The literature considers the big five personality traits and entrepreneurial self-efficacy (ESE) to be important individual-level factors that determine entrepreneurial intention. However, little is known about the profiles of personal characteristics of individuals who express a high level of entrepreneurial intention. The purpose of this paper is to carry out a comparative analysis of personal characteristics that contribute to new business start-up intention.
Using survey data from two samples, fuzzy set qualitative comparative analysis (fsQCA) was performed to extract patterns of personal characteristics (i.e. the big five personality traits and ESE) that impact entrepreneurial intention.
The outcomes of the analyses demonstrate that a high level of entrepreneurial intention can be realized through multiple configurations of the big five personality traits and ESE.
This paper can inform practice on entrepreneurship education. Specifically, the paper includes implications for the development of ESE, and for understanding multiple configurations of personal characteristics that lead to a high level of entrepreneurial intention.
This paper addresses an identified need to understand how personal characteristics operate conjointly and among individuals.
Şahin, F., Karadağ, H. and Tuncer, B. (2019), "Big five personality traits, entrepreneurial self-efficacy and entrepreneurial intention: A configurational approach", International Journal of Entrepreneurial Behavior & Research, Vol. 25 No. 6, pp. 1188-1211. https://doi.org/10.1108/IJEBR-07-2018-0466Download as .RIS
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The belief that entrepreneurs have unique personalities has a long tradition in the field of research in entrepreneurship (Gartner, 1988). However, within the last two decades, the personality discussion in entrepreneurship has re-emerged, criticizing the initial traits studies from many perspectives, including the personality measures not being specifically developed for entrepreneurs (Robinson et al., 1991); trying to measure too many traits, thereby causing weak linkages among constructs; and using insufficient quantitative methods (Baum et al., 2014). In this re-emerging stream, the role of personality attributes in becoming an entrepreneur began to be discussed from a fresh and revived perspective. As a result, a consensus formed among entrepreneurship scholars about the critical role of personality traits in entrepreneurial decisions and actions (Baum and Locke, 2004), supported by the results of several meta-analyses (Collins et al., 2004; Zhao and Seibert, 2006; Rauch and Frese, 2007; Zhao et al., 2010), particularly after the 2000s.
The personality dimension of entrepreneurship and its relationship with entrepreneurial intention can be discussed from a career choice perspective (Bird, 1988; Katz and Gartner, 1988), as choosing a career is intentional (Boyd and Vozikis, 1994). The choice of entrepreneurship as a career is affected by a number of determinants, including self-efficacy (Krueger, 1993; Krueger et al., 2000), education (Shahab et al., 2018), gender (Dawson and Henley, 2012; Mehtap et al., 2017), age (Kenny and Rossiter, 2018) and social context (Schölin et al., 2016; Henley et al., 2017; Bellò et al., 2018). One definition of entrepreneurial intention is “a self-acknowledged conviction by a person that they intend to set up a new business venture and consciously plan to do so at some point in the future” (Thompson, 2009, p. 676). The intention to become an entrepreneur is commonly regarded as the major antecedent of entrepreneurial behavior (Bird, 1988; Krueger et al., 2000), as intentions “are the best predictor of planned behavior, particularly when that behavior is rare, hard to observe, or involves unpredictable time lags” (Krueger et al., 2000, p. 413). The role of personality in entrepreneurship career choice is also being discussed within the attraction-selection-attrition (ASA) framework, as ASA proposes that individuals tend to select work environments with people that share the same personality profiles as theirs (Schneider, 1987). Thus, in this argument, entrepreneurs are regarded as individuals who act to work independently by establishing their ventures.
This study considers the big five personality traits and entrepreneurial self-efficacy (ESE) as determinant factors in an individual’s entrepreneurial intention. The decision to focus on these personal characteristics is dependent on the widespread evidence of the predictive roles of the big five personality traits and ESE in entrepreneurial intention (e.g. Zhao and Seibert, 2006; Zhao et al., 2010). While recent meta-analyses have shown that personality does play an important role in entrepreneurship, previous empirical findings in the investigation of specific personalities in relation to entrepreneurial intention produced mixed results. For example, Brandstätter (2011) summarized the findings of five meta-analyses on personality aspects of entrepreneurship and showed that patterns in meta-analyses are not reflected for each of the big five personality traits. Moreover, the patterns of results from meta-analyses of Zhao and Seibert (2006) and Zhao et al. (2010) overlap but are not completely congruent. In general, it can be concluded that, of the big five personality traits, conscientiousness, openness to experience, emotional stability and extraversion, including ESE, are positively associated with intention to become an entrepreneur. These findings may lead researchers to a definitive understanding of what causes individuals to have a high level of intention to start up a business or be self-employed, but do not help to determine certain profiles of personal characteristics of individuals who express a high level of entrepreneurial intention. Given recent advances in the research methods (Gabriel et al., 2018) and contemporary developments in entrepreneurship research (Kraus et al., 2018), it is timely to move from a variable-oriented approach to a person-oriented approach, which enables researchers to understand how variables operate conjointly and among individuals.
Instead of following a symmetry-based method of data analysis (such as correlations and regressions), a configurational comparative approach was followed in the present study. This configurational analytic approach investigates the interdependencies between several personality traits and their relationships with ESE within a person to lead to a high (or low) level of entrepreneurial intention. This approach allows researchers to gain more insight into theoretical and practical understanding of the personality profiles of individuals who become, or strive to become, successful entrepreneurs. Accordingly, the aim of the current study is to analyze the big five personality traits, or combinations of these traits, that lead to a high (or low) level of entrepreneurial intention. Moreover, the study aims to identify the role of ESE as a relevant condition that combines with the big five personality traits to lead to a high (or low) level of entrepreneurial intention.
In the present study, a fuzzy set qualitative comparative analysis (fsQCA) was used to achieve these goals. In recent years, the configurational comparative approach, specifically the fsQCA, has attracted much attention across diverse disciplines, including the field of business management (e.g. Roig-Tierno et al., 2016) and the field of entrepreneurship (Kraus et al., 2018). Based on set theory and Boolean logic, the fsQCA is generally viewed as a middle ground between quantitative (variable-oriented) and qualitative (case-oriented or person-oriented) methodological approaches (Ragin, 2008). Because the fsQCA allows for equifinality (the possibility of many pathways or combinations of causally relevant conditions linked to the same outcome) and causal asymmetry (the possibility of differences between the pathways to the presence and the absence of the same outcome), this analytic approach has important advantages over traditional approaches (i.e. regression-based approaches) (Woodside, 2013). Rather than considering personal characteristics in isolation, the fsQCA is able to identify how the big five personality traits and ESE combine to achieve a high (or low) level of entrepreneurial intention (Ragin, 2000, 2008; Fiss, 2011; Woodside, 2013).
By using the configurational comparative approach, this study aims to contribute to both theory and practice. Several studies call for more research on person-oriented research (Gabriel et al., 2018) in the field of entrepreneurship (Kraus et al., 2018), as well as the role of personal-level variables in the configuration of entrepreneurial intention (Fayolle and Liñán, 2014). The present study extends trait-oriented research in the field of entrepreneurship to focus on the person as opposed to traits. Doing so offers a fresh perspective on the personality profiles that facilitate entrepreneurial intention and offers novel insight for people making occupational decisions. Moreover, this study could potentially inform practice, particularly on entrepreneurship education. For example, while some traits are difficult to change, previous studies provide examples of interventions that can easily influence self-efficacy (e.g. Rauch, 2014).
The present study is structured as follows. After the introductory section, the literature review and propositions are presented. Subsequently, the methods and findings of the fsQCA are reported. Finally, the theoretical and practical implications of the study’s findings are discussed and recommendations for future studies provided.
The big five personality traits and entrepreneurial intention
Personality traits are “dispositions to exhibit a certain kind of response across various situations” (Rauch and Frese, 2007, p. 355) that are highly stable over time (Baum et al., 2014). Due to the complexity of human personality, a comprehensive model named the Big Five model was developed to explain major personality traits in five broad categories (Goldberg, 1990). After the emergence of the model, widespread support was received for the five factors – conscientiousness, openness to experience, emotional stability, extraversion and agreeableness – proposed by the model, causing the big five be the most widely used reference in personality studies (Gosling et al., 2003; Brandstätter, 2011).
Whether the personality dimensions of the Big Five model have an impact on entrepreneurial intention has been the focus of a number of studies (Zhao and Seibert, 2006; Zhao et al., 2010). Below, a brief literature review is presented to affirm the relevance of the big five personality traits to entrepreneurial intention.
Conscientious individuals tend to be hard-working, well planned and organized, and dependable in fulfilling their responsibilities and duties (Costa and McCrae, 1992; Zhao and Seibert, 2006; Ariani, 2013). Conscientiousness is closely linked with entrepreneurship, as a person who has a high need for achievement and motivation for goal achievement is more likely to become an entrepreneur (McClelland, 1961; Baum and Locke, 2004). Entrepreneurs are individuals who do not like repetitive and routine work, who take personal responsibility and who want to see the concrete results of their decisions (Antoncic et al., 2015). Conscientiousness has been found to be the personality construct demonstrating the largest difference between entrepreneurs and managers (Zhao and Seibert, 2006). The conscientiousness dimension was also found to be one of the two constructs most strongly linked with entrepreneurial intention in the meta-analysis by Zhao et al. (2010). While conscientiousness was not found to differ significantly between entrepreneurs and non-entrepreneurs in a recent study (Antoncic et al., 2015), Wang et al. (2016) identified a significant association between the personality constructs of conscientiousness, openness to experience and extraversion among university students.
Openness to experience
This dimension of Big Five model is defined as an individual’s intellectual curiosity for new concepts, ideas and beliefs, as well as their willingness to try out the new and unprecedented (Zhao and Seibert, 2006; Ariani, 2013). An individual who scores high on openness to experience is expected to have a vivid imagination and be creative, with a unique way of thinking and a desire to explore new ideas (Liang et al., 2013). These attributes are crucial for individuals who plan to establish their own business (Rothmann and Coetzer, 2003). An entrepreneur is an innovative and creative person, according to Schumpeter (1934). Openness to experience is found to be one of the constructs most significantly differing between entrepreneurs and professional career holders, together with extraversion and emotional stability (Chan et al., 2015). Consistent with that finding, Zhao et al. (2010) found in their meta-analysis that openness to experience was the personality trait second most highly associated with the intention of becoming an entrepreneur.
Individuals are regarded as emotionally stable if they are calm or even relaxed under stressful conditions. Having negative emotions like depression, low self-esteem, hostility, anger or fear that lead to high levels of neuroticism are also linked with low emotional stability (Costa and McCrae, 1992). It is generally agreed among scholars and practitioners that in order to establish and manage a new venture, a person has to be high in self-confidence, perseverance and resilience, and able to perform successful stress management under difficult conditions (Baron and Markman, 1999; Zhao and Seibert, 2006). Research studies have produced mixed results on this personality trait. In their empirical study, Antoncic et al. (2015) did not find a significant difference for neuroticism between entrepreneurs and non-entrepreneurs, whereas a positive association was found between emotional stability and the intention to become an entrepreneur in the meta-analysis by Zhao et al. (2010).
Individuals who have high extraversion scores are more likely to be warm, friendly, talkative, sociable, energetic and outgoing, as well demonstrating assertiveness and dominance in social relations. Individuals who possess high social and communication skills frequently demonstrate assertion and persuasion. Entrepreneurs need to build and manage their teams and promote their new venture ideas to employees, investors and customers (Shane, 2003), which is likely to be easier for extraverts than for introverts. Despite that, previous literature on the extraversion trait of entrepreneurs is not conclusive (Zhao and Seibert, 2006; Zhao et al., 2010). While no significant difference was found between entrepreneurs and managers in the meta-analysis by Zhao and Seibert (2006), the meta-analysis by Rauch and Frese (2007) indicated higher extraversion score for entrepreneurs than for managers. In a recent study, entrepreneurs and non-entrepreneurs significantly differed in extraversion and openness to experience traits (Antoncic et al., 2015). The meta-analysis by Zhao et al. (2010) found a positive association between extraversion and entrepreneurial intention.
Individuals who have high levels of agreeableness tend to be trusting, altruistic, caring and forgiving (Zhao and Seibert, 2006). As entrepreneurs might possess cooperativeness, patience and friendliness to a degree, these individuals also need to exert high energy levels and motivation, which can destroy their relationships (Antoncic et al., 2015). While entrepreneurs have to establish trusting relationships with stakeholders and team members (Eisenhardt and Schoonhoven, 1990; Shane and Cable, 2002), they are also responsible for the survival of their business in tough situations, which sometimes requires the entrepreneur to be self-centered or even manipulative (Zhao and Seibert, 2006). Empirical evidence shows that being agreeable is negatively associated with becoming an entrepreneur (Wooten et al., 1999), though the results of the meta-analysis by Zhao et al. (2010) did not indicate a significant correlation between the agreeableness construct of the Big Five model and entrepreneurial intention.
Self-efficacy and entrepreneurial intention
Self-efficacy is “an individual’s belief in one’s capability to organize and execute courses of action required to produce given attainments” (Bandura, 1997, p. 3). According to the propositions of social cognitive theory (Bandura, 1986), the behaviors, action courses and perseverance levels of individuals are linked to high levels of self-efficacy. Individuals with high levels of self-efficacy are found to prefer more challenging tasks and have higher resilience in the face of obstacles (Bandura, 1997). Perceived self-efficacy “is an attribution, one of personal competence and control in a given situation” (Krueger and Brazeal, 1994, p. 94). A person’s self-efficacy determines their persistence and resilience in achieving personal goals (Bandura, 1977), and higher levels of self-efficacy lead to approaching harder tasks with more optimism (Zhao et al., 2005). The self-efficacy construct, studied by various disciplines as the personal perception of having a skills set, is accepted as more important than possessing actual skills in determining an individual’s behavior (Krueger and Dickson, 1994).
In entrepreneurship research, the ESE construct “measures a person’s belief in their ability to successfully launch an entrepreneurial venture” (McGee et al., 2009, p. 965), and requires succeeding in tasks like innovation, marketing, management and finance that are related to the establishment of a new venture (Chen et al., 1998; Hsu et al., 2017). ESE is found to be positively associated with new venture performance, particularly in very young ventures (McGee and Peterson, 2017). Perceived feasibility of entrepreneurial acts is an important antecedent of entrepreneurial intention; thus higher levels of ESE increase the intention to choose entrepreneurship as a career (Krueger, 1993). Launching a new venture is viewed as “an intentional act that involves repeated attempts to exercise control over the process in order to achieve the desired outcome” (Arenius and Minniti, 2005, p. 235). Thus, among other constructs of personality, ESE is theoretically proposed as a major antecedent of the intention to become an entrepreneur (Bird, 1988; Boyd and Vozikis, 1994; Krueger et al., 2000; Laviolette et al., 2012; Crespo et al., 2018; Newman et al., 2018). Self-efficacy beliefs are strongly related to entrepreneurial intention, as “entrepreneurship clearly represents planned, intentional behavior” (Krueger and Brazeal, 1994, p. 93). Judgments about self-efficacy have an influence on behavior and the attainment of goals while strongly affecting entrepreneurial intention and the possibility that entrepreneurial intention will be followed by entrepreneurial actions (McGee et al., 2009). Particularly after the introduction of Ajzen’s (1991) theory of planned behavior, an important line of research emerged to investigate the relationship between ESE and entrepreneurial intention (Kickul et al., 2009; Engle et al., 2010; Piperopoulos and Dimov, 2015; Bellò et al., 2018; Hsu et al., 2018; Laviolette et al., 2012; Schmutzler et al., 2018). Empirical studies indicate a significant positive association between the two constructs (Chen et al., 1998; DeNoble et al., 1999; Arenius and Minniti, 2005; Zhao et al., 2005; Barbosa et al., 2007; Drnovšek et al., 2010; Shahab et al., 2018). Newman et al. (2018) conducted a systematic review of the literature on ESE and concluded that there is a significant positive link between ESE and the entrepreneurial intention of both students and working people.
The research propositions
The literature review shows that personal characteristics play an important role in motivation to be an entrepreneur, and thus a deeper understanding of entrepreneurial intention can be gained by exploring the joint influence of personal characteristics (Taormina and Kin-Mei Lao, 2007). To posit propositions, how these personal characteristics work together based on the configurational comparative approach should be considered. The fsQCA, as a configurational comparative method, assumes the cases (in this study, individuals) to display outcomes as a result of basic characteristics. Those characteristics are referred to as conditions. Based on set theory and Boolean logic, this approach emphasizes the examination of subset relations reflecting the link between a condition and an outcome (Fiss, 2007). Because this study focuses on determining the causal conditions that may combine in configurations to produce an outcome, an implication approach rather than a covariation approach should be followed. While a covariation approach expresses a proposition about the directional (positive or negative) association between an independent variable and a dependent variable, an implication approach connects a condition with an outcome to develop a proposition about the sufficiency and necessity of that condition to achieve the outcome (Thiem et al., 2016).
A condition can be classified as necessary if at all times the outcome is present (or absent), the condition is also present (or absent) (Ragin, 2000) – that is, the presence (or absence) of the outcome implies the presence (or absence) of the condition. In the context of this study, presence of the necessary conditions would mean that a higher level of entrepreneurial intention can only be achieved if a particular condition is present or absent. Nonetheless, the review of literature makes clear that neither theory nor empirical evidence has provided adequate support to conclude that a high or low level of the big five personality traits (conscientiousness, openness to experience, emotional stability, extraversion and agreeableness) or ESE are necessary in order to achieve higher levels of entrepreneurial intention. Hence, the present study makes the following proposition:
None of the big five personality traits (conscientiousness, openness to experience, emotional stability, extraversion and agreeableness) and ESE are necessary conditions to merit a prediction of high levels of entrepreneurial intention.
On the other hand, a condition can be interpreted as sufficient if, anytime the condition is present, the outcome is also present – that is, the condition does not need to be combined with other conditions; the presence of the condition itself implies the presence of the outcome (Ragin, 2006; Fiss, 2007). However, as the review of literature suggests, the big five personality traits and ESE depend on one another. Thus, it is improbable that any of these conditions is able to produce, on its own, a higher level of entrepreneurial intention. The absence of any necessary or sufficient condition to achieve a higher level of entrepreneurial intention shows that the conditions will form multiple configurations. Thus, these arguments suggest that:
The big five personality traits and ESE, as causal conditions, form multiple configurations that are sufficient to predict a high (or a low) level of entrepreneurial intention.
Method and data
This study uses data from a student sample and an employee sample. The student sample consists of 253 senior university business students from a state university in Turkey. Because university students are about to make a professional career choice and are empirically the ones in the population with the highest entrepreneurial inclination (Liñán et al., 2011), they are considered suitable for the study of entrepreneurial intention (De Clercq et al., 2013). The purpose of this study was briefly described to the potential participants when they attended their classes. Students participated in the present study voluntarily and no extra credit was given to them. In September 2017, 550 surveys were distributed to students studying business administration. A total of 253 questionnaires were usable, resulting in a response rate of 46 percent. Of the 253 university students, 44.7 percent were female and 55.3 percent male. The mean age of the participants was 21.68 years (SD = 1.79).
The second data source, collected between September 2017 and May 2018, comprised the adult workforce in Turkey. Participants were sought who worked in corporate enterprises and who had not yet made a conscious decision to become a manager–owner of a new venture. Studying such employees is particularly relevant as they do not have a commitment toward a certain career and, thus, might be interested in starting a new venture or becoming self-employed (Mueller and Thomas, 2000). A total of 1,500 potential respondents were selected randomly in a representative range of sectors and regions. The questionnaires were distributed at the workplace; individuals received these materials in person at work and directly returned them to the researchers. The aims of the study were explained in a covering letter, where the anonymity of responses was promised. The participants joined the study voluntarily. In total, 290 completed questionnaires were returned by employees. Because of missing data or incorrect marking, eight questionnaires were excluded from the analysis. Finally, 282 usable questionnaires were obtained, yielding an 18.8 percent response rate. Of the 282 participants, 59 percent were male and 41 percent female. The average age was 31.54 (SD=8.76) and mean organizational tenure was 5.63 years (SD=4.92). Regarding educational background, 59 percent had tertiary education and higher.
Measurement of variables
In the present study, the scale developed by Thompson (2009) was used for assessment of entrepreneurial intention. This scale included items related to intention or plans to establish a business, learn about starting a firm, look for business opportunities, and find initial resources to start a firm. Respondents were asked to rate the extent to which each statement was true about themselves based on a seven-point scale, anchored by 1 (untrue) to 7 (very true). A sample item was “Intend to set up a company in the future.” Cronbach’s α was 0.747 for the student sample and 0.762 for the employee sample.
The Ten Item Personality Inventory (TIPI) by Gosling et al. (2003) was used to assess the big five personality traits – agreeableness, conscientiousness, extraversion, openness to experience and emotional stability. This inventory is a brief measure of the big five personality dimensions – two items for each of the five dimensions. Participants responded to the TIPI using a seven-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Example items in the TIPI include “I see myself as extraverted, enthusiastic” (extraversion) and “I see myself as dependable, self-disciplined” (conscientiousness). The reliabilities (Cronbach’s α) of these scales ranged from 0.694 to 0.767 for the student sample and from 0.673 to 0.731 for the employee sample.
In the literature, various measures are employed in the assessment of ESE (Schjoedt and Craig, 2017). In our study, ESE was assessed using the scale developed by Kristiansen and Indarti (2004). This scale is a unidimensional measure of ESE which includes two items. Example items in the scale include “I have leadership skills that are needed to be an entrepreneur” and “I have the mental maturity to start to be an entrepreneur.” Responses from participants were recorded on a seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). The scale had a good internal consistency, with a Cronbach’s α of 0.757 for the student sample and 0.803 for the employee sample.
Fuzzy set qualitative comparative analysis (fsQCA)
In the present study, the fsQCA was utilized for the analysis of the data. The fsQCA is an example of a set theoretic approach (e.g. Ragin, 1987, 2000). In this method, cases are conceptualized as combinations of specific characteristics, and combinations of characteristics which are linked to an outcome of interest are examined (Ragin, 1987, 2000; Fiss, 2011). The fsQCA has been used increasingly, particularly in the field of business research (SenyKan et al., 2015) and in entrepreneurship research (Kraus et al., 2018). It is a method that manages to a certain extent to integrate some advantages of quantitative (variable-oriented) and qualitative (case-oriented) approaches (Ragin, 1987). As a set theoretical and qualitative method, it is based on a configurational understanding of causation (Ragin, 2000). It conceptualizes cases as combinations of attributes manifested by their set memberships and analyses how different attributes combine into configurations to generate an outcome (Fiss, 2007; Woodside, 2013).
Although there have been a variety of methods that can analyze the configuration concept, including regression analysis, cluster analysis, ideal-type profile deviation and the qualitative method, among others, the fsQCA presents several comparable advantages (Fiss, 2007). First, the fsQCA accounts for equifinality, allowing many pathways to the same outcome of interest. Second, the fsQCA explores the relationships between various configurations composed of different explanatory characteristics and the outcome of interest. Third, the fsQCA emphasizes causal asymmetry (Ragin, 1987, 2000, 2008), as “different paths can lead to the equifinal outcome, which is not necessarily the same configurations explaining the non-outcome” (Berger, 2016, p. 288). Moreover, this method distinguishes itself from traditional methods by stressing complex casualty and nonlinearity (Schneider and Wagemann, 2012).
Fuzzy sets and calibration
In the present study, the fsQCA version 3.0 for Windows was used to test the research propositions (Ragin and Davey, 2016). As the first step in the fsQCA, data relating to the outcome and antecedent conditions which are to be used in the analysis are calibrated. In general, calibrating original measures into fuzzy sets is done based on anchor points external to the data set or case knowledge to categorize meaningful groupings of cases (Ragin, 2008).
In calibrating measures and translating them into fuzzy set membership scores, an indirect or a direct method can be employed (Ragin, 2008). In the indirect method, the researcher assigns the cases into groups with respect to their degree of membership in the target set. On the other hand, in the direct method, three qualitative anchors that structure the degree of membership to a set under study are utilized (Ragin, 2000). Accordingly, “calibrated measure refers to specify qualitative thresholds for full membership (1), full non-membership (0) and crossover values (0.5) in order to assign the degree of membership to which different cases belong to the focus set” (Ragin, 2008, p. 72). In the study, the direct calibration method is used (Ragin, 2000) and the procedure suggested by Woodside (2013) is followed to determine three qualitative anchors. Anchors near to the 95th, 50th and 5th percentiles were taken as thresholds for, respectively, full membership (coded as 1), crossover point (coded as 0.5) and non-membership (coded as 0). The statistics and the fuzzy set calibration rules are presented in Table I.
Analysis of necessary conditions
The fsQCA analysis usually starts with a necessity analysis to identify those conditions that have a necessary relationship with the outcome of interest (Ragin, 2006; Fiss, 2007; Schneider and Wagemann, 2012).
Consistency and coverage measures are central in evaluating the degree of fitness of the cases in a data set to an association of necessity or sufficiency. Consistency evaluates the extent to which the same outcome is produced by cases with the same attributes (i.e. combinations of conditions) within a given data set. Coverage is a measure that indicates “the degree of relevance of certain causes or causal combinations to explain the outcome of interest” (Ragin, 2006, 2008). For a condition to be considered as necessary, the consistency and coverage thresholds should be over 0.90 and 0.80, respectively (Schneider and Wagemann, 2012).
Table II indicates the results of the necessity analysis for a high level of entrepreneurial intention in both student and employee samples, as well as for its absence (low level of entrepreneurial intention). Considering the student sample, the consistency scores range between 0.50 and 0.77 and the coverage scores range between 0.53 and 0.74 (for presence or absence). With regard to the employee sample, the consistency scores range between 0.50 and 0.77 and the coverage scores range between 0.53 and 0.74 (for presence or absence). As shown in Table II, none of the six conditions passes these thresholds; therefore, these conditions are necessary neither for causing individuals to have a high level of entrepreneurial intention nor for individuals to have a low level of entrepreneurial intention. These findings, therefore, lend support to the first proposition.
Analysis of sufficient conditions
After analyzing the necessary conditions, the next action is to identify the conditions that have a sufficient relationship with the outcome of interest. The sufficiency analysis shows several configurations of conditions that are sufficient to produce a high level of entrepreneurial intention, which provides a shred of evidence for the second proposition. The analysis of the sufficient conditions involves three main steps (Ragin, 2000, 2006, 2008; Fiss, 2011).
The first step is the construction of a fuzzy set truth table. The truth table makes it easier to understand the relationships between combinations of potentially causal conditions and the outcome of interest. Based on the calibrated scores, observations are assigned to specific configurations in the truth table. The rows of the truth table represent particular combinations of conditions and the full table, thus, represents any possible configuration (=2Number of characteristics) and their corresponding number of observations (0, 1, 2 …). In the second step, a rule should be developed to reduce the truth table to meaningful configurations. Following the suggestions in previous studies (e.g. Ragin, 2008; Fiss, 2011), in the present study, a frequency threshold of three for the number of observations was chosen and the lowest acceptable consistency score was set at 0.80. Thus, configurations with three or more observations and exceeding the minimum consistency threshold of 0.80 are regarded as sufficient for the outcome. In the present study, 77 cases in the student sample and 119 cases in the employee sample fell into configurations, exceeding the minimum solution frequency and the lowest acceptable consistency score of 0.80. In the final step, the Quine–McCluskey algorithm based on Boolean algebra is used to minimize the truth table logically. Because the algorithm is based on a counterfactual analysis of causal conditions, it is also possible to categorize causal conditions into core and peripheral causes (Ragin, 2008; Fiss, 2011; Schneider and Wagemann, 2012). The fsQCA produces outcomes that include complex, parsimonious and intermediate solutions (Ragin, 2008; Rihoux and Ragin, 2009). A complex solution does not contain any counterfactual cases in the minimization process; a parsimonious solution permits the use of any remainder in the minimization process, apart from their plausibility; an intermediate solution uses only the remainders that survive counterfactual analysis (Ragin, 2000, 2006, 2008; Fiss, 2011).
Table III indicates the results of fuzzy set analysis of the outcome entrepreneurial intention in both samples, following the notation prescribed in Ragin and Fiss (2008). In the table, full black circles (●) represent the presence of a causal condition, while circles with a cross-out (⊗) indicate the absence of a causal condition. Blank spaces in a solution represent a “do not care” situation – that is, a particular condition is not important for the configuration, and the presence (or absence) of this condition does not remove the configuration from the top quartile. Large circles in a solution represent core conditions (i.e. conditions which are included in both parsimonious and intermediate solutions and which show a strong causal relationship with the outcome of interest), while small circles represent the peripheral conditions (i.e. conditions which are only included in the intermediate solution and which show a weaker causal relationship with the outcome of interest) (Fiss, 2011).
The solutions were first assessed by analyzing the overall solution consistency and solution coverage measures reported in Table III, which also shows the intermediate solutions for both the student and the employee sample. Consistency indicates the proportion of a given condition covered by the outcome, whereas solution consistency measures the degree to which cases in the study share the configurations included in each solution to achieve a specific outcome (Schneider and Wagemann, 2012). On the other hand, the solution coverage indicates the combined coverage of all solutions – that is, how much of the outcome is covered by all configurations (Ragin, 2006, 2008; Rihoux and Ragin, 2009). The intermediate solution for the student sample has a consistency of 0.81 and coverage of 0.66, whereas the solution for the employee sample has a consistency of 0.83 and coverage of 0.74. The consistency and coverage of these solutions are greater than the suggested threshold values (0.74 for consistency and 0.25 for coverage); therefore, these solutions provide information on the empirical relevance of the conditions in view (Ragin, 2008; Woodside, 2013).
The results indicate five solutions (1Sa–4S) that represent clearly understandable paths leading to a high level of entrepreneurial intention for students, and four configurations (1Ea–3E) for employees. The consistency levels of all solutions are above the threshold of 0.80 suggested by Ragin (2008). The results also show the presence of both core and peripheral conditions that are included in several configurations. According to the raw coverage, solutions account for between 22 and 45 percent of cases associated with the outcome in the student sample, and between 28 and 44 percent of cases associated with the outcome in the employee sample. The raw coverage measures the proportion of memberships in the outcome explained by each term of the solution, while the unique coverage quantifies the proportion explained by only one solution (Ragin, 2006). Because the unique coverage of each solution is greater than zero, each solution contributes to the explanation of the outcome. With regard to core conditions, ESE and openness to experience are crucial. Three (1Ea, 1Eb and 2E) of the four solutions leading employees to have a high level of entrepreneurial intention include ESE and/or openness to experience as core conditions, while three (1Sa, 1Sb and 1Sc) of the five solutions leading students to have a high level of entrepreneurial intention include openness to experience and/or ESE as core conditions.
There are several equifinal solutions that lead to a high level of entrepreneurial intention for students and employees. However, none of the solutions is similar in achieving a high level of entrepreneurial intention for students and employees. Some of the solutions should be highlighted. Regarding the student sample, solutions 1Sa (OPEN * consc * ~extra) and 1Sb indicate that openness to experience as a core condition, combining with conscientiousness and the absence of extraversion as peripheral conditions, is sufficient for achieving a high level of entrepreneurial intention. Specifically, solution 1Sb (OPEN * consc * ese * ~extra) explains the larger number of cases with a high level of entrepreneurial intention for students (raw coverage 0.45). In other words, the more relevant of the five empirically is the solution 1Sb as it can explain high level of entrepreneurial intention in the student sample, and thus show high raw coverage level. This solution indicates that openness to experience as core condition, combining with conscientiousness and ESE along with the absence of extraversion as peripheral conditions, is sufficient to lead students to have a high level of entrepreneurial intention. Solution 2S (OPEN * ESE * ~extra * ~agree * ~estab) indicates that openness to experience and ESE as core conditions, combining with the absence of emotional stability, agreeableness and extraversion as peripheral conditions, is sufficient to lead students to have a high level of entrepreneurial intention. Solution 3S (ese * extra * agree * consc * estab) indicates that high levels of ESE, extraversion, agreeableness, conscientiousness and emotional stability lead to higher entrepreneurial intention regardless of whether openness to experience is high or low. Finally, solution 4S (agree * estab * ~ese * ~extra * ~consc * ~open) indicates that, in spite of the absence of other personal characteristics, agreeableness and emotional stability as casual conditions are sufficient to lead students to have a high level of entrepreneurial intention.
Regarding the employee sample, solutions 1Ea (OPEN * ESE * extra * ~estab) and 1Eb (OPEN * ESE * consc * ~agree) indicate a path to a high level of entrepreneurial intention, combining ESE and openness to experience as core conditions. These solutions furthermore suggest that, with ESE and openness to experience as core conditions, there are trade-offs between a high level of extraversion and a high level of conscientiousness, as well as between a low level of agreeableness and a low level of emotional stability. Specifically, solution 2E (ESE * consc * open * ~agree * ~estab) explains the larger number of cases with a high level of entrepreneurial intention for employees (raw coverage 0.44). In other words, the more relevant of the four empirically is the solution 2E as it can explain high level of entrepreneurial intention in the employee sample, and thus show high raw coverage level. This solution indicates that ESE as a core condition, combining with conscientiousness and openness to experience along with the absence of agreeableness and emotional stability as peripheral conditions, is sufficient to lead employees to have a high level of entrepreneurial intention. Finally, solution 3E (ese * agree * consc) indicates that ESE, agreeableness and conscientiousness are sufficient conditions to lead employees to have a high level of entrepreneurial intention.
As a result, these findings support the second proposition of the present study.
Figure 1 presents the XY plots displaying all solutions from Table III. If there is a symmetrical relationship between the causal condition (X axis) and the outcome (Y axis), then observations would cluster around the diagonal (Woodside, 2013). However, for both samples, the figures indicate that the majority of observations are located in the upper triangular part of the plot, which implies that there is no necessary causal condition for entrepreneurial intention, but that rather several configurations lead to this outcome. On the other hand, a few observations located in the lower triangular part indicate the sufficiency of the identified solutions.
Configurations for low level of entrepreneurial intention
Considering causal asymmetry, solutions leading to the presence of an outcome (a high level of entrepreneurial intention) may differ from those solutions leading to the absence of an outcome (a low level of entrepreneurial intention). Therefore, to examine what causal conditions lead to the absence of a high level of entrepreneurial intention, the fsQCA analysis with no high level of entrepreneurial intention was conducted by simply coding the negation of the outcome measure. Applying the same rule (consistency: 0.80; frequency threshold: 3), the results of the fsQCA indicate that there was no constant identifiable solution for a low level of entrepreneurial intention. As the consistency and coverage of these solutions are smaller than the suggested threshold values, these solutions are not informative. In sum, there are many but inconsistent paths leading to a low level of entrepreneurial intention.
Several robustness checks and sensitivity analyses were conducted to examine whether the solutions are robust to the use of alternative thresholds for consistency and the frequency of cases per configuration (Schneider and Wagemann, 2012).
First, the lowest acceptable consistency score of 0.80 was changed to 0.85, while retaining the frequency threshold of three for the number of observations. In the student sample, 63 cases fell into configurations, and 70 cases in the employee sample, exceeding the minimum solution frequency and the consistency score of 0.85. For both samples, fewer solutions emerged for the presence of high level of entrepreneurial intention (i.e. three instead of four solutions for the student sample); however, minor changes were observed. Specifically, ESE and openness to experience were the core conditions in the solutions, and interpretation of the results remains largely the same for both samples. Second, the frequency threshold of three for the number of observations per configuration was changed to five, while retaining the consistency score of 0.80. Setting the frequency threshold to five ensured that 47 cases in the student sample and 81 cases in the employee sample were captured for the analysis. Similar to the first sensitivity analysis, fewer solutions emerged and minor changes were observed.
Regarding the low level of entrepreneurial intention, the results of the two sensitivity analyses were similar to those reported before. There was no distinguishable solution for a low level of entrepreneurial intention for either sample. In summary, these sensitivity analyses yielded essentially similar configurations, thus adding robustness to the initial results of the present study.
Discussion and conclusions
Among all research efforts that aim to explain the reason that some individuals have a higher level of entrepreneurial intention than others, the career choice approach (Holland, 1985; Bird, 1988; Katz and Gartner, 1988) purports that individuals are attracted to careers that match their personality traits. Deciding to become an entrepreneur is similar to making a career choice to engage in entrepreneurial activities. Not surprisingly, many studies of the relationship between personal level variables and entrepreneurial intention focus on individuals’ personality and psychology factors (e.g. Liñán and Fayolle, 2015). However, several inconsistencies are noted in the results of previous studies (e.g. Brandstätter, 2011), probably pointing to a lack of complex causal interdependencies between researched factors (e.g. Gabriel et al., 2018). In the present study, the fsQCA was applied to two samples to further the understanding of causal complexity underlying entrepreneurial intention by examining how personality traits and ESE combine to define the level of entrepreneurial intention in each sample.
The results of the present study suggest that an individual who has a higher level of entrepreneurial intention merits the analysis of complex patterns. The combination of the big five personality traits and ESE explains 66 percent of the outcome in the student sample and 74 percent of the outcome in the employee sample. The fsQCA approach enables the examination of context dependencies between conditions and indications of the different paths leading to high levels of entrepreneurial intention as an outcome instead of evaluating the positive or negative effects of variables. More specifically, the results suggest that a complex solution of five different configurations is sufficient to predict a high level of entrepreneurial intention in the student sample, while there are four configurations that are sufficient to predict a high level of entrepreneurial intention in the employee sample. In each sample, all configurations equifinally lead to a high level of entrepreneurial intention, yet differ in coverage, which hints at empirical relevance.
The results of the present study suggest that a unique set of personal characteristics does not exist, much less significant differences in the intention of starting a new business venture. These results may address at least some of Gartner’s (1988) argument, which indicates that the heterogeneous nature of entrepreneurs makes it difficult to identify a personality profile. Nevertheless, a recent literature review on the personality traits of entrepreneurs indicates common results as well as the heterogeneous nature of entrepreneurship (Kerr et al., 2017). Despite many configurations explaining high levels of entrepreneurial intention in both samples, they can be narrowed down to a main path, characterized by high ESE and openness to experience. Most of the configurations share ESE and openness to experience as the core conditions and only differ in peripheral or contributing conditions. This result is consistent with previous studies which found that entrepreneurial intention is positively related to openness to experience and ESE (Zhao et al., 2005; Zhao et al., 2010; Miao et al., 2016).
A striking finding in the current study is that ESE dominates all the configurations that lead to a high level of entrepreneurial intention among employees when compared to students. Although the literature clearly indicates that ESE is an important antecedent of intention of both students and working adults to start a new business (Newman et al., 2018), it seems that the link between ESE and entrepreneurial intention is stronger for working adults than for students. Probably, compared to students, working adults have more opportunities to have work experiences (Farashah, 2015), and to observe and to adopt entrepreneurial role models (BarNir et al., 2011), which affects their ESE. This, in turn, contributes to the intention to start a new business. Surely, future research should more fully investigate this relationship in both the student sample and the working adults sample.
In a few cases, results of individual characteristics (namely, agreeableness and emotional stability) went in an unpredicted direction (present vs absent) in distinct configurations. This implies that the fsQCA is able to identify conditions that may have opposite causal effects depending on their combinations with other conditions in which they are situated (Schneider and Wagemann, 2012). In contrast, traditional statistical methods (e.g. regression and structural equation modeling) are usually based upon the predominant linear paradigm and symmetrical interrelationships between the variables of interest and, thus, present uniform causal effects for each variable (e.g. Woodside, 2013). Moreover, these findings can explain the ambiguous or conflicting results from earlier studies on the topic (e.g. Zhao et al., 2010; Brandstätter, 2011; Hussein and Aziz, 2017). For example, the agreeableness condition integrates two configurations that lead to a high level of entrepreneurial intention for students and one configuration that leads to a high level of entrepreneurial intention for employees. In three of the configurations, the low level of agreeableness combines with other conditions to achieve a high level of entrepreneurial intention for students or employees, in line with the findings of previous studies (e.g. Schmitt-Rodermund, 2004; Zhao et al., 2010). Certainly, configurations including a high level of agreeableness contradict the general expectation of the entrepreneurial personality (low score on agreeableness). On the other hand, it is worth emphasizing that agreeableness was found to be one of the significant big five predictors of entrepreneurial success in a study by Leutner et al. (2014). Regarding emotional stability, the results are similar. There are two configurations in the student sample which include a high level of emotional stability. This confirms the findings of several studies (e.g. Zhao et al., 2010). On the other hand, one configuration in the student sample and two configurations in the employee sample include a low level of emotional stability that combines with the other conditions to achieve a high level of entrepreneurial intention. These results are in agreement with several studies which show no significant differences for a low level of emotional stability (high in neuroticism) between entrepreneurs and non-entrepreneurs (e.g. Antoncic et al., 2015).
In summary, a configurational way of thinking avoids the issues inherent in a one-size-fits-all approach and suggests that focusing on the joint and interdependent effects of various individual predictors is particularly conducive to understanding the development of entrepreneurial intention. Despite the presence of the configurational way of thinking in entrepreneurship literature (Kraus et al., 2018), to the best of the authors’ knowledge, the present research is the first to provide a more holistic understanding of individuals with a high level of entrepreneurial intention, exploring them as being characterized by heterogeneous configurations formed by individual characteristics.
Implications for theory
The findings of the present study contribute to the entrepreneurial intention literature in several ways. First, research focused on intention examines the predictors of intention in a uniform, linear and additive way. Adoption of a configurational approach and the application of the fsQCA advance the comprehension of the individual characteristics that lead to a high or low level of entrepreneurial intention. The results point toward a way to interpret and understand some of the inconsistent results found in previous studies regarding the impact of personality traits on entrepreneurial intention (e.g. Wooten et al., 1999; Envick and Langford, 2000; Brandstätter, 2011). The fsQCA approach enables researchers to examine interdependencies among individual characteristics and, in sum, to indicate the different configurations for a high level of entrepreneurial intention as an outcome rather than assessing the positive or negative effects of the predictors (Schneider and Wagemann, 2012; Woodside, 2013).
Second, the results add to the discussion regarding the most important personality traits for entrepreneurial intention. Of the big five personality traits, conscientiousness (e.g. Collins et al., 2004; Zhao and Seibert, 2006; Stewart and Roth, 2007; Zhao et al., 2010) and openness to experience (e.g. Zhao and Seibert, 2006; Zhao et al., 2010; Chan et al., 2015) have been found to be most highly associated with the intention to become an entrepreneur. In a similar vein, ESE has been proposed as a major antecedent of the intention to become an entrepreneur (e.g. Bird, 1988; Boyd and Vozikis, 1994; Krueger et al., 2000). The results of this study indicate that many of the configurations include ESE and openness to experience as core conditions. According to the configurational approach, a core condition is an essential element that has a strong causal relationship with the outcome of interest (Fiss, 2011). As a consequence, the results of the present study provide support for the understanding of critical individual characteristics, leading to higher intention to become an entrepreneur.
With regard to the field of entrepreneurship research, the present study aims to contribute to increased recognition of the fsQCA as a methodological tool. Kraus et al. (2018) conducted a systematic literature review on the application of fsQCA in the field of entrepreneurship. The present study contributes further by using this methodology in the examination of the entrepreneurial intention construct, which is a rapidly evolving field of entrepreneurship research (Liñán and Fayolle, 2015), and indicates how the fsQCA is useful methodologically for exploring how different personal attributes combine into configurations to achieve a high level of entrepreneurial intention (Fiss, 2007; Woodside, 2013).
Implications for practice
This study indicates the existence of complex configurations of individual attributes which suffice to predict a high level of entrepreneurial intention, and has several implications for both those individuals who intend to establish a business and entrepreneurship research. For example, an individual may assess his or her personality traits and make use of that information to figure out the desirability of his or her career choice (Rauch and Frese, 2007). Most of the tests in the area of personality measurement and assessment are in fact self-report scales or reports of others’ rating scales. When performing a personality assessment in the future studies, students may be evaluated their peers and similarly employees may be evaluated by their co-workers on the big five personality dimensions. The objective personality measurement based on both self-report and other ratings (e.g. peer, best friends and colleague) provides comprehensive information to make more cautious occupational and/or strategic decisions.
Our fsQCA findings highlight the importance of multiple equifinal routes to high level of entrepreneurial intention. These routes demonstrate the different personal characteristics for the intention of starting a new business venture based on a configurational approach. These results are particularly relevant to educators and policymakers, alongside other actors in the entrepreneurship ecosystem.
For the working adults sample, the findings suggest that high level of entrepreneurial intention can be achieved through different configurations which most of them share ESE and openness to experience as the core conditions and other personality characteristics as peripheral conditions. These results can be useful to inform practice and guide educators and policymakers. Literature indicates that individuals can attain higher levels of confidence through work experience, as having a professional experience enables them to gain entrepreneurial and managerial skills, which in turn increase their self-efficacy (Lee et al., 2011; Kolvereid, 1996). On the other hand, literature shows that training in psychological factors (i.e. achievement motivation, personal initiative) positively contributed to business growth while training in business management skills (i.e. entrepreneurship development, business start-up education, business plan development) positively affected start-up a new business venture (Glaub and Frese, 2011). Thus, for working adults, it seems that training efforts should focus on the business management skills as it is more important for establishing a new business venture.
For the student sample, the results suggest that a complex solution of five different configurations is sufficient to predict a high level of entrepreneurial intention. Yet, compared to the working adults, it seems that the link between ESE and entrepreneurial intention is weaker for students. These findings may have some important implications for entrepreneurship education on the students. Since the students are at the verge of making a career choice, the focus of educators and policymakers may remain to be mainly on increasing entrepreneurial attitudes or culture (Mwasalwiba, 2010). Previous studies indicated the effectiveness of entrepreneurship education in strengthening ESE (Cox et al., 2002; Wilson et al., 2007; Nowiński et al., 2017). Through entrepreneurship education and training activities that aim to increase the ESE levels of these individuals, their intention to establish their own businesses and to perform entrepreneurial behaviors might be developed (Liñán, 2004). However, not only should the traditional methods (i.e. lectures, case studies, group discussions) be used for entrepreneurship education, but also using some kind of apprenticeship builds both real world knowledge and skills and also triggers attitudes toward entrepreneurship or entrepreneurial intention (Aronsson, 2004).
Limitations and future directions
The present study has several limitations which have to be pointed out. First, this study is a cross-sectional study which measures the conditions and outcomes at the same time using self-report measures. Because self-report measures are susceptible to several biases, great care was put into the conducting the study and collecting data (Podsakoff et al., 2003). As mentioned above, objective measurement of the study constructs based on both self-report and other ratings will provide comprehensive information which may lead to improvement in the precision of fuzzy sets calibration. Future research can focus on measurement-related issues, perhaps proposing other, more objective measurement instruments for the conditions and the outcome.
Second, the big five personality traits are used as causal conditions that combine with ESE to achieve a high level of entrepreneurial intention. Because the main concern in this study was to explore the combinations of the big five personality traits and ESE that lead to high (or low) entrepreneurial intention, other possible causal conditions that predict the outcome were not tested. For example, specific attributes such as locus of control (Rotter, 1954) and need for achievement (McClelland, 1985) are useful in predicting entrepreneurial inclination (e.g. Rauch and Frese, 2007; Stewart and Roth, 2007). Nonetheless, adding many conditions in the fsQCA generate very complex results that can be hard to explain (Wagemann and Schneider, 2010). Future research could focus on testing other causal conditions that are integral to their studies.
This study offers a holistic picture of how the personalities of individuals impact their intention to start new businesses. The results derived from the use of the fsQCA may enable researchers in the field of entrepreneurship to rethink the personal characteristics of individuals in trying to understand their entrepreneurial intention. More specifically, this study provided empirical evidence that shows that there are various configurations, incorporating several personal characteristics, equifinally leading to a high level of entrepreneurial intention. This study not only provides empirical and methodological implications for researchers in the area of entrepreneurship, but is also of practical relevance to educators and policymakers.
Descriptive statistics and calibration values for conditions and outcome variables
|Descriptive statistics||Calibration criteria|
|Entrepreneurial intention – EI||22.85||8.79||6||42||38||23||6|
|Entrepreneurial self-efficacy – ESE||10.51||2.86||2||14||14||11||5|
|Extraversion – EXTRA||9.60||3.08||2||14||14||9.76||4|
|Agreeableness – AGREE||6.44||2.48||2||14||11||7||2|
|Conscientiousness – CONSC||10.37||2.66||2||14||14||10||6|
|Emotional stability – ESTAB||7.40||2.76||2||14||13||8||3|
|Openness to experiences – OPEN||10.79||2.59||3||14||14||11||6|
Analysis of the necessary conditions
|Student sample||Employee sample|
Note: The tilde symbol (~) indicates the absence of a condition
Configurations for having high level of entrepreneurial intention
|Student sample||Employee sample|
|Entrepreneurial self-efficacy – ESE||⦁||●||⦁||⊗||●||●||●||⦁|
|Extraversion – EXTRA||⊗||⊗||⊗||⦁||⊗||⦁|
|Agreeableness – AGREE||⊗||⦁||⦁||⊗||⊗||⦁|
|Conscientiousness – CONSC||⦁||⦁||⦁||⊗||⦁||⦁||⦁|
|Emotional stability – ESTAB||⊗||⦁||⦁||⊗||⊗|
|Openness to experiences – OPEN||●||●||●||⊗||●||●||⦁|
Notes: Black circles indicate the presence of a causal condition. Circles with “×” indicate its absence. Blank spaces indicate “don’t care.” Large circles indicate core conditions and small circles peripheral ones
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