《管理学原理》课程教学资源（文献资料）The impact of founder personalities on startup success.pdf

1 Vol.:(0123456789) Scientifc Reports | (2023) 13:17200 | https://doi.org/10.1038/s41598-023-41980-y www.nature.com/scientificreports The impact of founder personalities on startup success Paul X. McCarthy1,2, XianGong3 , Fabian Braesemann4,5*, Fabian Stephany4,5, Marian‑Andrei Rizoiu3 & Margaret L. Kern6 Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several frm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the frm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The efects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a signifcant feature of a frm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We fnd that the Big Five personality traits of startup founders across 30 dimensions signifcantly difer from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not fnd one ’Founder-type’ personality; instead, six diferent personality types appear. Our results also demonstrate the benefts of larger, personalitydiverse teams in startups, which show an increased likelihood of success. The fndings emphasise the role of the diversity of personality types as a novel dimension of team diversity that infuences performance and success. Te success of startups is vital to economic growth and renewal, with a small number of young, high-growth frms creating a disproportionately large share of all new jobs1, 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges. As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s frst moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a signifcant and growing municipal debt. Ten in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfzer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives3 . Te economic beneft to Europe and, in particular, the German city where the vaccine was developed has been signifcant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups4 . While stories such as the success of BioNTech are ofen retold and remembered, their success is the exception rather than the rule. Te overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later5 . But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors ofer valuable insights. Tree diferent schools of thought characterise their perspectives: frst, supply-side or product investors: those who prioritise investing in frms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors: those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing OPEN 1 The Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia. 2 School of Computer Science and Engineering, UNSW Sydney, Sydney, NSW, Australia. 3 Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia. 4 Oxford Internet Institute, University of Oxford, Oxford, UK. 5 DWG Datenwissenschaftliche Gesellschaft Berlin, Berlin, Germany. 6 Melbourne Graduate School of Education, The University of Melbourne, Parkville, VIC, Australia. *email: fabian.braesemann@oii.ox.ac.uk

2 Vol:.(1234567890) Scientifc Reports | (2023) 13:17200 | https://doi.org/10.1038/s41598-023-41980-y www.nature.com/scientificreports/ or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Tirdly, talent investors: those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address. Investors who adopt the third perspective and prioritise talent ofen recognise that a good team can overcome many challenges in the lead-up to product-market ft. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need6 . Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifed away in the mid-1980s onwards towards more environmental factors such as venture capital fnancing7–9 , networks10, location11 and due to a range of issues and challenges identifed with the early entrepreneurship personality research12, 13. At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her infuential work ’Te Sociology of Entrepreneurship’, Patricia H. Tornton14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). Te supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as fnance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. Tis new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship15–19, that there is heterogeneity in defnitions and samples used in research on entrepreneurship16, 18, and that founder personality plays an important role in overall startup outcomes17, 19. Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions: 1. Which personality features characterise founders? 2. Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success? We aim to understand whether certain founder personalities and their combinations relate to startup success, defned as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology20, which matches people to their ‘ideal’ jobs based on social media-inferred personality traits. We fnd that personality traits matter for startup success. In addition to frm-level factors of location, industry and company age, we show that founders’ specifc Big Five personality traits, such as adventurousness and openness, are signifcantly more widespread among successful startups. As we fnd that companies with multifounder teams are more likely to succeed, we cluster founders in six diferent and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specifc combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have signifcantly higher odds of success. We organise the rest of this paper as follows. In the Section "Results", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Ten, we present the result for multi-variate regression analysis that allows us to relate frm success with external and personality features. Subsequently, the Section "Discussion" mentions limitations and opportunities for future research in this domain. In the Section "Methods", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information. Results Data Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology20 from Twitter user profles. Here, we restrict our analysis to founders with a Crunchbase profle. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profle can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account. We infer the personality profles of the founding teams of early-stage ventures from their publicly available Twitter profles, using the methodology described by Kern et al.20. Ten, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. Te fnal dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section A.5). Revisions of Crunchbase as a data source for investigations on a frm and industry level confrm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at microlevel, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States21. Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a

3 Vol.:(0123456789) Scientifc Reports | (2023) 13:17200 | https://doi.org/10.1038/s41598-023-41980-y www.nature.com/scientificreports/ reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association22. However, we acknowledge that the data source might sufer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profle from the database). The defnition of startup success Te success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates23, and others focus on fundraising from external investors at various stages24. Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to fnd traction. Success in a startup is typically staged and can appear in diferent forms and times. For example, a startup may be seen to be successful when it fnds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming proftable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Ofering (IPO). For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists25. However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. Tis is because it refects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth26, liquidity events27–29, proftability30 and social impact31, all of which have benefts as they capture incremental success, but each also comes with operational measurement challenges. Terefore, we apply the success defnition initially introduced by Bonaventura et al.32, namely that a startup is acquired, acquires another company or has an initial public ofering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and ofen signifcant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability. Using multifactor analysis and a binary classifcation prediction model of startup success, we looked at many variables together and their relative infuence on the probability of the success of startups. We looked at seven categories of factors through three lenses of frm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. Te model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section A.6 of the Supplementary Information (in particular Extended Data Fig. 19 and Extended Data Fig. 20). In total, we considered over three hundred variables (n = 323) and their relative signifcant associations with success. The personality of founders Besides product-market, industry, and firm-level factors (see SI section A.1), research suggests that the personalities of founders play a crucial role in startup success19. Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their frm’s success by applying the success defnition used by Bonaventura et al.32. Employing established methods33–35, we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. Te startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter. To measure the personality of the founders, we used the Big Five, a popular model of personality which includes fve core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status36Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses37. Further, as other research has shown, we assume that personality traits remain stable in adulthood even through signifcant life events38–40. Personality traits have been shown to emerge continuously from those already evident in adolescence41 and are not signifcantly infuenced by external life events such as becoming divorced or unemployed42. Tis suggests that the direction of any measurable efect goes from founder personalities to startup success and not vice versa. As a frst investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifer to distinguish and classify entrepreneurs from employees and viceversa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig. 1A). Tus, based on personality information alone

4 Vol:.(1234567890) Scientifc Reports | (2023) 13:17200 | https://doi.org/10.1038/s41598-023-41980-y www.nature.com/scientificreports/ we correctly predict all unseen new samples with 82.5% accuracy (See SI section A.2 for more details on this analysis, the classifcation modelling and prediction accuracy). We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was signifcant and had the largest efect size. Te facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable efect (Fig. 1B). Adventurousness in the Big Five framework is defned as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily43 and is about developing and testing new products, services and business models with the market. Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure 6). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Ten, once we established the founder data clusters, we used agglomerative hierarchical clustering. Tis ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Ten it merges them to create a hierarchy of possible cluster schemes with difering numbers of groups (See Extended Data Fig. 7). And lastly, we identifed the optimum number of clusters based on the outcome of four diferent clustering performance measurements: Davies-Bouldin Index, Silhouette coefcients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure 8). We fnd that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig. 1C. To better understand the context of diferent founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research44. Tis research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identifed occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig. 9 for an overview of these tribes, as identifed by McCarthy et al.44). Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Tus, these clusters represent and share personality attributes of these previously identifed occupation-personality tribes44, which have the following known distinctive personality attributes (see also Table 1): • Accomplishers (#0)—Organised & outgoing. confdent, down-to-earth, content, accommodating, mildtempered & self-assured. • Leaders (#2)—Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confdent. • Fighters (#5)—Spontaneous and impulsive, tough, sceptical, and uncompromising. We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section A.3 for more details). For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of diferent tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators. To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that refected the common themes of these occupations, namely: Table 1. Typology of Founders by Personality. Six diferent types of founders are revealed by clustering founders (n = 32 k) by their Big Five personality facets. Each type—Fighter, Operator, Accomplisher, Leader, Engineer and Developer (FOALED)—has its distinctive personality footprint. Founder type Distinctive Personality Traits Clustered by personality (Cluster code) Personality traits of founders in this cluster (Big Five facets) Fighter (#5) Emotional stability (anger, anxiety, depression, immoderation, self-consciousness, vulnerability) Operator (#4) Highest in conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster. Accomplisher (#0) Highly extraverted (all facets) and conscientious (fve facets) Leader (#2) Highest in openness in the facets of artistic interests and emotionality also highest in agreeableness in facets of altruism and sympathy. Expert/Engineer (#1) Highest in openness in the facets of imagination and intellect. Developer (#3) ’Middle child’ cluster—no facets are maximums or minimums, but it shares characteristics similar to fghters but higher in extraversion