (2014) note that the studies on the factors affecting serial entrepreneurship intention still lack depth, and what role the factors play and how the factors exert their influence need to be further analyzed. Based on the existing literature on the factors affecting serial entrepreneurship intention, this study constructs an ISM model showing a multilevel hierarchical structure with six levels, which defines the key factors, basic conditions, and paths affecting serial entrepreneurial intention. Secondly, this study contributes to the theoretical development of serial entrepreneurship research. The results of this study show that entrepreneurial expectation is the key factor affecting serial entrepreneurial intention, which directly affects the serial entrepreneurial intention, and that identification and evaluation of opportunity indirectly affect serial entrepreneurship intention through entrepreneurship expectation. As Parker (2014) notes, the key factor that decides why some people become serial entrepreneurs while others remain novice entrepreneurs is the identification and evaluation of opportunity, and identification and evaluation of opportunity play an important role in the formation of serial entrepreneurial intention. This is proved by this study. Furthermore, this study puts forward influencing mechanism of identification and evaluation of opportunity, which further supplements the conclusion and defines the key role played by entrepreneurial expectation. In addition, from results, we also suggest that entrepreneurial cognitive schema and behavioral addiction tendency have a direct effect on identification and evaluation of opportunity, which provides new perspectives and useful clues for opportunity cognition mechanism. Existing studies have identified the unique regular pattern of identification and evaluation of opportunities of serial entrepreneurs, but have not yet explored the cognitive mechanism behind the regular pattern ( Yu et al., 2020 ). Prototype model is one of the recognition modes for entrepreneurs to find opportunities, the higher the matching degree between things and prototypes, the more likely they are to find entrepreneurial opportunities ( Shane, 2003 ). Entrepreneurial cognitive schema affects the prototypes and cognitive modes of opportunity identification, which is a useful clue. At the same time, behavioral addiction tendency as a special pathological feature is closely related to the individual nervous system and can affect individual cognition ( Moore et al., 2021 ), which provides a new perspective for the study of the mechanism of opportunity cognition. Moreover, entrepreneurs with behavioral addiction tendency will have such special behaviors as compulsive thinking, conceit, and neglect of family and friends ( Spivack et al., 2014 ), which reflects the dark side of entrepreneurial activities. This study finds that demographic factors directly affect behavioral addiction tendency, which provides useful clues for the study of the dark side of entrepreneurial activities. Thirdly, the ISM model shows multiple influencing paths of the factors affecting serial entrepreneurial intention, which provides a framework for the research of serial entrepreneurship intention. Although some influencing paths have been confirmed by empirical research ( Parker, 2014 ; Wang et al., 2018 ; Zhang and Wang, 2020 ), some paths still need to be explored. It is interesting to note that in Figure 1 , individual factors and environmental factors are below the third level, and the entrepreneurial factors are above the fourth level except for entrepreneurial experience (F5). Based on the role and the descriptive definition of entrepreneurial experience in ISM model, it is found that more attention is paid to the entrepreneurial failure context in existing studies and the research on the mode of distress exits exit is more extensive, while the research on the mode of taking the initiative to exit is lacking, which may be one of the reasons why the entrepreneurial factors are above the fourth level except for entrepreneurial experience (F5).
The conclusion of this study provides enlightenment for entrepreneurs and entrepreneurial management organizations in managing entrepreneurial activities. Firstly, entrepreneurial expectation is the direct key factor affecting serial entrepreneurship intention. Psychological capital affects entrepreneurial expectation, which in turn affects serial entrepreneurial intention. Entrepreneurial self-efficacy affects the willingness to participate in entrepreneurial activities in the future ( Hsu et al., 2017b ). Maintaining a high degree of self-efficacy in entrepreneurial activities can enhance serial entrepreneurship intention and make entrepreneurs more persistent. Although self-confidence helps to recover from entrepreneurial failure, entrepreneurs’ overconfidence in environmental cognition will reduce entrepreneurial performance ( Li and Cheng, 2018 ), so entrepreneurs should maintain moderate and reasonable self-confidence and high entrepreneurial self-efficacy. Secondly, identification and evaluation of opportunity have a direct effect on entrepreneurial expectation, which in turn affects serial entrepreneurial intention. So entrepreneurs should effectively improve their ability to identify and evaluate opportunities. This study also proposes two strategies to improve the ability of identification and evaluation of opportunity. One, human capital (e.g., rich entrepreneurial experience) helps entrepreneurs to identify entrepreneurial opportunities and strengthen their ability to evaluate and develop entrepreneurial opportunities ( Ucbasaran et al., 2003 , 2008 ). Entrepreneurs should effectively learn and absorb entrepreneurial failure experience, especially in the context of entrepreneurial failure. Two, entrepreneurs enrich their entrepreneurial cognitive schema through continuous learning ( Vaillant and Lafuente, 2019 ), providing an effective cognitive mechanism for the identification and evaluation of opportunities. Thirdly, financial conditions and entrepreneurial environment are the basic conditions affecting serial entrepreneurship intention. Therefore, government departments should provide entrepreneurial education such as failure education and emotional education to guarantee the entrepreneurs’ learning. At the same time, the government needs to provide strong support in entrepreneurship policy, both financially and psychologically, to create a good economic and social environment for entrepreneurial activities.
Limitation and future research
This study provides some new ideas and directions for future research, but there are still some limitations. Firstly, this study uses the method of interpretive structure model to propose a hierarchical structure model of the factors affecting serial entrepreneurial intention. The method is one method of systems engineering that has been partly applied in the field of entrepreneurship ( Muruganantham et al., 2018 ; Wei et al., 2019 ), but the applicability of it still needs to be further studied. Secondly, the hierarchical model proposed in this study lacks strong empirical support. Future research can carry out empirical exploration of relevant approaches to provide empirical support for the relationship between factors. Finally, the single case study in this paper seems not enough to fully explain the interpretive structure model of factors affecting serial entrepreneurial intention. As Cardon et al. (2011) note approaches of sense-making under different cultural backgrounds may have different effects on individual intentions and behaviors. Future research can enrich the model using more cases from different cultural backgrounds.
In addition, the hierarchical model proposed in this study provides a new research approach and direction for future research on serial entrepreneurial intention. First of all, we need to pay more attention to the research on exit modes, especially the mode of taking the initiative to exit. As indicated earlier, entrepreneurs may actively quit entrepreneurship or passively quit entrepreneurship, but less attention is paid to the mode of taking the initiative to exit in existing studies ( Yu et al., 2020 ). Along this line, we need to further explore the influencing mechanism of different exit modes on serial entrepreneurship intention, especially the impact of the mode of taking the initiative to exit on subsequent entrepreneurial decisions. Secondly, we need to continue to explore the cognitive mechanism behind entrepreneurs’ opportunity identification. Entrepreneurs use the cognitive structure of identifying opportunities to compare new ideas with opportunities, to identify opportunities ( Santos et al., 2015 ). Entrepreneurial cognitive schema is an “action-based knowledge structure” used by entrepreneurs based on highly developed and orderly knowledge ( Mitchell et al., 2000 ), so entrepreneurs identify opportunities that match the prototypes in entrepreneurial cognitive schema. However, this study shows that serial entrepreneurs enrich their entrepreneurial cognitive schema through continuous learning, which can update the cognitive structure used to identify opportunities. Whether entrepreneurs only recognize the opportunities that match the prototypes or update their entrepreneurial cognitive schema (prototype model) to identify opportunities still needs further exploration. At the same time, the pathological perspective is a new perspective for the study of cognitive mechanism behind entrepreneurs’ opportunity identification. As Moore et al. (2021) note the pathological characteristics of entrepreneurs affect their cognitive structure, so we need to continue to explore the impact of other types of neurological or pathological characteristics on opportunity recognition, such as insomnia and obsessive-compulsive personality disorder. Thirdly, we need to further explore the dark side of entrepreneurship. Emotional reaction, performance feedback, and entrepreneur–enterprise connection in entrepreneurial activities will all become reinforcing factors of behavioral addiction to entrepreneurship ( Yu et al., 2021 ). Along this line, future research can continue to explore reinforcing factors of behavioral addictive tendency, such as physiological factors and other dark sides of entrepreneurial activities.
Data availability statement
The original contributions presented in this study are included in the article/supplementary material, and the datasets generated for this study are available on request to the corresponding author.
Author contributions
XB was responsible for drafting the manuscript, as well as the acquisition, analysis and interpretation of data. DC and YC participated in the data analysis and revising of the manuscript. All authors have read and approved the final manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords : serial entrepreneurial intention, entrepreneurial reentry, interpretive structure model, entrepreneurial expectations, entrepreneurial cognitive schema, behavioral addiction tendency
Citation: Bai X, Cheng D and Chen Y (2022) Research on factors affecting serial entrepreneurial intention: An interpretive structure model. Front. Psychol. 13:992141. doi: 10.3389/fpsyg.2022.992141
Received: 12 July 2022; Accepted: 27 September 2022; Published: 25 October 2022.
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Copyright © 2022 Bai, Cheng and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Dejun Cheng, [email protected]
This article is part of the Research Topic
Psychological Determinants of Entrepreneurial Intentions and Behaviors
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Introduction, section snippets, references (78), recommended articles (6).
Journal of Business Research
No entrepreneur steps in the same river twice: limited learning advantage for serial entrepreneurs ☆.
Deterministic learning is less feasible in high-noise and low-signal entrepreneurship contexts. The empirical evidence on serial entrepreneurs having an advantage over novice entrepreneurs is mixed. Entrepreneurs learn by lowering high noise ( w ) and increasing the fidelity of a learning outcome ( θ ). We draw on Jovanovic and Nyarko’s (1995) Bayesian learning framework. Assessing learning by doing across fifteen combinations of the number of businesses and the industry distance among founded firms, our findings are bleak. Learning in successive businesses is a high-noise ( w ) and low-signal ( θ ) environment, where the progress ratio, or the ratio of total learning to initial learning, is close to 1. In launching businesses in multiple industries, these learning challenges are slightly higher. Overall, learning by doing is noisy and delivers limited improvements in business duration.
The romanticized view of an entrepreneur is that of a serial entrepreneur. Elon Musk, Steve Jobs, or Richard Branson are among the famous serial entrepreneurs who have started multiple businesses in a variety of industry sectors. Investors are keenly interested in financing serial entrepreneurs to improve the odds of making their ventures successful. Serial entrepreneurs are adept at opportunity recognition (Cope, 2011, Lattacher and Wdowiak, 2020). They benefit from prior business failures and successes (Sarasvathy et al., 2013, Lafuente et al., 2019, Lee et al., 2021), re-entry dynamics (Amaral et al., 2011), prior industry experience (Lahiri & Anu, 2020), variations in learning styles (Eggers and Song, 2015, Lin et al., 2019), and improved performance over time (Sewaid et al., 2021). Dabić et al. (2021) provide an overview of the serial entrepreneurship literature. Despite romanticization by the popular press and investors, academic research on serial entrepreneurs is mixed. A primary metric of success in serial entrepreneurship is business duration – how much longer a serial entrepreneur's business survives than that of a novice entrepreneur (Lafontaine & Shaw, 2016). Studies have highlighted both positives and negatives related to serial entrepreneurship outcomes. On the one hand, serial entrepreneurs achieve higher sales and greater productivity than novice entrepreneurs (Shaw & Sørensen, 2019). Lafontaine and Shaw (2016), investigating retail firms in Texas, find approximately half of business duration is explained by learning. Others have focused on the advantages in income (Chen, 2013) or entrepreneurial earnings 1 (Toft-Kehler et al., 2014) between serial and novice entrepreneurs. On the other hand, Parker (2013) concludes that serial entrepreneurs obtain temporary benefits in future venturing. Focusing on the behavioral aspects of serial entrepreneurs, Eggers and Song (2015) find that serial entrepreneurs who fail in previous ventures change industries. This change proves costly because it limits value creation in the next venture. Perhaps the mixed effects can be explained by the study using employer-employee matched data by Rocha et al. (2015) who found support for higher person-specific effects with serial entrepreneurs.
Grossman et al. (1977) were among the first scholars to highlight the value of the Bayesian approach in modeling firm activities. Bayesian learning in the serial entrepreneurship context has been called on in prior research, from both theoretical (Chen, 2013) and empirical (Parker, 2018) perspectives. We formally apply and empirically test MacMillan’s (1986) initial conceptualization on serial entrepreneurs by developing a Bayesian learning-by-doing model to assess learning across successive businesses at varying industry distances (Jovanovic & Nyarko, 1995). Consistent with past work on venture survival as a viable longer-term outcome of venturing efforts (Soto-Simeone et al., 2020), we use business duration as a measure of learning. Duration is measured by the difference between the opening and closing dates of the establishment.
Extant studies on serial entrepreneurship draw on panel data econometrics or duration models. However, the serial correlation across multiple businesses must be modeled using the learning and forgetting rates of all prior businesses. In the classical work by Jovanovic and Nyarko (1995), the notion of learning by doing focuses on the role of process improvement over time on similar tasks where there is a theoretically best way to complete a task ( θ ), with ongoing variability in completing the task over time ( σ w 2 ). The entrepreneurial activities of identifying opportunities, acquiring resources, and leveraging resources to improve venture performance forms the general core of the process. The notion of learning by doing in the entrepreneurial context is driven by improvements in efficiency or productivity in establishing successive businesses. Without efficiency or productivity to improve the chances of future success, serial entrepreneurship would be a process based largely on luck. Consequently, we aim to assess whether performance improvement in serial entrepreneurship accumulates over successive businesses.
In the current context, Bayesian learning is important because entrepreneurs, in their attempts to establish successive businesses, can seldom predict their ability to be successful or assess the value of their business ideas. Wading through the firm establishment process, entrepreneurs update their beliefs about the business in the context of priors related to previous entrepreneurial experiences. The process of resource combination and orchestration allows serial entrepreneurs to update the prospects for their business and decide whether it is best to pivot, abandon, or retool existing resource combinations. However, the signals derived from experimenting across businesses are also fraught with the noisy environment where errors of omission or errors of commission can lead to false updates of the priors. Due to idiosyncratic and variegated challenges in setting up businesses, whether learning by doing through Bayesian learning is even possible is an equally fundamental question. Therefore, the purpose of this study is two-fold. First, to determine whether serial entrepreneurs benefit from Bayesian learning as they draw on their prior entrepreneurial experiences to update their existing beliefs. It seeks to address long-standing calls to assess Bayesian learning in a serial entrepreneurship context. We fully acknowledge that learning in noisy environments makes the application of Bayesian learning especially challenging. This leads to our second and more important point on whether Bayesian learning is meaningful in the serial entrepreneurship context. Indeed, the mixed evidence on gains from serial entrepreneurship calls for a more critical assessment of the value of Bayesian learning in this context.
Although the notion of learning by doing in serial entrepreneurship is inherently appealing, the analytical model we propose also helps explore whether learning by doing is feasible for serial entrepreneurs. Our skepticism stems from the fact that entrepreneurship is a process riddled with errors and failures, which requires idiosyncratic learning in each new venture, and even in the same industry. If risk, uncertainty, and failure are prevalent in entrepreneurship, the notion of learning across ventures seems less feasible. Indeed, the notion of learning to improve efficiency or productivity in successive businesses is riddled with errors and tentative experiments, which substantially limit the rate of learning. The errors, idiosyncrasies, and failures increase as entrepreneurs venture into distant industries. Our concern is not to do with the learning associated with the procedural elements of establishing a business, but it is rooted in learning by doing where the efficiency of improving duration-based outcomes in successive businesses would appear to be in doubt.
We agnostically approach the research question: What is the nature of learning by doing for serial entrepreneurs? This concerns whether it is possible to run longer surviving firms for serial entrepreneurs. Suppose θ measures entrepreneurial ability in general or success in completing a task. We build on the work of Jovanovic and Nyarko (1995) and provide estimates for the: (i) nature of learning [ σ θ 2 a n d σ w 2 ]; (ii) progress ratio; and (iii) half-life of learning curves for serial entrepreneurs. Here, σ θ 2 denotes the variability from the theoretically ideal way of establishing longer duration businesses ( θ ), and σ w 2 represents the variability based on idiosyncrasies and exceptions in improving business duration ( w ) in successive businesses. A serial entrepreneur learns a better way of approaching θ with each successive business, and w is the noise in learning. Each successive business presents a unique set of challenges and idiosyncrasies ( w ) that hinders the realization of θ . Put differently, θ is the improvement that comes from becoming a serial entrepreneur (ability to successively improve business outcomes) while w represents the noise, inefficiency, and errors in each business that hinder an approach to θ .
If the feasibility of learning by doing is higher in serial entrepreneurship, we expect θ to be high and the variance of w to be relatively small. If the feasibility of learning by doing is lower in serial entrepreneurship, then we expect a lower θ and a higher w . If θ is substantially lower and w is significantly higher, then learning by doing may not be feasible. The progress ratio refers to the ratio of the highest learning achieved by a serial entrepreneur to the initial learning. A lower progress ratio implies less progress in improving productivity, whereas a progress ratio close to 1 implies no learning because the highest improvement is closest to the initial improvement. The half-life refers to the number of businesses required to reach half of the highest improvement achieved by a serial entrepreneur. A half-life ratio of =<1 implies that many learning forms in the first business do not lead to substantive improvements in learning in subsequent businesses.
These estimates using Bayesian methodology represent among the first efforts to formalize learning by doing for serial entrepreneurs. The study contributes to the serial entrepreneurship literature by providing a theoretically grounded approach to learning under uncertainty and risk. Despite prior work by Jovanovic and Nyarko (1995) on the relevance of Bayesian learning in stable contexts, such as surgery and flying planes, we draw on the data of serial entrepreneurs in Texas (Lafontaine & Shaw, 2016) to develop and test a model where the nature of learning, the progress ratio, and the half-life to improvements by serial entrepreneurs is assessed. Our study extends prior studies on non-organizational settings (Edmondson et al., 2001, Rockart and Dutt, 2015, Chen et al., 2018), heterogeneity in learning settings such as production (Thompson, 2001), and transmittal of learning in organizational settings (Hatch and Mowery, 1998, Hendel and Spiegel, 2014). Researchers have called for scholars to focus on entrepreneurial learning (Minniti & Bygrave, 2001) and to improve our understanding of the entrepreneurial learning process (Cope, 2005). Woo et al. (1994) argue for the characterization of entrepreneurship as an ongoing process of experimentation and learning under uncertainty. Minniti and Bygrave (2001, p. 7) argue that a “theory of entrepreneurship requires a theory of learning.” Nonetheless, “in terms of theory building, many aspects of entrepreneurial learning remain poorly understood” (Cope, 2005, p. 373). We address these calls and extend the work on the learning of serial entrepreneurs (Rocha et al., 2015, Lafontaine and Shaw, 2016). From a practical standpoint, though pedagogy and practice of entrepreneurship rest on the assumption that entrepreneurship can be learned, whether learning across successive idiosyncratic experiences is feasible and whether learning by doing is achievable for serial entrepreneurs are important questions.
Theoretical background
Learning by doing is an important consideration in economics and applies to cost, quality, and speed improvements in a variety of settings (Cope and Watts, 2000, Thompson, 2010, Thompson, 2012). The interest in understanding the serial entrepreneurship phenomenon has been perennial ever since the early work by MacMillan (1986) who highlighted the centrality of serial entrepreneurs in seeking to understand entrepreneurship. The premise underlying serial entrepreneurship is that entrepreneurs who
Suppose an individual must make a decision z related to some unknown target y . When z = y then she attains maximum efficiency in the task (or maximum entrepreneurial ability), A . Following Jovanovic and Nyarko (1995), we assume ability or efficiency ( q ) is given by q = A 1 - y - z 2 .
After multiple trials (say T ), the entrepreneur has: y t = θ + w t , t = 1 , ⋯ , T , w t ∼ iid N ( 0 , σ w 2 ) , where θ is a critical parameter – for example, an optimal way of performing tasks, optimal speed, the optimal way of performing
Our task is to estimate the parameters Θ = [ N , π , σ θ , σ w ] from (12). For a single entrepreneur, say i ∈ I = 1 , ⋯ , n , we would like to estimate entrepreneur-specific parameters Θ i = [ N i , π i , σ θ , i , σ w , i , i ∈ I ] where T i stands for “experience” measured by the number of serial enterprises per owner controlling for various characteristics that are, basically, dummy variables. Our performance variable is size S i of each enterprise. Our model is: ln q i ∗ = N i ln 1 - σ θ , i 2 ( 1 + T i π i ) ( 1 - σ w , i 2 ) + z ' i γ + u i , u i ∼ iid N ( 0 , σ u 2 ) , i = 1 , ⋯ , n , where z i ∈
We offer plausible rationales for the lack of support for our findings. Although the conceptualization of serial entrepreneurship is extensively studied, our findings do not meaningfully support the possibility of learning by doing for serial entrepreneurs. The noise is high, signals for learning are low, the progress ratio is negligible, and the half-life of learning is short. These findings can be indirectly supported by the environmental and individual factors that may increase the
Prior evidence on learning in entrepreneurship has been mixed, and our results dampen the need for understanding learning by doing. We found that learning in successive businesses is a high-noise and low-signal environment, and the ratio of total learning to initial learning is not significantly different. The proposed model allows us to assess learning by serial entrepreneurs in improving the duration of their businesses. However, a comprehensive theory of entrepreneurial learning that
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Pankaj C. Patel is a Professor of Management at Villanova University. His research interests are at the intersection of technology and governance. He received his PhD from the University of Louisville.
Venture capitalists and serial entrepreneurs
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In order to face increased environmental dynamism (ED), firms are increasingly called on to leverage deliberate learning processes that make dynamic capabilities emerge in a path-dependent way from the conversion of tacit knowledge to explicit knowledge. Moreover, to mitigate the effect that ED can play in eroding a firm’s capabilities and subsequently its performance, managers need to effectively align short- and long-term strategies, which in the literature have been addressed as ‘capability traps’. Although these two processes are strictly interrelated, to date they have been treated in quite an isolated way and usually through the development of linear approaches. To fill this gap, leveraging the knowledge-based view and dynamic capabilities theories, a conceptual system dynamics model was developed in this study to reconstruct the causal intertwined relationships existing between ED, capabilities development, and a firm’s performance. Moreover, by building a stock and flow diagram and simulating different scenarios, it was found that the most effective way to cope with ED is to dedicate efforts in both knowledge stocks’ development and process improvement. The paper thus offers theoretical contributions to each of these three literature streams (i.e., knowledge-based view, dynamic capabilities, and system dynamics) and provides a framework to guide managers and decision-makers into arranging deliberate organisational learning processes and fostering organisational alignment between short- and long-term policies.
Team faultlines and upward voice in India: The effects of communication and psychological safety
While voice contributes to improved organizational functioning, employee ideas and opinions can be diverse and at cross-purposes. Despite voice pertinence, there is little scholarly work on how group composition influences members’ voice behavior. Drawing on the categorization-elaboration model (CEM) perspective of faultline theory and the value-in-diversity paradigm, we examine the effect of team diversity faultlines on members' upward voice. The research model is tested across two time-lagged studies in India. The results of study 1 reveal that gender diversity strengthens the positive influence of age diversity on team communication. Built on study 1, study 2 demonstrates that team communication mediates the link between team diversity faultlines and upward voice, with psychological safety as a boundary condition. We discuss the findings and implications for theory and practice.
Uncovering sustainability storylines from dairy supply chain discourse
Food supply chains are key to ensuring sufficient nourishment of the population. For instance, taking a circular economy approach can help facilitate the adoption of sustainable practices, such as the use of technology to combat food waste issues. Yet, creating intrinsically sustainable production systems remains complex, given differing stakeholder perspectives, structures and approaches that can be entrenched along supply chains.
Based on an empirical investigation conducted in the UK dairy supply chain, and implementing a critical discourse analysis, this paper aims at gaining an understanding how sustainable storylines are either propagated or supressed by different stakeholders. The paper contributes to the theoretical debate on power in supply chains through the lens of cultural hegemony and the discourse coalition concept, leading to the theoretical contribution of this work: an explanation of how a self-perpetuating cycle of legitimacy helps a storyline become, and remain, dominant.
Uncertainty, entrepreneurial orientation, and the pursuit of M&A: Managing the unpredictable
Uncertainty has long-been the subject of academic discussion and debate regarding organizational pursuits and the topic’s growth in top business and management journals has seen increased substantially in recent years. In this article, we take up prior calls to provide clarity regarding uncertainty measurement in management research by developing and testing a CATA measure of uncertainty. We further clarify and compare the uncertainty-entrepreneurial orientation dynamic to explain why firms engage or focus on M&A activity vis-à-vis their annual reports. We find support for an interaction between Entrepreneurial Orientation (EO) and uncertainty to explain firms’ M&A Discourse. Implications and future research directions are discussed.
Reconciling the social sector with external accountability requirements: Lessons from stewardship theory
Not-for-profit organisations are increasingly being held to account for their social performance. Due to their resource dependency, they generally rely on external financial resources to ensure their missions. This alternative is usually coupled with social accountability requirements from private investors. Given their externally induced nature, social accountability initiatives might result in the implementation of management control systems imported from for-profit organisations without any real consideration – and, consequently, leading to an oversimplification – of the social sector’s characteristics and complexities. Relying on both agency and stewardship theories, we consider how to reconcile external reporting obligations with the characteristics of non-profit organisations. To this end, we use a single qualitative case study of a social organisation responsible for housing services, which recently introduced a reporting system to meet its private investors’ external requirements. We stress the potential risks of implementing an agency theory–based system in a social context and maintain that the expected accountability targets are unlikely to be reached. We also make recommendations to improve the current system by drafting new metrics.
Formal institution deficiencies and informal institution substitution: MNC foreign ownership choice in emerging economy
This study suggests that multinational corporations (MNCs) use foreign ownership choice as a strategic response to formal institutional deficiencies in emerging economy. We argue that although MNCs choose lower ownership level in foreign subsidiaries when economic and political institutional deficiencies are high, informal institutions can substitute for deficient formal institutions. A longitudinal analysis of 9,377 foreign entries in China shows that social institutions serve as substitutes for deficient economic institutions but not for deficient political institutions in shaping foreign ownership choice; and the substitutive effect of social institutions is especially important for foreign subsidiaries with lower levels of slack resources or do not have SOEs as the local partner.
Mike G. Tsionas is Professor of Economics at Lancaster University since 2014. Before that he was professor at the Athens University of Economics and Business. He has served in the past as alternate member of the Euro-group in his capacity as senior adviser for the Greek Ministry of Finance. Mike has published over 150 papers in academic journals such as the Review of Economic Studies, the Journal of the American Statistical Association, the Journal of the Royal Statistical Society, the Journal of Econometrics, the Journal of Applied Econometrics, etc. He is a Fellow of the Journal of Econometrics; a Distinguished Author of the Journal of Applied Econometrics, and he has been awarded the Dean’s Award for Excellence in Research in 2019. Mike has been also invited as a keynote speaker in several international conferences.
Pejvak Oghazi is Professor in Business Studies and head of department at Sodertorn University, School of Social Sciences in Stockholm, Sweden. He is also senior research associate Management at Hanken School of Economics, Finland. Prior to his current position, Professor Oghazi worked as an industrial manager at national and international level. Professor Oghazi’s current research interests revolve around topics in Digitalization, Business models, SCM and Marketing.
Vanessa Izquierdo González is the general director of Sagardoy Business&Law School, the academic partner of Sagardoy Law Firm. For more than 20 years she was the academic director on Centro de Estudios Garrigues. She studied Law at Deusto University, she is master’s in human resources at Garrigues, MBA at IESE and PhD at the Information Sciences Faculty of the Complutense University of Madrid.
The title of the paper is based on the quote “No man ever steps in the same river twice, for it's not the same river and he's not the same man” by Heraclitus.
- DOI: 10.1108/13552551211227693
- Corpus ID: 153216850
Performance drivers of serial entrepreneurs
- F. Kirschenhofer , C. Lechner
- Published 4 May 2012
- International Journal of Entrepreneurial Behaviour & Research
30 Citations
Serial founder entrepreneurs : careers and corridors, venture choices and outcomes.
- S. O'Regan
Serial entrepreneurship, learning by doing and self-selection☆
- Vera Rocha , Anabela Carneiro , C. Varum
The moderating role of previous venture experience on breadth of learning and innovation and the impacts on SME performance
- Kanhaiya K. Sinha , Chad Saunders , S. Raby , J. Dewald
- Business International Journal of Entrepreneurial Behavior & Research
Serial entrepreneurs: A review of literature and guidance for future research
- Marina Dabić , Božidar Vlačić , T. Kiessling , Andrea Caputo , M. Pellegrini
- Business Journal of Small Business Management
The trinity of entrepreneurial team dynamics: cognition, conflicts and cohesion
- Ming-Huei Chen , Y. Chang , Yuan-Chieh Chang
Re-visiting Effectuation: The Relationships with Causation, Entrepreneurial Experience, and Innovativeness
- Virginia Cha , Y. Ruan , M. Frese
Rural entrepreneurs: what are the best indicators of their success?
- Erick Eschker , Gregg J Gold , Michelle D. Lane
Entrepreneurship Dynamics: Entry, Survival and Firm Growth
- Economics, Business
A comparative study of novice and habitual entrepreneur’s choice for founding team member
- Willy Das , Satyasiba Das
- Business Journal of Entrepreneurship in Emerging Economies
Does Managerial Training have any impact on the performance of MSE Managers? Empirical evidence from Ghana
- V. Atiase , David Botchie
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The prevalence of multiple owners and directors in the SME sector: implications for our understanding of start-up and growth
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Seriality in entrepreneurship has been mostly investigated as a matter of occupational choice (Carbonara et al., 2020 ), with studies ranging in their approaches, forming a dichotomy between those arguing for the importance of learning by doing (Rocha et al., 2015) and those countering with the study of the innate abilities of individual serial …
Serial entrepreneurs: A review of literature and guidance for future research September 2021 Journal of Small Business Management DOI: 10.1080/00472778.2021.1969657 License CC BY-NC-ND 4.0...
Massimiliano Pellegrini (2021): Serial entrepreneurs: A review of literature and guidance for future research, Journal of Small Business Management, DOI: 10.1080/00472778.2021.1969657
Serial entrepreneurs: A review of literature and guidance for future research | Semantic Scholar DOI: 10.1080/00472778.2021.1969657 Corpus ID: 244212158 Serial entrepreneurs: A review of literature and guidance for future research Marina Dabić, Božidar Vlačić, +2 authors M. Pellegrini Published 24 September 2021 Business
Our research expands the dynamic entrepreneurship researchfield through a systematic and bibliometric analysis of "serial entrepreneurs." Accordingly, this is one of the first studies to review the serial entrepre- neurship research domain systematically.
Serial entrepreneurs: A review of literature and guidance for future research - The Lincoln Repository Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that asmany as 50% of all entrepreneurs are
Serial entrepreneurs: A review of literature and guidance for future research Journal of Small Business Management . 10.1080/00472778.2021.1969657
Serial entrepreneurs: a review of literature and guidance for future research Dabić, Marina and Vlačić, Božidar and Kiessling, Timothy Scott and Caputo, Andrea and Pellegrini, Massimiliano (2021) Serial entrepreneurs: a review of literature and guidance for future research. Journal of Small Business Management .
We propose a theory of serial entrepreneurship in which an entrepreneur has three occupational choices: maintain his business in operation, shut it down to enter the labor market to earn an exogenous wage, or shut it down to launch a new venture while incurring a serial startup cost.
Little research has been conducted regarding serial entrepreneurship compared to entrepreneurship research more broadly, despite research that suggests that as many as 50% of all entrepreneurs are serial entrepreneurs. ... and provide a review of academic literature for future researchers to utilize. Our perceptual map has identified four key ...
Entrepreneurship research shows that most new ventures fail, yet serial entrepreneurs continually exit previous ventures and start new ones. Our study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis.
This study explores 118 scholarly articles indexed in Web of Science and Scopus databases on serial entrepreneurship through multiple correspondence analysis. Identify key areas for future research, explore and consolidate the theoretical foundations used, and provide a review of academic literature for future researchers to utilize.
After presenting their detailed map of research into serial entrepreneurship, the authors provide future research avenues: (a) identifying the opportunity to diversify theoretical approaches, (b) deepening the study of entrepreneurial strategies, (c) expanding the study of innovation and technology, and finally, (d) the need to shed more light on …
Literature review. Serial entrepreneurs are more likely to run their businesses more successfully. ... In addition, the hierarchical model proposed in this study provides a new research approach and direction for future research on serial entrepreneurial intention. First of all, we need to pay more attention to the research on exit modes ...
The study contributes to the serial entrepreneurship literature by providing a theoretically grounded approach to learning under uncertainty and risk. ... A review of literature and guidance for future research. Journal of Small Business Management ... Mike has published over 150 papers in academic journals such as the Review of Economic ...
It thereby enables future research to position clearly within its realm and extend the more detailed - either qualitative or quantitative - investigation in each of the identified phases of an EF. Finally, the mechanisms behind learning from the failure seem to be well covered in the entrepreneurship literature from different points of view.
Taylor & Francis Online: Peer-reviewed Journals
DOI: 10.1108/13552551211227693 Corpus ID: 153216850; Performance drivers of serial entrepreneurs @article{Kirschenhofer2012PerformanceDO, title={Performance drivers of serial entrepreneurs}, author={Florian Kirschenhofer and Christian Lechner}, journal={International Journal of Entrepreneurial Behaviour \& Research}, year={2012}, volume={18}, pages={305-329} }