The impact of founder personalities on startup success

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 firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’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 effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ 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 find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

population at large. We can train a classifier to distinguish founders from employees with 82.5% accuracy. 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).
However, we do not find one "Founder-type" personality; instead, six different personality types appear, with startups founded by a "Hipster, Hacker and Hustler" being twice as likely to succeed. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which has the potential to be extended through further research into other team settings within business, government and research.

Background
The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new net jobs [9]. 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 first 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 significant and growing municipal debt. Then 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 Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives [10]. The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz's C1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups [11].
While stories such as the success of BioNTech are often retold and remembered, their success

Introduction
In this project, we aimed to understand whether certain combinations of founder personalities are related to startup success, defined as when the firm has been acquired, acquired another firm or is listed on a public stock exchange. The project provides a large-scale quantitative perspective on the colloquial "Hacker, Hustler, Hipster" [14] dream team that is envisaged to form the optimal combination of personalities to accomplish business success. For the quantitative analysis, we draw on a previously published methodology [15], which matched people to their ideal jobs based on social media-predicted personality traits.
Here, we applied the same methodology to another set of Twitter users: founders and executives with a Crunchbase profile. Crunchbase is the world's largest directory on startups. It provides information about more than 1 million companies, primarily focused on funding and investors. A company's Crunchbase profile can be considered a digital business card of an earlystage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account. Again, as with Twitter, all information on Crunchbase is publicly available.
In this project, we inferred the personality profiles of the founding teams of early-stage ventures using the methodology described from their publicly available Twitter profiles. Then, we correlated this information to funding from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures.

What makes for a successful startup?
Venture capitalists and other investors, especially in early-stage unproven startup companies, each have their perspective on the key factors that make for likely success. Three different schools of thought can mostly characterise these different perspectives: Supply-side or product investors: those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks.
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 or recurrent or emerging large-scale social and economic challenges such as decarbonisation of the economy.
Talent investors: those who prioritise the foundation team above the startup's initial products or what industry or problem it is looking to address.
Getting to the point at which the startup has demonstrated the market is willing to use and pay for its novel products and services regularly, known as product-market fit, is seen as a vital milestone for investors and founders alike, and is often a conditional trigger for additional rounds of investment.
Much focus in recent years has been on reconciling the first two of these investor perspectives to achieve product-market fit as quickly and with the least possible capital invested in creating a minimum viable product.
However, investors who adopt the third perspective and prioritise talent recognise that a good team can overcome many challenges in the lead-up to product-market fit. 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. Some of today's most prominent startup success stories, such as Twitter, were not the startups' first idea for a product or service but the result of trying several other things that failed.
This story is common in product innovation, with many well-known consumer products emerging from previous "failures". For example, the renowned engineering lubricant WD-40 is so named as the result of the 40th attempt to create the formula, and 3M's Post-It notes were a product made from a "failed" adhesive project.
In this article, we analyse a variety of firm-level, founder-level and founder-team-level determinants of the success of startups, which are by their very nature experimental, high risk and likely to fail.
Firstly, we examine a range of firm-level determinants of startup success, including location ( Fig. ), industry ( Fig. ) and age of startup ( Fig. ) to explore to what extent these factors are associated with success. Then building on our previous occupation-personality fit research [

The rise of the hipster in startups
Clear functional roles have evolved in established industries such as film and television, construction and advertising.
In advertising, there is a long-established functional distinction between the categorical roles of creatives (people who devise the words, images and music for advertisements, including copywriters and creative directors), suits (client-facing account managers and sales executives) and quants (strategy and planning roles associated with audience measurement and the buying and placements of advertisements across different media).
The necessary tension, especially between suits and creatives in advertising, is well understood, as "there is an enduring oppositional culture between the 'creatives' and the 'suits' within agencies. From the point of view of the 'creatives', the lifeblood of the agency is considered to lie in the creative team with the other functions either considered inferior or unavoidable evils" [17].
In technology, the categorical roles of Hackers (skilful computer programmers and developers) and Hustlers (entrepreneurial leaders able to win over customers and investors to new products and ideas) have been around for decades, with similar oppositional tension. For example, when Steve Jobs announced he would take medical leave from Apple in January 2009, Mat "Wilto" Marquis described him as a hacker and a hustler in a well-wishing tweet.
However, the first use of Hacker and Hustler in conjunction with Hipster in the context of the putative startup founder dream was coined by influential venture capitalist Elias Bizannes in 2011. It was then popularised in 2012 by an address at the influential technology conference South by Southwest by Rei Inamoto and in a subsequent Forbes article "The Dream Team: Hipster, Hacker, and Hustler" [14].
Hipster is a broad term used to describe members of an urban subculture in many cities in the US and other countries who are design conscious and favour non-mainstream fashions, trendy foods and alternative music. Bizannes co-opted the term to reflect what he perceived was the increasing need for successful startups to have a founder with design-savvy, aesthetic imagination and insider knowledge (Hipster) in addition to the traditional roles of someone good at selling things (Hustler) and creating technology products (Hacker).
Founders are not like most other people We then created a list of low EOI occupations (n=112), each of which had less than 0.5% of whom also held the titles founder or co-founders in their LinkedIn Profile. People in these roles may still be founders and co-founders, but it is unlikely that they are. Any individual in even the most entrepreneurial of these 112 occupations (internal auditor) is still five times less likely also to be a founder or co-founder than the global average (2.5%) across all 624 occupations. From our previous study, we randomly selected a sample of Successful Employees (n=6k) for whom we have inferred personality data and who are unlikely to be entrepreneurs as they are drawn from the 112 low EOI occupations.
Using the two samples together: Successful Entrepreneurs and Successful Employees (unlikely to be founders), we trained and tested a machine learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict Entrepreneurs with 77% accuracy and Employees with 88% accuracy (Fig. ). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See Extended Data Fig. 1 for details on modelling and prediction accuracy.).

Adventurousness -the key feature
We explored in greater detail which personality features are the most important in distinguishing successful entrepreneurs from successful employees and found that the subdomain or facet of This is important because, to our knowledge, this is the first study to show differences between employees and entrepreneurs at the facet level of the Big 5 personality domains and the largest-scale study (n=10.4k) of any kind in this field.
In our sample, Successful Entrepreneurs were defined as founders or co-founders of companies who have attracted over USD $100k+ in investments from venture capitalists. This is consis- Then, once we established the founder data clusters, we used agglomerative hierarchical clustering; a "bottom-up" clustering technique that initially treats each observation as an individual cluster and then merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig. 4).
And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index. We found that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5).

Personality footprints of founders
To better understand the unique personality characteristics of each of the six different clusters of founders and co-founders we: 1. Analysed the personality footprints of each cluster. We examined the distinctive personality traits of each group and identified which clusters were home to the maximums in each of the 30 personality facets (See summary in Table 1) and also created a heat map revealing the complete personality footprint of each of the six types ( Fig. ).

2.
Matched the occupation closest to the centre of each cluster using the personalityoccupation matrix from our previous research in two separate studies based on 128,279 people in 3,513 professions using ten dimensions [15] and a second more recent study based on 99,897 people in 624 occupations using 30 personality dimensions [16].
3. Identified which of the eight occupation-tribes from previous research [16] each founder or co-founder belonged to. Leveraging previous research, we then looked at the distribution of tribe membership of each founder within each cluster.

Founders within the personality-occupation landscape
To better understand the context of different founder types, we positioned each of the footprints of each of the six types of founders within an occupation-personality matrix (n=624 jobs) established from previous research [16]. Prior research showed that "each job has its own personality" using a substantial sample of employees (n=99k) across various jobs. Furthermore, we found that the occupations themselves clustered into eight different groups-which we refer to as occupation tribes -based on their personality alone. The key personality attributes of each of these tribes from this prior research is reproduced in Extended Data Fig. 16.

Distinctive Personality Traits
Personality traits of founders in this cluster (Big 5 facets)

3H Typology
Hipster / Hacker / Hustler Highest in openness in the facets of artistic interests and emotionality also highest in agreeableness in facets of altruism and sympathy. For each founder and co-founder, we found the closest corresponding occupation tribe for each based on personality similarity. Then we tallied the founders within each cluster by tribe to reveal the level of coherence or the extent to which most founders within each group belonged to one occupation tribe.
These labels also accord with the distribution of roles founders in each of these clusters hold.
Accomplishers are often CEOs, CFOs or COOs while Fighters tend to be CTOs, CPOs and CCO.
(See Extended Data Fig. 6 for more details).
We labelled these clusters with these tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See Extended Data Fig. 5 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 different 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; #3 Fighters and #4 Accomplishers.
To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely: • Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers.
This is consistent with this cluster's personality footprint, which is highest in openness in the facets of imagination and intellect.
• Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.
• Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.
Together, these six different types of startup founders ( Fig. ) represent a framework we call the FOALED model of founder types -an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and Developers.
Each founder Personality-Type has its distinct facet footprint. Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level ( Fig. ).
Evidence for the "Hipster, Hacker, and Hustler" thesis By analysis of the six types of startup founders in our FOALED model within the broader Occupation-Personality landscape, we identify three types to be characterised as types of Hackers (Fighters, Operators and Developers) and two as Hustlers (Accomplishers and Leaders). The remaining type is different in personality to both Hackers and Hustlers. It is more of a subject matter expert whose insider field knowledge and problem-solving design strengths can be seen as a type of Hipster (Engineer).
When we subsequently explored the combinations of personality types among founders and their relationship to the probability of the firm's success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of startup success for Hipster, Hacker and Hustler foundation teams ( Fig. ).   This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. b, We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that "breed" success. c, In our multifactor model, we show that firms with diverse and specific combinations of types of founders, including (Hipster, Hustler, and Hacker) have significantly higher odds of success. The definition used by Bonaventura et al. [7], namely that a startup either is acquired, acquires another company or has an initial public offering (IPO), sees 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 often significant business growth prospects.

Ensemble Theory of Success
Rather than looking at associations of any one factor of success, we use a quantitative multifactor analysis of success that incorporates a range of firm-level factors such as where a startup is Founder-team personality combinations.
The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in Extended Data Fig. 13 and in Extended Data Fig. 14 respectively.
In total, we considered over three hundred variables (n=323) and their relative significant association with success.

Firm-level factors and success
The first lens we looked through was at the firm-level. Much of the previous literature on startups has been focused on firm-level or external factors and their influence on success [5]. Startup success has been shown to relate to how much capital the startup has raised, how old it is and what industry it is in, among other things[28].
Here we show startup success is influenced strongly by its location (firms from Japan, Scandinavia, USA, France, and Germany are more likely to be successful than those from Turkey, Argentina, Mexico or other countries); industry (firms in Payment Systems and Privacy & Security are most successful) and a company's age (more details in the SI).

Founder-level factors and success
The second lens we looked through was that of founder-level factors or those internal to the firm, i. e. the personality features of founders and their association with success. Our modelling shows firms with multiple founders are more likely to succeed, as illustrated in Fig. ), which shows firms with three or more founders are more than twice as likely to succeed as solo-founded startups.
This finding is consistent with investors' advice to founders and previous studies [8]. (We also noted that some types of additional founders increase the probability of success more than others as shown in Extended Data Fig. 10 and Extended Data Fig. 11).
Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, which naturally provides a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus potentially complementing each other's particular strengths associated with startup success.
The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big 5 personality team footprints, illustrated in Figure ). Here maximum values within each startup for each Big 5 trait for any of its cofounders are mapped, and the spread of these between successful firms -those who have IPOed, been acquired or acquired another firm and the other firms are shown.

Team-level factors and success
Lastly, we considered team-level factors -founder team personality combinations and how they related to startup success.
We found that ten combinations of founders with different personality types were significantly correlated with greater chances of startup success when accounting for other variables in the model. The coefficient of each of these factors is illustrated concerning other features that were also found to be significantly associated with success in Figure ( Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with: • A Leader and two Developers (a hustler and two hackers) • An Operator and two Developers (three hackers of two different types) • An Engineer, Leader and Developer (a hipster, hustler and hacker) The last of these aligns with and provides evidence for the Hipster, Hustler & Hacker hypothesis as well as a commonality of Developers or "purebred hackers" in all three of the most successful combinations.

Discussion
Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world's most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork's touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer's COVID-19 vaccine.
We have shown that founders' personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders' personality traits are significantly different from those of successful employees -so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.
Just as occupation-personality maps derived from data based on people already successful in those roles can provide career-guidance tools, so too can data on successful entrepreneurs' personality traits help others decide whether to become a founder may be a good choice for them.
We have learnt through this research that there is not one type of ideal "entrepreneurial" personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.
Startups are, to a large extent, a team sport; as such, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company's likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits. Our work demonstrates the benefits of diversity among the founding team of startups. Greater awareness of these benefits may help create more resilient startups capable of more significant innovation and impact.

Biases and Limitations
While each is large and comprehensive, there are some known and likely sample biases in the principal data sources used (namely Crunchbase, Twitter and LinkedIn).
Crunchbase is the principal public chronicle of Venture Capital funding, and so there is some likely sample bias toward: • Startup companies that are funded externally. Self-funded or bootstrapped companies are less likely to be represented in Crunchbase.
• Technology companies, as that is Crunchbase's roots.
• Multifounder companies. As it's a public social record, companies with multiple founders are likely better represented in Crunchbase than those with one founder. • Companies that succeed. Companies that fail, especially those that fail early, are likely to be less represented in the data.
Samples were also limited to those whose founders are active on Twitter, which adds addi-  There is also potential for extending this research in other settings in government, NGOs and within research itself. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another

Opportunities and Future research questions
This study demonstrates that successful startup founders have significantly different personalities than many successful employees. It also shows that many factors influence startup success.
The methods and data described here reveal that firm-level factors such as the startup's context within geography (where it is located), the economy (which industry it addresses), and timing

Data Sources
Entrepreneurs Only (EO) Dataset. Data about the founders of startups were collected from Crunchbase (Table 2), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.
Crunchbase contains data on over two million companies -mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. While Crunchbase started in the technology startup space, it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.
While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.
We selected founders active on Twitter to be able to use natural language processing to infer Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).
The "Entrepreneurs Only" (EO) dataset is analysed in combination with other data about the companies they founded to explore questions around the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.
For the multifactor analysis, we cleaned EO the data filtering by a number of factors to ensure the sample was robust and consistent. More details on this data wrangling is included in Extended Data Fig. 7 and Extended Data Fig. 8.

Tab. 2:
| Summary of the basic information of the Entrepreneurs Only (EO) dataset the number of founders and associated startups in population, how many countries those startups are across, and the time span the data collected covers, the number of features included. ).  (n=16,675). The attraction of a significant investment from outside, especially from specialist venture capitalists, is seen as one measure that indicates a startup has had some degree of success or, at the very least, shows promise of future success. Therefore, we filtered the EO Funded Founders by those whose companies had attracted more than US$100k in investment to create a reference set of Successful Entrepreneurs (n=4,400).

Founders with Personality Data
Most company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.
To create a control group of Successful Employees, who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier, Zoologist and Detective very rarely co-occur with founder or cofounder. Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit. It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI for more detail). A reference set of Successful Employees (n=6,685) was then selected across 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, also from the previous occupationalpersonality fit study (PX McCarthy and others, 2022).
The Successful Entrepreneurs and Successful Employees were combined to create the SEE dataset, which was used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical Clustering
We applied a number of clustering techniques and tests to the personality vectors of the EO data set to determine if there are natural clusters and, if so, how many are the optimum number.
Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic -a statistical test we used to answer whether the "EO" dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the "EO" dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real "EO" dataset. The ratio measures the difference between the "EO" data distribution and the simulated uniform distribution, which tests the randomness of the data.
The range of Hopkins statistics is from 0 to 1. Where the scores are close to 0, 0.5 and 1, respectively, this indicates whether the dataset is uniformly distributed, randomly distributed or highly clustered.
To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC) -a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward's linkage method is used to choose the pair of clusters for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, where the lower heights represent more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of clusters.
A range of Clustering performance metrics were used to help determine the optimal num-ber of clusters in the dataset after an obvious clustering tendency was confirmed. The following metrics were implemented to comprehensively evaluate the differences between within-cluster and between-cluster distances:

Privacy and ethics
The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase, we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data and Code Availability
A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are also available.
Extended Data Fig. 2

Occupations within startups
Information about personality traits not only helps to distinguish between individuals who tend to be founders of startup companies and employees, but it also correlates with the job role that founders will take in the startup companies they establish. For example, Extended Data Figure 6 shows the distribution of the six founder personality clusters by eight typical job roles in startup companies. Extended Data Fig. 7: | Data Wrangling for Multifactor Analysis Effects of the data cleaning on the sample size. Five preparatory steps reduce the data set to 25,214 founders with inferred personality traits who have been involved in founding 21,187 startup companies.
To use the founder data from Crunchbase described above (32k profiles) for the success prediction, we needed to conduct several preparatory steps, which led to a reduction of the final number of observations as outlined in Extended Data Figure 7.
The aim is to create a company-founder panel from the Crunchbase data based on exact founder names and company URLs as identifiers. Starting with 32,727 profiles corresponding to 23,292 companies, we removed organisations without names, reducing the data set to 27,181 founders and 23,290 companies. As a next step, we kept only those founders in the data set, founding the 23,290 companies in the data (via the 'founders' column), yielding a total of 25,341 founders and 21,351 companies. Merging these founders with the companies led to a further reduction of the data set to 25,338 founders and 21,311 companies. The merging also resulted in some duplicates because of the identical names of some founders. These duplicates were removed by keeping only those company-founder combinations where the company of each potentially duplicated founder was mentioned either as their primary organisation or in their biography. This step did not affect the number of founders but reduced the number of companies by three, which could not unambiguously be assigned to any individual. As the last step, we removed companies that were founded before 1990, leading to a final data set of 25,214 founders involved in the foundation of 21,187 companies.
Extended Data Fig. 8: | Foundation Year Number of companies in the data set by foundation year. Following the approach taken by Bonaventura et al. (2020), we restrict the data set to those companies founded from 1990 onwards.
In reducing the data set to those companies that were founded from 1990 (see Extended Data Figure 8) onwards, we aimed at limiting the potential bias that could arise from having companies in the data set that cannot be considered as startup companies because of their age. Therefore, this additional restriction removes less than 0.6% of the companies in our data set.
In total, 3,442 of 21,187 companies (16%) in the data set have been successful according to the criterion used by Bonaventura et al. [7]. On average, successful companies needed 6.38 years to become successful (see Extended Data Figure 9). Extended Data Fig. 11: | Founder types and success Firms with specific founder personalities have higher chances of success -most significant for personalities of the "Accomplisher" type. On the one hand, the correlations between external and internal factors and success, on the other hand, are visible when comparing different machine learning models that predict startup success. For example, Extended Data Figure 13 shows the predictive performance of six logistic regression models compared to a baseline random draw model. According to the recall Machine Learning Performance metric, the best-performing models (5) and (6)