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International Journal of Wine Business Research

ISSN : 1751-1062

Article publication date: 16 December 2021

Issue publication date: 5 July 2022

The wine industry is the one that is tightly linked to sustainability as its processes both impact and are impacted by the environment, society and companies’ financial well-being. However, data show that this may not be recognized in practice. Thus, this research paper aims to examine what has changed with respect to sustainability practices over the past 10–15 years in this industry.

Design/methodology/approach

A development-based multi-method approach was used to examine the purpose. In Phase 1, a grounded theory study conducted between 2009 and 2015 from wine businesses in 12 different global regions brought to light a potential disconnect between theory and practice in the importance of sustainability. In Phase 2, a comprehensive literature review and analysis of updated online content from the Phase 1 companies was conducted to paint a picture of the progression of sustainability focus and its implementation in company processes.

Using legitimacy theory as a foundation, it was found that the choice to pursue sustainability in this industry generally begins with a focus on environmental practices followed by financial sustainability and more recently social sustainability. Producers are also starting to emphasize overall sustainability often encompassing all three dimensions. The industry has also progressed through “levels of sophistication” in the different major supply chain processes (supply, production and distribution) over the years with their environmental efforts.

Originality/value

A framework of sustainability growth in the industry through a matrix of process sophistication is developed from the data. The results offer implications for theory, practice and industry policy and informs the future trajectory of sustainability within global business.

• Sustainability
• Literature review
• Content analysis
• Grounded theory
• Sustainable supply chain practices
• Global wine industry
• Sustainability growth
• Multi-method study

Acknowledgements

The author thanks Colorado State University College of Business, Colorado State University Office of International Programs and University of West Australia Visiting Fellowship for their help and support.

Golicic, S.L. (2022), "Changes in sustainability in the global wine industry", International Journal of Wine Business Research , Vol. 34 No. 3, pp. 392-409. https://doi.org/10.1108/IJWBR-03-2021-0021

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• Open access
• Published: 23 August 2013

Sustainability in the wine industry: key questions and research trends a

• Cristina Santini 1 ,
• Alessio Cavicchi 2 &
• Leonardo Casini 3  

Agricultural and Food Economics volume  1 , Article number:  9 ( 2013 ) Cite this article

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Sustainability is playing a key role in the wine industry as shown by the attention paid at several levels by the academia, institutions and associations. Nevertheless, the principle itself of sustainability opens a wide debate and it significantly affects firms in all their activities.

Using a systematic literature review, this paper wants to highlight some of the questions that academics must face when they approach the issue of sustainability with a specific focus on the wine industry. In particular the paper aims to: highlight where research is going and what has already been done; define the contribution of background research in explaining the determinants of sustainable orientation in the wine industry; and understand the role of research (and academics’ social responsibility) for the diffusion of a sustainable orientation within the wine industry. The purpose of this paper is to provide a detailed overview of the main research contributions to the issue of sustainability in the wine industry.

Introduction

The wine industry is definitely engaged in sustainability. The emerging interest in sustainability is confirmed by a growing body of academic literature as well as by the rise of new academic journals and scientific communities. Also, the industry has shown an involvement in sustainability in general; people in the wine industry wonder about the effectiveness of sustainable practices and under what conditions it pays to be oriented towards sustainability. Talking about sustainability opens up a multitude of research issues, especially in wine, where being sustainable is often misunderstood with being organic or biodynamic. This paper investigates background research on sustainability in wine; it outlines what are the main challenges that scholars must face when they deal with this research issue. After having provided a description of research trends, the paper will highlight the determinants of a firm’s orientation towards sustainability and the role that research has in promoting sustainability.

So many green nuances

The word “sustainability” has so many definitions that it holds a shadow of ambiguity (Warner 2007 ). Sustainability can be seen as a concept based on various principles (de Bruyn and van Drunen 2004 ): economic principles (maximising welfare and improving efficiency), ecological principles (living within carrying capacities and conservation of resources) and equity-principles that concern intragenerational and intergenerational equity (the disparity of wealth among different regions of the world or among generations).

(Ohmart 2008 ) gives an idea of how complex it is to be sustainable in agriculture: “sustainability involves everything you do on the farm, including economics, environmental impacts of everything done on the farm and all aspects of human resources, including not only you and your family but your employees and the surrounding community” (Ohmart 2008 ): 7.

Nevertheless, there is no univocal sustainable behaviour and some companies should be considered more sustainable than others. (Isaak 2002 ) distinguishes between green and green-green businesses ; green – green businesses are green oriented since their start up, whilst green businesses become green after that managers- who are not inspired by ethical issues - have intuited the benefits (in terms of marketing, corporate image positive feedbacks or cost savings) that being “sustainable” might create for the company.

In a recent study, Szolnoki ( 2013 ) points to the idea that wineries have of sustainability: misunderstandings and differences in the approach between countries and wineries emerge.

Some countries are “greener” than others according to the degree of companies’ sustainable behaviour. Globally spoken, California holds a leading position among the most sustainable agricultural producing countries: Warner ( 2007 ) describes the efforts spent by the Californian wine grape industry for reaching and educating growers about quality issues and sustainability; the availability of place-based networks of production has facilitated social learning among grape growers. (Warner 2007 ) says: “More than any other group of California growers, winegrape growers are operationally defining sustainability as agricultural enterprise viability, environmental quality and product quality” (p.143). In Northern California there are about 40 industry organizations advocating sustainability in addition to several associations that support organic viticulture at a national level (see among others: Washington State Association of Wine grape Growers; Oregon Wine Advisory Board; New York Wine & Grape Foundation; Penn State Cooperation Extension; Wine Council of Ontario). A case worth to be mentioned is the one of Lodi region in California (Ohmart 2008 ) that effectively shows how a local economic system could respond to the call for sustainability: after having released a workbook programme that encompasses all the sustainable practices in winemaking, results have been monitored in order to assess how principles have been implemented and to examine action plans carried by companies and associations. The great success of the Lodi programme relies on the active involvement of growers that is the result of a successful combination of workshops, a proactive behaviour of associations and effective communication flows.

Starting from the behaviour adopted by wineries, some scholars (Casini et al. 2010 ) have proposed a model that would help to classify wineries’ orientation in terms of sustainability. In the model, “ devoted ” wineries have a strong orientation towards sustainability that is emphasised in customer communication; those companies must invest in customers and employees training and education; furthermore devoted wineries must ensure an alignment between their corporate and managerial visions. Another category of wineries, the so called “ unexploiters ”, stands half the way between devoted and “ laggards ” wineries, or those who would never adopt sustainable practices. Unexploiters usually decide to adopt sustainable practices, but do not inform other people (clients, first of all) about their decision. Consequently the benefits that might be gained through a sustainable orientation are limited. At the opposite of unexploiters stand opportunists , wineries that do not have a particular interest in sustainability, but tend to heavily highlight the few sustainable practices introduced.

As it can be guessed, companies can choose among various alternatives: it is not only a matter of being green or not, but they can also choose among a multitude of “green nuances”.

We conceive sustainability as a behaviour adopted to respond to stimuli, whether they are external or internal to the firm. This perspective introduces three elements into the discussion: firstly, the presence and the type of stimuli or drivers; secondly, the degree of responsiveness that characterises the organization; and thirdly, a firm’s motivations.

In other words, we can say that orientation towards sustainability depends on how the following questions can be answered: Who cares about sustainability issues? How much do I and my organization care about sustainability? Why should we care about sustainability?

This paper aims to systemically analyse the main academic contributions to the issue of sustainability in the wine industry in order to outline insights that can depict strategic, managerial, consumer and organizational implications, and to highlight what are the main challenges that scholars must face when they get into this research issue. After having provided a description of where the research is going, the paper will explain the determinants of a orientation towards sustainability among firms and it will outline the role of research in promoting sustainability.

Key drivers of sustainability

An analysis of the drivers of sustainability is, in our opinion, the first step to understand the relationship between firms and sustainability. This section introduces the issue of drivers of sustainability, by highlighting the findings emerging from background research (the role of institutions and associations, the role of top management and entrepreneurs, etc.) and the role played by drivers in defining wineries’ orientation towards sustainability. We assume that an exploration of the incidence of perceived stimuli on companies’ choices represents a way for explaining firm’s behaviour; by conceiving sustainability as a behaviour adopted by firms in responding to selected stimuli, we focus on the impact that forces (external or internal to the firm) have on a firm’s strategy; being sustainable represents one of the strategic choices that firms can make. The presence of drivers affecting a firm’s orientation towards sustainability partially explains the differences in the overall degree of sustainability at a firm or at a country level. It is almost impossible to define a general ranking for estimating a country’s overall orientation towards sustainability: the numerous indexes available simply confirm the differences among countries, but it is extremely hard to classify them, because of the differences in index composition and in the trait of sustainability under observation. A focus on the key drivers of sustainability offers a balanced solution to this problem. Both academics and practitioners increasingly emphasize the issue of drivers. The company Accenture has elaborated a list of six key drivers of sustainability, that “are not only reshaping the way businesses and governments operate, but also redefining the value they deliver” (from corporate website). The list includes: consumer demand for sustainable products and services; stakeholder influence; resource depletion; employee engagement; capital market scrutiny; regulatory requirements.

Also background research (Dillon and Fischer 1992 ; Lawrence and Morell 1995 ; Winn 1995 ; Bansal and Roth 2000 ; Davidson and Worrell 2001 ; Marshall et al. 2005 ; Gabzdylova et al. 2009 ) has highlighted the role of drivers – whether they are conceived as internal/external or internal/institutional - to describe a firm’s adoption of a sustainable behaviour.

Internal drivers are all those drivers that take place within the firm: they are ethical motives inspiring top management and entrepreneurs as well as strategic intentions based on the recognition of an advantage that might arise from sustainability. External drivers, instead, take place in the firm’s external environment.

Institutions, associations, regulators and market demand

External drivers happen outside of the firm and include pressures arising from institutions, customers, communities, associations, environmental groups, activists, regulators and competitors.

Background research has highlighted the role played by industry associations in creating “sustainable awareness” among grapegrowers and wineries Broome and Warner ( 2008 ; Silverman et al. 2005 ; Warner 2007 ).

A key factor of success in spreading sustainable practices is local players’ networking capacity. In some specific areas, such as California, agro-ecological partnerships have fostered the adoption of sustainable agricultural practices (Swezey and Broome 2000 ; Dlott 2004 ) and they have proactively spread a green orientation among wineries (Broome and Warner 2008 ).

Environmental concerns have progressively found a diffusion among wineries and became strongly related to corporate image. New Zealand is heavily investing in environmental issues: “The New Zealand wine industry aims to be the first in the world to be 100% sustainable. The Sustainable Winegrowers New Zealand (SWNZ) programme introduced in 1995 is a framework of industry standards set up to achieve this by vintage 2012” (from the website: http://www.newzealand.com ).

Also corporate activism should be considered, as shown by the efforts spent by individual companies for promoting practices that would reduce gas emission and waste. The case of The Wine Group, in the US, highlights the consideration that large companies give to environmental issues: in 2008 The Wine Group has launched a website ( http://www.betterwinesbetterworld.com ) to document how “Bag in Box” can help in reducing emissions and waste ( http://www.winebusiness.com ). Both the New World and the Old World face similar environmental challenges but they strongly differ in terms of fertiliser usage, that is significantly lower in Europe ( http://www.eea.europa.eu ).

The development of specific programmes for sustainable winegrowing has fostered the adoption of “ground to bottle” practices for producing grapes and wine (Broome and Warner 2008 ). This is highlighted by the willingness that institutions and organizations show in providing long term financial support to sustainability programmes and training activities: (Warner 2007 ) underlines the need for continuous investments in reinforcing a commitment to sustainability.

Institutions and regulators have a prime role in enhancing wineries’ interest towards sustainability through funding the adoption of specific practices and education programmes (Swinbank 2009 ).

A orientation towards sustainability among competitors can foster a me-too mechanism with the result of spreading sustainable practices in the competitive environment: after that Mondavi has introduced the flange-type bottle with a C-cap on the market (Murphy 2000 ), other wineries in the market have shared - consciously or unconsciously – the same principles that have inspired Mondavi before the product launch.

Consumers’ involvement in sustainability is also reshaping wineries’ interest toward this issue, as described by (Bisson et al. 2002 ): “As consumers become more aware of the vulnerability of our global environment, the demand for sound agricultural production practices is increasing. In the future, the perception of the producer as a conscientious environmental steward will be an important influence on the consumer’s purchasing decision. This is due in part to the fact that the typical wine consumer is well educated and affluent” (p.698). Consumers’ pressure has created a market for wines inspired by environmental issues, such as organic or biodynamic wines (Forbes et al. 2009 ): in some countries, such as the UK, organic wine moved from a niche to a mainstream position (Sharples 2000 ).

Entrepreneurs and top management

Most of the research has focused on explaining the role of external drivers in enhancing a sustainability orientation within firms, but less research has been done about internal drivers.

A consistent body of research can be found in the general management and business strategy literature, that analyses the role of people involved within the organization in promoting a sustainability orientation: various issues have been investigated such as the role of top management’s values in determining sustainability orientation (Berry and Rondinelli 1998 ; Quazi 2003 ), entrepreneurial commitment to sustainability (Shaltegger 2002 ) or management practices and principles reshaped by a sustainability orientation (Atkin et al. 2012 ; Warner 2007 ).

In some cases, niche research fields have emerged by providing a “sustainability” perspective to diffused and internationally adopted research approaches: this is the case of Ecopreneurship (ecological entrepreneurship) or the Natural Resource Based View, a version of the Resource Based View of the Firm approach mainly based on environmental issues.

Ecopreneurship is a term that has been introduced in early 1990s (Bennett 1991 ; Berle 1991 ; Blue 1990 ) and that renames a growing body of literature that investigates most of the critical questions in entrepreneurship from an ecological and environmental perspective. The works by Walley and Taylor ( 2002 ), Shaltegger ( 2002 ) and Schaper ( 2002 ) provide a comprehensive overview of this research field. From this research the prominent role that personality traits can have on the degree of a sustainability orientation within firms emerges: for instance, Regouin ( 2003 ) has highlighted that reasons behind a firm’s conversion to organic farming depend on personal traits such as curiosity, flexibility, risk propensity and creativity in exploring innovative marketing approaches.

Although there is a growing body of academic literature that is exploring the “internal” drivers towards sustainability, only a few studies have been done on wine.

Sustainability and strategy

(Bonn and Fisher 2011 ) say that sustainability is often a missing ingredient in strategy: there is a great debate on corporate social responsibility, corporate environmentalism, sustainable practices adoption, green marketing, green corporate image, etc., but the issue of sustainability is not considered as priority in strategy making.

Research in wine has focused on the relationship between a sustainability orientation and competitive advantage.

It has been shown how being organic contributes to an effective differentiation (Bernabeu et al. 2008 ): Delmas et al. ( 2008 ) explore the case of a winery in California (the Ceago winery, owned by Fetzer), that has chosen to produce organic wine to differentiate its product from the mass; (Pugh and Fletcher 2002 ) examine how a wine multinational corporation (the BRL Hardy) focuses on a specific and different market segment through one of its controlled brands (Banrock Station) that supplies organic wine to the market.

, Gilinsky and Netwon in 2012 provides useful insights for understanding if incorporating an Environmental Management System (EMS) into business models positively or negatively affects wineries’ performance. From the research the relevance that EMS has in pursuing a differentiation strategy for some of the wineries who employ EMS has emerged. The literature shows that little attention has been paid to the benefits that implementing EMS might have for wineries (Forbes and De Silva 2012 ).

The role of research

Next to the wine industry, also research in the wine business is going green. Research in the field of sustainability in wine has been fostered by the growing interest of the industry and by the active role of institutions - that funds specific research programmes - associations or individual companies. Supporting research has resulted in a renewed interest in sustainability with the final result of promoting further research. When observing some cases - such as the Washington State Wine Industry - we can say that university research has fostered the development of the wine industry (Stewart 2009 ). (Ohmart 2008 ) suggests that a successful diffusion of sustainable practices among grapegrowers depends on two factors: rigorous science and its effective delivery to grapegrowers, two issues that partially explain the differences in terms of penetration and diffusion of sustainable practices in viticulture.

In their analysis of the history of winemaking in California, Guthey and Whiteman ( 2009 ) say that funded university research has contributed to shape Californian wine production thanks to the useful inputs provided for developing winemaking practices and understanding human environment relationships.

The field of sustainability in the wine industry appears as a breeding ground for the development of academics and university collaborations: (Lee 2000 ) provides a general framework that can be used for describing the benefits arising from the relationship between academics and industry. In general it can be said that collaboration between research institutions and the industry (1) may be helpful in solving technical problems, (2) may facilitate the access to useful findings; and (3) may make the implementation of innovation easier. It is not surprising that industry heavily supports research in some countries: we can cite among others the cases of the Wine and Food Institute in California cofounded by the Robert Mondavi Winery and the Anheuser-Busch Foundation and Ronald and Diane Miller of Silverado Vineyards ( http://www.winespectator.com ). Another case worth to be mentioned is the Australian Wine Research Institute, that has actively promoted research in the field of wine in general and has had a relevant role in spreading a sustainable culture among wineries. Research has been stimulated in new world countries and not only in California or Australia, as the case of Vinnova from Chile shows. Great efforts have been spent for codifying research insights and facilitating knowledge dissemination and accessibility: some countries, such as New Zealand and Chile, have developed Codes of Sustainability, to promote the adoption of sustainable practices among wineries.

Conducting research on sustainability has some social implications and researchers who are working in this field have a social responsibility: with their work, researchers can foster the adoption of sustainable practices among wineries at different levels and they can indirectly contribute to the growth of the overall welfare of people living in a certain area.

Research orientations: a selective systematic literature review

Methodology.

Where is research going and what has been done? In order to answer this specific question we have carried out a systematic literature review by analysing academic databases and some wine academic journals. In particular we have performed a keywords based research in the following academic search engines: ISI Web of Knowledge SM , Scopus SciVerse® and EBSCO (that contains Econlit, Business Source Premiere and Greenfile databases).

In our research we did not want to use generic “scientific” search engines (i.e., Google Scholars or Mendeley) and to perform a search on specific academic databases that are widely diffused among scholars.

The keywords used, combined with the word “wine” are: green, organic, sustainable, sustainability, biodynamic, ecopreneurship, environment . We have also selected some academic journals specialised in wine, and we have checked the presence of articles that examine the issue of sustainability in wine; the journals selected are: the International Journal of Wine Business Research; the Australian journal of Grape and Wine Research; the Journal of Wine Research, Enometrica and the Journal of Wine Economics. We have decided not to focus on analyses of practices: there is a wide literature on environmental and organic practices in the wine industry, but it mainly focuses on winemaking and agronomic aspects and we are interested in management, strategic and marketing. Background research has provided us useful inputs for performing our systematic literature review; in particular the works by (Lobb 2005 ) and (Thieme 2007 ) have been helpful for designing our methodology. The work by (Hart 1998 ) has been extremely useful for understanding how to analyse results. After having verified their contents, the articles have been included in a database that has been created for sorting and analysing results. We have then classified the articles collected into four main categories that have been built on the basis of major JEL classifications.

Topics, geographic area and research techniques employed

The main four categories corresponding to our classification of the main research bodies are (Table  1 ): (1) strategy; (2) entrepreneurial and top management behaviour; (3) consumer behaviour; and (4) supply chain management and certification. It is important to observe the differences emerging from the geographic area of research in which research has been carried out.

The category “strategy” includes all those articles that deal with the issues of business strategy and sustainability. It is a matter of fact that research on strategy is mainly performed in the New World Countries: Chile, New Zealand, US, Australia and Argentina lead the way to understand the links between wine and sustainability in a strategic orientation. Research techniques employed are often qualitative and case study research is frequently performed. One of the reasons could be the necessity to explore the main drivers of pressure towards sustainability taking into account the motivations and opinions of different wineries’ stakeholders. In fact, according to Flint ( 2009 ), in order to conduct such exploratory research, an appropriate methodology such as grounded theory is necessary that has been used to reveal how social actors interpret and act within their environments. In other papers, the aim is to enlighten an entire sector at national or regional level and for this reason a multidisciplinary case study approach is employed (Guthey and Whiteman 2009 ; Cederberg et al. 2009 ). The topics investigated in this category are diverse, but two trends stand out: at a country level the analysis is carried out to understand the boundaries of emerging organic wine industry and the implications to promote place branding activities; at firm level the interest is for internal and external pressures towards sustainable and environmental practices.

Even more concentrated is the research investigating “entrepreneurial and top management” drivers for the adoption or improvement of environmental behaviour: Marshall, Cordano and Silverman use the Theory of Planned Behaviour and the Theory of Reasoned Action mainly in US (California) and New Zealand wineries. Results are not univocal because the weight of internal and external pressures, attitudes and subjective norms can vary among cases and the papers give evidence of interactions among considered variables.

Contrary to the other research fields, the consumer behaviour field is investigated worldwide. Europe seems to be focusing more on consumers’ perception of - and willingness to pay for - organic wine, while the New World research is oriented to a more complex issue such as the environmental friendly label or a more general topic as green production practices. This is the category where quantitative analysis and statistical techniques are more used and developed.

Finally, research in the field of supply chain management and certification aims to give an overview of various attempts to implement codes of sustainable winegrowing practices and to reduce the impact of environment based activities on carbon emission; these studies are carried out both in the New and Old World. It is worth to emphasize the various methods employed to analyse impacts and efficiency of practices on the environment; not a single technique or tool seems to have been recognized worldwide as a standard for such measurement and thus more research is needed.

A brief final note is about the kind of journal and the year of publication: only 5 of the papers collected have been published before 2005. This highlights how “young” this field of study is. Particularly, the field of strategy seems to be the newest one.

An analysis of journals reveals a multidisciplinary interest in sustainability and wine: journals such as Renewable Agriculture and Food Systems, Journal of Cleaner Production, E:Co Emergence: Complexity and Organization, International Journal of Sustainable Development & World Ecology devote specific attention to the various facets of sustainability; on the other side the Journal of Wine Research and the International Journal of Wine Business Research have a wine sector focus. Then we can find another kind of reviews with a general focus on the agri-food sector ( British Food Journal , Food Quality and Preference and Acta Agriculturae Scandinavica Section B-Soil And Plant Science , Journal of Rural Studies ) or new journals with specific topics on firms and sustainability such as Business Strategy and the Environment .

Conclusions

We have seen how a tight relationship between academics and industry can provide benefits to the wine industry and can improve its overall orientation towards sustainability: research can help winegrowers in the adoption of sustainable practices and can provide answers to some managerial issues.

Scholars suggest to focus research on a few critical aspects, such as the reconfiguring and understanding of economic performance and the creation of the conditions for incremental adjustment and multidisciplinary learning to happen (Guthey and Whiteman 2009 ).

Research has a social responsibility in the development of a sustainability orientation in the wine business: once spread, research results can motivate wineries to adopt a sustainable behaviour and create a sustainability awareness among industry and consumers.

The main challenge is “to change perceptions and mind-sets, among actors and across all sectors of society, from the over-riding goal of increasing productive capacity to one of increasing adaptive capacity, from the view of humanity as independent of nature to one of human and nature as coevolving in a dynamic fashion with the biosphere” (Folke, 2002, in Guthey and Whiteman, 2009 ); research plays a key role in the achievement of this goal, and by helping managers and people during the learning process and the adaptation of the organization to the evolving social conditions.

Some scholars perceive the role played by the role of drivers in the defining a sustainability orientation as critical: “We encourage further intra-industry, as well as inter-industry, research in order to better understand when internal and external drivers are most critical, and perhaps at times, less critical, in ushering in environmental stewardship” (Marshall et al. 2005 ).

One of the key emerging research questions to focus on is: “under what conditions sustainability happens”. The wine industry is particularly suitable for research on sustainability, as it has been shown by the analysis of the literature we have performed.

Anyway, although sustainability issues are affecting the wine industry all over the world, research does not show how to keep the path of such a diffusion and it is much more intensive in some countries rather than others, as it has emerged from the analysis provided. It can be said that research is more concentrated and focused on sustainability in those countries where the pressure of drivers is stronger.

The originality of our paper relies in being the first classification about research on sustainability and wine. Our paper aimed to identify the main methodologies and research techniques used, as well as the main problems observed by scholars. Further investigations to highlight any relationship between university research and the pressure of key drivers should be carried out.

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Cristina Santini

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Alessio Cavicchi

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Authors’ contributions

CS carried out the systematic review on wine and set the database. CS analyzed together with AC the dataset. In particular CS wrote the paragraphs entitled “Entrepreneurs and top management” and “Sustainability and strategy”, “Research orientations: a selective systematic literature review”. AC carried out the general review on sustainability and together with CS has performed the dataset analysis. AC contributed to write, more specifically the paragraph entitled “Institutions, associations, regulators and market demand”: AC also contributed specifically to the development of the following paragraphs: “The role of Research” LC contributed together with the other authors to conclusions, discussion and introduction. All authors read and approved the final manuscript.

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Santini, C., Cavicchi, A. & Casini, L. Sustainability in the wine industry: key questions and research trends a . Agric Econ 1 , 9 (2013). https://doi.org/10.1186/2193-7532-1-9

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Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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• Human behaviour
• Information technology

An Author Correction to this article was published on 07 May 2024

This article has been updated

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.

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Introduction.

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 jobs 1 , 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 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 3 . 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 €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 startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The 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 later 5 .

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 offer valuable insights. Three different schools of thought characterise their perspectives: first, 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. 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 or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, 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 often 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. 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 need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 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 influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The 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 finance, 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. This 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 entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 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:

Which personality features characterise founders?

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, defined 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 methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly 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. Then, we present the result for multi-variate regression analysis that allows us to relate firm 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.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. 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 profile 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 profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final 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 firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . 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 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 Association 22 . However, we acknowledge that the data source might suffer 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 profile from the database).

The definition of startup success

The 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 rates 23 , and others focus on fundraising from external investors at various stages 24 . 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 find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds 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 profitable 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 Offering (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 capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (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 often significant 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 classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-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. 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 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 significant 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 success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The 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 five 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 status 36 Using 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 spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first 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 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.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification 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 significant and had the largest effect size. The 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 effect (Fig.  1 B). Adventurousness in the Big Five framework is defined 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 easily 43 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. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). 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 (see Extended Data Figure  8 ). We find 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.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This 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 identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified 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). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

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 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/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 reflected the common themes of these occupations, namely:

Experts/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.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. 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 have significantly higher odds of success.

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, naturally providing 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 Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

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 can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming 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.

To a large extent, founding a startup is a team sport; therefore, 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 personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) 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 founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. 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 critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs 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. Crunchbase started in the technology startup space, and 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 their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of 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 seem to be founders or co-founders. To illustrate, many 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.

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 44 . 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 section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were 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 several clustering techniques and tests to the personality vectors of the entrepreneurs’ 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 entrepreneurs’ 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 entrepreneurs’ 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 entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating 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 groups 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, with lower heights representing 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 groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

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 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 available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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Role of commercial enzymes in wine production: a critical review of recent research

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Purified enzymes of microbial origin are applied in the beverage industry since decades because of their ability to enhance products and processes with minimal side effects and low costs. Commercial enzymes are widely used during different wine making steps providing a broad range of effects, such as to maximise juice yield, improve aroma compounds, flavour enhancement, colour extraction in red wines, and contribute in the removal of dissolved unwanted colloidal particles and pectin substances during wine stabilization and filtration. This review presents a study of recent advances in the application of commercial enzymes in the wine making of red, white and sweet wines that have been made in essentially the last 13 years (2005–2018). Literature has been critically analysed to discover general rules about previous research. Special attention is paid to the safety of enzyme application due to allergic issues. Future research efforts should be concentrated on application of immobilizated enzymes and the use of microorganisms with potential enzymatic side activities during wine production.

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Microbial enzymes: industrial progress in 21st century.

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Espejo, F. Role of commercial enzymes in wine production: a critical review of recent research. J Food Sci Technol 58 , 9–21 (2021). https://doi.org/10.1007/s13197-020-04489-0

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DOI : https://doi.org/10.1007/s13197-020-04489-0

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The future of financial analysis: How GPT-4 is disrupting the industry, according to new research

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Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here .

Researchers from the University of Chicago have demonstrated that large language models (LLMs) can conduct financial statement analysis with accuracy rivaling and even surpassing that of professional analysts. The findings, published in a working paper titled “ Financial Statement Analysis with Large Language Models ,” could have major implications for the future of financial analysis and decision-making.

The researchers tested the performance of GPT-4 , a state-of-the-art LLM developed by OpenAI , on the task of analyzing corporate financial statements to predict future earnings growth. Remarkably, even when provided only with standardized, anonymized balance sheets, and income statements devoid of any textual context, GPT-4 was able to outperform human analysts.

“We find that the prediction accuracy of the LLM is on par with the performance of a narrowly trained state-of-the-art ML model,” the authors write. “LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company’s future performance.”

Chain-of-thought prompts emulate human analyst reasoning

A key innovation was the use of “ chain-of-thought ” prompts that guided GPT-4 to emulate the analytical process of a financial analyst, identifying trends, computing ratios, and synthesizing the information to form a prediction. This enhanced version of GPT-4 achieved a 60% accuracy in predicting the direction of future earnings, notably higher than the 53-57% range of human analyst forecasts.

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“Taken together, our results suggest that LLMs may take a central role in decision-making,” the researchers conclude. They note that the LLM’s advantage likely stems from its vast knowledge base and ability to recognize patterns and business concepts, allowing it to perform intuitive reasoning even with incomplete information.

LLMs poised to transform financial analysis despite challenges

The findings are all the more remarkable given that numerical analysis has traditionally been a challenge for language models. “One of the most challenging domains for a language model is the numerical domain, where the model needs to carry out computations, perform human-like interpretations, and make complex judgments,” said Alex Kim, one of the study’s co-authors. “While LLMs are effective at textual tasks, their understanding of numbers typically comes from the narrative context and they lack deep numerical reasoning or the flexibility of a human mind.”

Some experts caution that the “ ANN ” model used as a benchmark in the study may not represent the state-of-the-art in quantitative finance. “That ANN benchmark is nowhere near state of the art,” commented one practitioner on the Hacker News forum . “People didn’t stop working on this in 1989 — they realized they can make lots of money doing it and do it privately.”

Nevertheless, the ability of a general-purpose language model to match the performance of specialized ML models and exceed human experts points to the disruptive potential of LLMs in the financial domain. The authors have also created an interactive web application to showcase GPT-4’s capabilities for curious readers, though they caution that its accuracy should be independently verified.

As AI continues its rapid advance, the role of the financial analyst may be the next to be transformed. While human expertise and judgment are unlikely to be fully replaced anytime soon, powerful tools like GPT-4 could greatly augment and streamline the work of analysts, potentially reshaping the field of financial statement analysis in the years to come.

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