Industry-specific specialization in venture capitalists’ internationalization decisions

Nörthemann, Antonia Article — Published Version Industry-specific specialization in venture capitalists’ internationalization decisions Journal of Business Economics Provided in Cooperation with: Springer Nature Suggested Citation: Nörthemann, Antonia (2022) : Industry-specific specialization in venture capitalists’ internationalization decisions, Journal of Business Economics, ISSN 1861-8928, Springer, Berlin, Heidelberg, Vol. 93, Iss. 5, pp. 891-927, https://doi.org/10.1007/s11573-022-01129-1 This Version is available at: https://hdl.handle.net/10419/311240 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of Business Economics (2023) 93:891–927 https://doi.org/10.1007/s11573-022-01129-1 1 3 ORIGINAL PAPER Industry‑specific specialization inventure capitalists’ internationalization decisions AntoniaNörthemann1 Accepted: 1 December 2022 / Published online: 17 December 2022 © The Author(s) 2022 Abstract This study considers the impact of venture capitalists’ industry specialization on cross-border investment decisions. The article argues that specialized venture capitalists’ liability of foreignness is lower in specific international markets as assessments of the market and the behaviour of market participants are facilitated through industry-specific learning effects. Specialization leads to lower risk in cross-border investments from a venture capitalist’s point of view. With increasing deal-specificity, the support and value enhancement of a foreign portfolio company are ensured in international markets due to the investor’s specialization. A multilevel investigation of 46,525 worldwide venture capital deals from 2001 to 2019 in 69 countries shows a positive relationship between venture capitalists’ industry specialization and internationality. The results show that the likelihood of a cross-border deal increases with higher levels of industry specialization. Furthermore, this effect is moderated by determinants at the institutional and portfolio company levels, reflecting the degree of information asymmetry between the venture capital firm and the portfolio company. Keywords Venture capital· Internationalization· Specialization· Industry experience· Learning JEL Classification G11· G24· M13· M16· O34 1 Introduction The investment behaviour of venture capitalists is an increasingly international phenomenon (Devigne etal. 2018). As venture capital firms generally invest in young companies operating in aspiring markets, these investments are characterized by * Antonia Nörthemann antonia.noer[email protected] 1 Chair ofEntrepreneurship andFinance, Faculty ofBusiness Administration andEconomics, Heinrich-Heine-University, 40225Düsseldorf, Germany 892 A.Nörthemann 1 3 high levels of asymmetric information and uncertainty (Stuart et al. 1999; Ueda 2004; Vanacker and Manigart 2010). Investors face institutional, geographical, cultural and legal distance from their own nations (Devigne etal. 2018). This remoteness results in higher information asymmetries, investment risk and costs for the investor if the target company is located abroad. Therefore, the investor must devote more resources to monitoring and managing the business abroad (Pruthi etal. 2003; Mäkelä and Maula 2008; Devigne etal. 2016). The sum of these additional costs that would not occur in a domestic deal is defined as the ‘liability of foreignness’ (Zaheer 1995). Current literature on international venture capital offers several strategies to compensate for the liability of foreignness in cross-border deals. Studies often address cooperation with other investors through syndication or implementing close monitoring and supporting systems to track foreign portfolio companies’ activities abroad. Moreover, research has underlined the relevance of the accumulation and use of the investor’s own experience in international investments (Meuleman and Wright 2011; Li etal. 2014). Investment experience can be acquired in domestic markets as well as when investing abroad (Schertler and Tykvová 2012). According to Li etal. (2014), the intensity and outcomes of learning through prior investments, however, differ. Through investment experience gained in their home countries, investors can improve their understanding of the investment process from the initial screening of portfolio companies until the companies’ exits. Additionally, Guler and Guillen (2010) found evidence that venture capitalists with high social status in the domestic market can transfer it to the international market. Additionally, they are more likely to invest abroad with increased domestic experience. International experience acquired through investments in various foreign countries mitigates unfamiliarity and enhances venture capitalists’ awareness, tolerance and sensitivity to unexpected behaviours and practices in local institutions and cultural environments (De Clercq and Dimov 2008), particularly if the investor already has prior experience in a country (Li etal. 2014). In addition to domestic and international experience, this paper investigates the role of investors’ experience within specific industries, which will be defined as the investors’ industry specialization. During an investment’s holding period, monitoring and adding value to a target company are some of the major goals of venture capital investors (Gupta and Sapienza 1992; Bertoni etal. 2011). Thus, one can assume that the positive development of a portfolio company is more likely when the investor is familiar with business models in the specific industry of investment, particularly for international investments. Regarding the importance of industry-specific rather than general experience, Gompers etal. (2008) found that industry-specific experience is especially critical to identifying good investment opportunities and adding value to companies. In that information asymmetry and the overall risk of foreign investments are considered to be higher for international rather than domestic investments (Devigne etal. 2018), an investor’s industry specialization may be particularly important in international investment decisions. Therefore, this paper highlights a firm’s expertise within an industry as a crucial characteristic that may influence its internationalization decisions. A central role is assigned to tacit knowledge, which organizations continuously acquire 893 1 3 Industry‑specific specialization inventure capitalists’… through their activities (Itami and Roehl 1991; Hart 1995). By investing several times in one industry, investors gain a precise understanding of industry-specific products and business models. This comprehension is essential for offering valueadded services to a portfolio company and increasing the portfolio company’s value. Therefore, an investor’s industry-specific expertise may play a central role in the successful management of foreign investments and is likely to influence investment decisions. In addition to domestic and international knowledge, industry-specific knowledge—a firm’s specialization in an industry—may be a relevant competence for guiding portfolio companies and may attenuate a firm’s liability of foreignness. An investor’s industry specialization will likely determine how effectively the investor can advise his target company, as business processes within an industry are similar and recurring. Thus, with each additional investment in and support for a portfolio company within an industry, knowledge about how to support the portfolio company most efficiently increases, and successful practices are established. Furthermore, with increasing levels of industry specialization, the number of suitable investment opportunities in the domestic market matching an investor’s specialization may decrease. Specialized investors may, therefore, increasingly be forced to search abroad for suitable investment opportunities. Even though firms’ industry specializations may affect venture capitalists’ internationalization decisions due to different competencies and knowledge resulting from specialization and fewer opportunities in the domestic market, this is disregarded in the literature on international venture capital. The additional risks associated with an investment abroad may vary depending on a deal’s specificity. Consequently, there is a research gap regarding the fit between a target company’s business and a venture capitalist’s industry specialization and how this fit affects the likelihood of investing abroad. In an increasingly internationalized venture capital market, investigating this omission in the literature is important for understanding firms’ investment decisions based on their learning effects from previous deals. Contrasting with the prevailing view that diversification lowers the risk of a portfolio, Bygrave (1987); Bygrave (1988) offered the first theoretical views on specialization in venture capitalists’ portfolios. In a survey of 98 venture capital investors, Norton and Tenenbaum (1993) maintained that to reduce investment risk, venture capitalists should specialize and enhance their technical and product expertise. Most venture capitalists have invested according to a specialization strategy rather than diversifying their investments among various industries. Gupta and Sapienza (1992) also demonstrated that venture capital firms prefer less industry diversity when investing in early-stage companies. Based on a US sample of 169 venture capital firms from California, Massachusetts and Texas, the authors found that small venture capital firms are less likely to invest in different industries. Regarding the link between industry specialization and the scope of an investor’s investments, Cumming and Dai (2010) studied a sample of US investments and found a negative relationship between industry specialization in tech industries and the geographic scope of investments. However, their results do not reveal any findings about other industries, as they only account for specialization in tech industries, and the findings are also not generalizable to the international market. 894 A.Nörthemann 1 3 Given the assumption that industry specialization is relevant for successful investments, especially under the difficult conditions of cross-border investment, the role of industry specialization should be addressed more specifically. This paper provides more nuanced findings about the extent to which industry specialization affects firms’ decisions to invest abroad, incorporating their level of specialization over time. Existing research highlights how institutional target market characteristics play a major role in attracting international venture capital. Thus, it can be expected that the institutional view and a firm’s internal view are not independent of each other. Therefore, institutional characteristics are included as potential moderators for the impact of industry specialization on the likelihood of investing abroad. Regarding institutional characteristics, this paper focuses on the role of start-up hubs in the context of industry specialization and internationalization. Literature on urban economics highlights the role of start-up and venture capital clusters and density in fostering innovation and entrepreneurship (Florida and Mellander 2016). These clusters are characterized by evolved entrepreneurial ecosystems consisting of dense networks connecting research to commercial products and services, high levels of entrepreneurial activity, and high availability of venture funding sources (Chen etal. 2010). Investors and portfolio companies often locate themselves in start-up hubs, where they can benefit from the network. However, due to start-up hubs’ networkand value-enhancing impacts on portfolio companies, investors’ specializations and advice may be less needed. In addition, from a venture capitalist’s point of view, investing in a portfolio company located in a start-up hub may be accompanied by higher costs due to higher rents or wages. Furthermore, in start-up hub regions, competition for attractive investment opportunities may be higher. Considering these costs, it is questionable whether specialized investors, in particular, are inclined to follow the trend of investing in start-up hubs, especially as they are less dependent on the supporting effect of these hubs. Furthermore, portfolio companies’ attributes are likely to impact venture capitalists’ investment decisions. One strategy used by venture capitalists to decrease investment risks when investing abroad includes selecting portfolio companies with lower ex-ante information asymmetries (Devigne etal. 2018). This may be possible for investments in a later round (Schertler and Tykvová 2012), in companies that have received higher funding or with the increasing age of the portfolio company. Accordingly, these three portfolio company characteristics are included as moderators in the analyses. To examine these issues, this study uses a data set containing detailed information about venture capital firms and corresponding portfolio companies at the deal level from 2001 to 2019. The data set includes 46,525 initial investment decisions into a portfolio company with a global scope. The data are evaluated at the level of the firms’ investment level, considering the individual decisions made in different firms’ portfolios within a 5-year rolling window. The effects of venture capitalists’ industry specializations are measured in a binomial logistic regression model. This study shows that venture capitalists with higher levels of industry specialization have a significantly higher probability of investing cross-border than venture capitalists with lower levels of industry specialization. Moreover, this effect 895 1 3 Industry‑specific specialization inventure capitalists’… is negatively moderated by the portfolio company being located in a start-up hub, and for companies with higher funding to date, which may reduce the importance of venture capitalists’ capabilities in international investment decisions. The effect is positively moderated by the age of the portfolio company. The study makes several contributions to the understanding of venture capital firms’ international investment behaviours. By examining the role of firms’ industry specialization in international investments, the study explains firm behaviour at the micro level. In contrast to studies drawing on network theory (Patzelt etal. 2009; De Prijcker etal. 2012; Vedula and Matusik 2017), which describe firm characteristics depending on the firm’s partners, this paper offers alternative explanations for investors’ behaviours by focusing primarily on the investors’ own capabilities and their development through learning effects and specialization. Thus, the results add to the literature on international venture capital that refers to firm-level determinants (e.g., Cumming and Dai 2010; Gupta and Sapienza 1992; Cumming and Dai 2010; Vedula and Matusik 2017) of cross-border investments. By interacting firm-specific characteristics with institutional-level and companylevel characteristics, this study also provides insight into the interplay of firm-specific and external criteria, as they may not operate independently (Vanacker etal. 2014; Devigne et al. 2018). This allows for more precise conclusions about the investors’ circumstances, including internal and external determinants, and how they affect investment decision behaviours in international markets. Lastly, this study uses a global data set and therefore provides indications about investors’ behaviours on a global level. The remainder of this study is organized as follows. Section2 describes theoretical considerations and develops the hypotheses, and Sect.3 details the data set and variables. Section4 presents the results of the descriptive and multivariate analyses, Sect.5 discusses the results and Sect.6 provides the conclusions. 2 Theoretical considerations anddevelopment ofhypotheses 2.1 Institutional drivers ofinternational venture capital investments Previous research examines several drivers of venture capital’s cross-border capital flows. These drivers mainly relate to the characteristics of the institutional environment and the country in which the investment is made. Investors prefer to invest in countries with highly developed institutional environments since, from an investor’s point of view, this implies greater market transparency and regulatory stability (Groh etal. 2010; Guler and Guillen 2010; Aizenman and Kendall 2012). In particular, the literature mentions that strong legal, financial, and political institutions facilitate cross-border venture capital flows (Devigne etal. 2018). Previous literature also reports that venture capitalists prefer investing in countries with cultural similarities to their own countries (Li et al. 2014; Dai and Nahata 2016). These similarities may include a common language, colonial ties (Aizenman and Kendall 2012), or economic integration between the countries (for example, the European Union) (Alhorr et al. 2008). With increasing cultural similarities, communication 896 A.Nörthemann 1 3 and trust between the participating parties—in this case, the investor and the portfolio company—increase (Sorenson and Stuart 2001). Hence, the investor’s liability of foreignness decreases when investing in a country with cultural similarities to the investor’s own country. Furthermore, studies have found that the economic growth of the target market is a driver of international venture capital (Groh etal. 2010; Schertler and Tykvová 2011; Aizenman and Kendall 2012). Economic growth within a country can be represented by a dynamic development of entrepreneurial ecosystems, which may foster the creation of new companies (Neck etal. 2004). An entrepreneurial ecosystem is a system of interactions between individuals and organizations—for example, financial intermediaries, research institutions, suppliers, customers, and the government (Colombo etal. 2019a, b). It thus comprises the area in which start-ups are established. From the perspective of a foreign investor, this leads to attractive investment opportunities, particularly due to the framework created by a strong institutional environment for investors (Mack and Mayer 2016). In addition, constant low interest rates in global markets may have led to an increase in the popularity of venture capital as an asset class. This may have resulted in increased competition for interesting and promising investment opportunities, encouraging investors to explore international markets. Further, the amount of ‘dry powder’, defined as venture capitalists’ committed but unallocated capital on hand, has increased in the last years, which may have forced investors to seek investment opportunities beyond their national borders. 2.2 Firm‑specific drivers andtheimportance ofindustry specialization Despite the aforementioned drivers of international venture capital investments, comprising strong institutions and commonalities between countries, it is nevertheless argued that compared to domestic deals, international venture capital deals are characterized by higher information asymmetries and additional risks. These risks arise from the geographical, cultural, and institutional distance between the investor and the portfolio company (Lockett and Wright 2002; Pruthi etal. 2003; Wright etal. 2005; Devigne etal. 2016; Devigne etal. 2018). These risks are described as the liability of foreignness, which captures all additional costs arising for the investor that would not occur in a domestic deal (Zaheer 1995). This perspective, however, tends to focus on the institution-based view (Peng 2002). Likewise, the drivers of international venture capital flows mainly relate to institutional differences. At this point, the supplementary view focusing on the characteristics and capabilities of investors, following the resource-based view by Wernerfelt (1984), is missing. Under the circumstances of an international investment with high information asymmetries, an investor’s firm-specific knowledge can represent a competitive advantage relative to other investors who do not possess these characteristics. Some previous findings examine the probability of investing abroad according to investor type. For example, some studies show that corporate venture capitalists exhibit a broader geographic scope, as they invest in companies from which their corporation can derive a strategic advantage. Such target companies require a certain 897 1 3 Industry‑specific specialization inventure capitalists’… fit with the parent company’s product and service offerings so the corporate investor can access the target’s technologies and knowledge. From a strategic point of view, limiting investment opportunities to the domestic market would reduce the number of target companies that fit the investor’s product (Gupta and Sapienza 1992; Bertoni etal. 2015). Furthermore, existing literature describes the decision on whether to internationalize from the perspective of an optimal portfolio allocation. Investing in different countries leads to geographic portfolio diversification and therefore may reduce an investor’s overall investment risk (Knill 2009; Humphery-Jenner and Suchard 2013). In addition, several studies consider the characteristics of an investor’s human capital (De Prijcker etal. 2012; Devigne etal. 2018). Regarding the international investment expertise of venture capitalists’ investment managers, there is evidence that more experienced managers are more likely to invest abroad (Schertler and Tykvová 2011). This is substantiated by their superior knowledge of the institutional environment and better access to networks (Devigne et al. 2018). In terms of an investor’s social capital (e.g., networks), research shows that venture capitalists’ social networks impact their international investment behaviours. To share the risks accompanying international investments, venture capitalists often decide not to invest in stand-alone deals rather than syndicates (Tykvová and Schertler 2014; Chemmanur etal. 2016). This practice enables venture capitalists to pool resources for monitoring, supporting and exiting their portfolio companies in international markets. In this context, local investors play a special role, as they are not affected by the liability of foreignness, and they know the local markets and behaviours. Hence, venture capitalists syndicating with local investors can solve problems arising abroad with the help of local partners (Sorenson and Stuart 2001; Cumming and Dai 2010; Vedula and Matusik 2017). Rather than addressing the capabilities of external partners, this paper presents the firm-specific characteristics that influence internationalization decisions. This focus is meaningful because firms’ characteristics determine their success in enhancing their portfolio companies’ value and internal efficiency (Mahoney and Pandian 1992; Hart 1995; Abell and Nisar 2007). These characteristics include the investors’ level of industry-specific knowledge on technological product development and access to key resources like personnel, raw materials, and distribution channels— what could be considered their level of industry specialization (Gupta and Sapienza 1992). Industry specialization is not static; rather, organizations acquire it through experience and practice in the form of tacit knowledge (Itami and Roehl 1991; Hart 1995). Firms can learn directly from their investment experience and thus develop specialized knowledge (Gupta and Sapienza 1992; Liu and Maula 2016). From venture capitalists’ point of view, the provision of industryand product-specific support and advice to portfolio companies is likely to be facilitated if firms have already developed their industry specialization in the past (Zhang and Pezeshkan 2016). Specialization development may help investors learn about the characteristics and behaviours of markets and their participants in a specific field of business, which is not limited to the domestic market but can also be applied globally. One the one hand, a higher level of specialization likely attenuates an investor’s liability of foreignness as the benefits of specialization in the market exceed the risks 898 A.Nörthemann 1 3 of cross-border investment. Through a high degree of industry specialization, the insecurity linked to investment abroad may be reduced by industry-specific expertise accumulated through similar previous investments. Therefore, it is assumed that industry specialization facilitates investments abroad. On the other hand, one could also argue that investors with increasing levels of industry specialization face fewer investment opportunities in their home markets if they want to continue investing in similar business models. Investment opportunities matching an investor’s industry specialization may not be available in the domestic market, resulting in a push effect to invest abroad. In summary, industry specialization may be a vehicle for reducing the liability of foreignness and facilitating investments abroad. In addition, international investments may result from an investor’s industry specialization. Consequently, the first hypothesis is as follows: Hypothesis 1: Venture capitalists’ degree of specialization is positively related to the probability of internationalization. 2.3 Market‑level andportfolio company‑level determinants influencing venture capitalists’ decisions toinvest abroad Beyond venture capitalists’ industry specialization, a considerable body of research examines the mechanisms for overcoming information asymmetries within foreign markets. One strategy is observing and interpreting the determinants of the market and the portfolio company (Valliere 2012). Regarding the determinants of the target market, the concept of the entrepreneurial ecosystem is widely used to represent increased entrepreneurship and growth in a certain region (Cohen 2006; Stam 2015; Spigel 2017). Entrepreneurial ecosystems are networks of social, cultural, political and economic elements in a region that contribute to the competitiveness and success of new ventures within the system (Dubini 1989; Spigel 2017). This is because favourable institutional conditions for growth are created in a developed entrepreneurial ecosystem: ease of firm communication and cooperation (Gertler 2003), wide social networks that create pathways for knowledge spillover (Powell etal. 2005), and strong connections between founders and founding sources (Powell etal. 2002). Evolved entrepreneurial ecosystems are present in start-up hubs, where the density of innovative companies, networks, and venture funding sources is high. From a venture capital investor’s point of view, the risk of a cross-border deal may be evaluated as lower if the investment occurs in an evolved entrepreneurial ecosystem. Thus, an evolved entrepreneurial ecosystem may act as a quality signal for the target market. Thereby reducing information asymmetries. In such regions, the value and success stories of other investors already exist, which venture capital firms can draw on in their decision-making. This ecosystem is missing in regions where only a few deals have been executed. In sum, I assume that due to entrepreneurial ecosystems’ supporting and assisting effects, an investor’s industry specialization is less essential because it is locally available. Therefore, investors are not compelled to rely only on their own expertise. Under the assumption that venture capitalists assess an entrepreneurial ecosystem as highly developed, it may be that less specialized investors are more 905 1 3 Industry‑specific specialization inventure capitalists’… measured against its previous investments before the deal under consideration. The measure is calculated using a 5-year rolling window as the timespan and, therefore, the number of deals increases over time. Since new investment firms entering the sample will have no experience at all due to their missing history in the sample. Therefore, the first 5 years of the data set and the first 5 years of a venture capital firm’s lifetime are not included in the analyses. Industry specialization equals zero if a firm has no experience in the industry of the deal being investigated. Industry specialization can take a maximal value of one if all deals a firm has made within the timespan of the rolling window can be assigned to the industry of the deal under consideration to date. The measurement of Industry specialization incorporates learning effects by assuming that with each additional deal within an industry, a firm’s experience increases through successfully supporting companies in that industry in the market. Therefore, for the first deal in a portfolio, the variable industry specialization always equals zero, as no learning effects can occur until a firm’s first investment. Industry classification within the sample was conducted by Refinitiv Eikon Industry Minor Classification, and this classification has been adopted by this study. The specialization categories include biotechnology, communication and media, computer hardware, computer software and services, consumer-related, industrial/ energy, internet-specific, medical/health, other products, and semiconductors. Four different moderators referring to the portfolio company and the corresponding target market are considered. All moderators should illustrate the strength of the information asymmetries between the venture capital firm and the portfolio company. The first moderator is a Start-up hub dummy, which measures whether the region of the portfolio company’s headquarters is known for supporting the creation and growth of new ventures. Start-up hub dummy equals one if the portfolio company is located in a start-up hub and zero otherwise. Start-up hotspots were identified based on a data set of all tracked venture capital deals in the Refinitiv Eikon platform from 2001 to 2019. Cities with the most start-up companies in the corresponding year were labelled as start-up hotspots. For this purpose, the companies per city and year were counted, and those cities with values above the 75 percent quartile were marked as hotspots. In addition to examining institutional factors, the study also examines known indicators of portfolio companies’ financing histories that may moderate the influence of industry specialization on decisions to invest abroad. The round in which the investment takes place (funding round) takes the value of the respective investment round. Log (company funding to date) is the logarithm of the financing in US dollars received by a portfolio company from all investors up to the respective investment date. Lastly, the age of the portfolio company Log (Age of portfolio company), is included in the models. The age of the portfolio company is measured as the logarithm of the difference between the founding year and the year of the investment decision. 906 A.Nörthemann 1 3 3.4 Control variables Several control variables are included in the multivariate analyses. It is likely that in addition to industry specialization, knowledge of other institutional environments and cultures is also necessary; thus, the international investment experience is controlled for (Gupta and Sapienza 1992; Cumming and Dai 2010). International investment experience influences investment decisions because experienced investors are more familiar with foreign institutional and legal environments and have better access to international networks (Schertler and Tykvová 2011; De Prijcker etal. 2012; Devigne etal. 2018). In addition to knowledge of the particularities of international markets, the investors’ experience in domestic markets also influences their investment decisions. For example, through experience in domestic markets, investors gain skills in structuring deals and monitoring investments (Gompers etal. 2009; Wang and Wang 2011). Both international and domestic experience are measured by the logarithm of the number of deals a firm made prior to the year of the deal under consideration. Furthermore, the age of the firm (age of firm) as well as the size of the firm (firm size), based on the venture capital firm’s assets under management, are included as control variables. Age of firm is measured as the logarithmic value of the difference between the date of the establishment of the firm and the date of the investment. Particularly young ventures imply a higher risk associated with investment than older companies, and venture capitalists may be affected by this risk in their investment decisions (Zhang and Pezeshkan 2016). In addition to the characteristics of investment firms and portfolio companies, the study also includes other structural and institutional characteristics of the target market. In addition to the company’s industry specialization, the availability of promising investment alternatives in the investor’s home country could also play a role in an investor’s decision to internationalize. Thus, I add a control variable which considers this factor. The variable Local demand measures the number of venture capital deals in the industry of the deal under consideration in the home country of the venture capital firm prior to the year before the respective deal. The efficiency of the legal system (efficiency legal system) impacts venture capital investments through more assertive mechanisms for solving agency and control problems (Cumming etal. 2010). The analyses in this study use the index created by Porta etal. (1998) to illustrate legal efficiency. Efficiency legal system can take values from zero to ten, with low scores indicating low efficiency. Beyond the aforementioned formal institutions, investment behaviours are also likely to be influenced by informal institutions such as cultural heritage (Bruton etal. 2005; Gantenbein etal. 2019). People speaking the same language or sharing the same religion often share similar values (Gantenbein etal. 2019); therefore, some authors have used a country’s language and religion as proxies for culture (Grinblatt and Keloharju 2000; Guiso etal. 2006). To control for cultural differences between the investment nation and the nation of the portfolio company, two dummy variables that equal one if the two countries share the same language or religion are included in the models. The variables cover cultural differences among the main languages and religions of the data set. The main official languages of firm nations and 907 1 3 Industry‑specific specialization inventure capitalists’… target nations are Arab, English, French, German, Spanish, and Portuguese. Data about countries’ official languages are taken from the CIA World Factbook. The religion dummy variable covers Buddhism, Christianity, Hinduism, and Islam. Data on countries’ denominations are also taken from the CIA World Factbook. Since languages or beliefs that are only represented in one country act as a fixed effect for that country, these could not be integrated. To further control for country-specific cultural peculiarities, in particular, those related to entrepreneurship and financing, the global entrepreneurship score developed by Acs etal. (2017) is included in the models. The global entrepreneurship score measures the health of the entrepreneurship ecosystem annually and is provided by the global entrepreneurship development institute (GEDI). GEDI collects data on the entrepreneurial attitudes, abilities, and aspirations of local populations and weights these against the prevailing social and economic infrastructure of a country. The score consists of various components, for example, the average of a population’s willingness to take risks, the quality of a country’s human capital, and a country’s financial openness. Year and legal effects are also included in the analyses. Legal classification fixed effects capture the legal classification of the target nation based on the ‘Legal Classification of Investment Nation’ by Porta etal. (1998). Countries are classified as French, English, German, Scandinavian or Socialist. 3.5 Descriptive statistics In the sample, 8855 deals are labelled cross-border deals, and 37,670 are labelled domestic deals. Therefore, almost 20% of all deals in the sample are characterized as cross-border deals. On average, the firms’ industry specialization is 0.286, meaning that on average, 28.6% of the firms’ portfolio investments at the time of the investment decision can be assigned to the industry of the company in which the investment is made. Therefore, on average, firms do not invest in a company without any prior industry experience. The maximum industry specialization in the sample is 1, and the minimum value is no industry specialization at all. Most deals can be classified into the categories of computer software and services (33.99%), internet-specific (26.7%), biotechnology (8.91%), and medical/health (8.16%). The communications and media category has the fewest deals (2.67%). A detailed overview of the firms’ industry specializations is displayed in Table4. Slightly less than 30% of the deals included in the sample are located in a start-up hotspot (mean = 0.293). The average funding a portfolio company received (measured in logarithmic values) at the date of the investment of the firm under consideration is 16.746 US dollars (SD = 1.76). Firms are on average 2.403years old and at most 5.13years old at the time of the investments (also measured in logarithmic values). The average logarithmic age of the portfolio companies is 1.26years and the maximum age is 5.537years. The earliest investments into portfolio companies occur in the year of establishment. On average, investments into portfolio companies occurred after two rounds (SD = 1.701). The mean of the number of investment rounds seems relatively small. However, this may be because the data set only 908 A.Nörthemann 1 3 includes initial investments in companies and does not include follow-on investments in later rounds. Before the investments, venture capital firms had executed an average of 2.69 deals in their home countries and 1.68 deals abroad. The local demand for venture capital financing in the specific industry under consideration in the investor’s home market is on average 0.208. This means, that in the investors’ home country, on average 20.8% of the deals in the previous year were made in the sector of the deal under observation. Most deals occur in countries with legal systems that are considered efficient (mean = 9.64). The lowest efficiency score for a legal system is 2.5 points and the highest is 10 points, the maximum efficiency. Similar patterns can also be observed regarding the financial openness of the target markets. The average financial openness of the target markets is 0.954 points, with 1 as the maximum score and 0 as the lowest score. Investment deals primarily occur in countries with the same official language (mean = 0.834) and the same religious orientation (mean = 0.924). Deals also mainly occur in countries with high global entrepreneurship scores (mean = 74.681). The countries with the lowest global entrepreneurship are assigned a score of 11.8, while some countries reach maximum scores of 90.23. 3.6 Model specification Within the framework of multivariate analysis, this paper examines if and to what extent a firm’s industry specialization impacts its international investment decisions. Furthermore, this work analyzes the extent to which characteristics of the portfolio company and the corresponding target market influence the relationship between a Table 4 Industry specialization Industries in which firms under consideration invest and by which the industry specialization variable is measured on the basis of previous investments within the industry to which the deal under consideration is allocated using a 5-year-rolling-window. Frequency and percentage indicate in which category investments were made in the used data set. Industry classification is done by Refinitiv Eikon Industry Minor Classification Variable Industry specialization option Frequency Percent Industry specialization Biotechnology 4145 8.91 Communication and media 1243 2.67 Computer hardware 1364 2.93 Computer software and service 15,814 33.99 Consumer related 1681 3.61 Industrial/Energy 2019 4.34 Internet specific 12,423 26.70 Medical/Health 3796 8.16 Other products 2413 5.19 Semiconductors 1627 3.50 Total 46,525 100.00 909 1 3 Industry‑specific specialization inventure capitalists’… firm’s industry specialization and the decision to invest abroad. Binomial logistic regression models are conducted to investigate the relationship between a firm’s industry specialization and internationalization decisions. Therefore, the dependent variable P (Cross-border deal = 1) indicates the probability of a cross-border deal. The base model specification, investigating the isolated effect of a firm’s industry specialization on the probability of investing abroad, is: (1) P(Cross −border deal =1) i =𝛽 0 +𝛽 1 Industry specialization +𝛽 2 Local demand +𝛽3Log(Domestic experience)+𝛽4Log(International experience ) +𝛽5Log(Age of firm)+𝛽6Log(Size of firm) +𝛽7Global entrepreneurship score +𝛽8Efficiency legal system +𝛽9Financial openness +𝛽10Same religion dummy +𝛽11Same language dummy +Legal effects +Year effects +𝜀i where investment decisions are indexed by i. To avoid biased regression estimates, all regression models control for possible cross-correlation effects arising from unobserved individual firm effects among all deals made by a respective firm. Therefore, clustered standard errors are used. Pearson correlations of all variables included in the models are displayed in Table5. As variance inflation factors (VIFs) do not exceed values of 6.11 (see also Table 5), there is no evidence of multicollinearity. The models analyzing the interaction effects have the same model specification, including the respective additional interaction effect. All interaction effects relate to characteristics reflecting informational asymmetries between the venture capital firm and the portfolio company, which I expect to moderate the impact that industry specialization has on the investor’s internationalization decision. Model 2 includes the moderating effect of the portfolio company being located in a start-up hub on the impact that industry specialization has on the probability a firm invests abroad. The model specification is the following: Model 3 analyzes the moderating effect of the funding round on the impact that industry specialization has on the probability a firm invests abroad. The model specification is as follows: (2) P(Cross −border deal =1) i =𝛽 0 +𝛽 1 Industry specialization +𝛽 2 Startup hub +𝛽3Industry specialization ×Startup hub +𝛿Controls i + Legal effects + Year effects +𝜀 i (3) P(Cross −border deal =1) i =𝛽 0 +𝛽 1 Industry specialization +𝛽2Funding round +𝛽3 Industry specialization ×Funding round +𝛿Controlsi+Legal effects +Year effects +𝜀i 910 A.Nörthemann 1 3 Table 5 Pairwise correlations and variance inflation factors This table shows variance inflation factors (VIFs) and pairwise correlations for the variables used in the base model. Significance is marked by * at 5% Variables VIF (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) Cross-border deal 1.000 (2) Industryspecialization_rw_5y 1.22 0.014* 1.000 (3) Local demand 1.41 0.022* 0.408* 1.000 (4) Log(Domestic investment experience) 2.76 − 0.139* 0.059* 0.011* 1.000 (5) Log(Internat. investment experience) 2.00 0.282* 0.032* 0.054* 0.563* 1.000 (6) Log(Age of firm) 1.96 0.022* − 0.021* − 0.035* 0.663* 0.508* 1.000 (7) Log(Size of firm) 1.79 0.033* 0.016* − 0.008* 0.588* 0.543* 0.422* 1.000 (8) Global entrepreneurship score (target market) 6.11 − 0.337* 0.065* 0.014* 0.165* − 0.084* 0.019* 0.055* 1.000 (9) Efficiency legal system (target market) 4.12 − 0.251* 0.040* − 0.013* 0.134* − 0.038* 0.036* 0.084* 0.759* 1.000 (10) Financial openness (target market) 4.50 − 0.234* 0.018* − 0.041* 0.095* − 0.066* 0.034* 0.011* 0.780* 0.618* 1.000 (11) Same religion dummy 1.64 − 0.515* 0.011* − 0.032* 0.095* − 0.180* − 0.023* − 0.039* 0.384* 0.181* 0.316* 1.000 (12) Same language dummy 2.20 − 0.552* 0.035* 0.017* 0.210* − 0.148* − 0.010* 0.079* 0.407* 0.325* 0.142* 0.391* 1.000 Mean VIF 2.71 911 1 3 Industry‑specific specialization inventure capitalists’… Model 4 analyzes the moderating effect of the portfolio company’s funding on the impact that industry specialization has on the probability that a firm invests abroad. The model specification is as follows: Model 5 analyzes the moderating effect of the portfolio company’s age on the impact that industry specialization has on the probability a firm invests abroad. The model specification is as follows: 4 Empirical results The results show a significant positive relationship between venture capitalists’ industry specialization and the probability of investing abroad (see Table6). Since the logit regression model is nonlinear, the effect of industry specialization on the probability of a cross-border deal is not the same at every level of industry specialization (see Fig.1). Hence, to better understand the main effect, the probability of a cross-border deal depending on the level of industry specialization is calculated for the mean of industry specialization, as well as the mean of industry specialization plus/minus one standard deviation, while all other independent variables of the model are held at means. If the venture capitalist’s industry specialization increases by about one standard deviation from the mean, the probability that the venture capital investor will decide on a cross-border deal increases by about 1.6%. In comparison, the probability of a venture capital firm executing a cross-border deal with a specialization value of the mean minus one standard deviation decreases by about 1.4% from the mean. These findings underline the assumption that the higher the investor’s industry specialization, the more likely investors’ perceived insecurity linked to a cross-border investment may be reduced. Therefore, the first hypothesis saying that the venture capital firm’s degree of specialization is positively related to the probability of internationalization can be confirmed. Regarding the control variables, results indicate that venture capital investors are less likely to invest abroad the higher the demand for venture capital financing (4) P(Cross −border deal =1) i =𝛽 0 +𝛽 1 Industry specialization +𝛽2Log(Company funding to date) +𝛽3Industry specialization ×Log(Company funding to date)+𝛿Controls i + Legal effects + Year effects + 𝜀i (5) P(Cross −border deal =1) i =𝛽 0 +𝛽 1 Industry specialization +𝛽2Log(Age of portfolio company) +𝛽3Industry specialization ×Log(Age of portfolio company)+𝛿Controls i +Legal effects +Year effects +𝜀i 912 A.Nörthemann 1 3 Table 6 Logistic regression using robust standard errors Probability of a cross-border deal Model 1 Model 2 Model 3 Model 4 Model 5 Independent variable Industry specialization_rw_5y 0.614*** 0.736*** 0.647*** 2.909*** 0.396** (0.098) (0.107) (0.125) (0.785) (0.157) Start-up hub dummy 0.0891 (0.088) Industry specialization_rw_5y * Start-up hub dummy − 0.608*** (0.173) Funding round 0.115*** (0.0163) Industry specialization_rw_5y * Funding round − 0.030 (0.036) Log (Company funding to date) 0.156*** (0.022) Industry specialization_rw_5y * Log (company funding to date) − 0.137*** (0.0459) Log (Age portfolio company) 0.186** (0.092) Industry specialization_rw_5y * Log (age portfolio company) 5.346*** (1.043) Control variables Local demand − 1.020*** − 0.971*** − 0.975*** − 1.016*** − 0.813*** (0.205) (0.207) (0.205) (0.218) (0.213) Log (Domestic investment experience) − 0.451*** − 0.449*** − 0.439*** − 0.437*** − 0.437*** (0.034) (0.034) (0.034) (0.035) (0.036) Log (Internat. investment experience) 0.686*** 0.687*** 0.687*** 0.671*** 0.695*** (0.038) (0.038) (0.038) (0.039) (0.039) Log (Age of firm) − 0.087* − 0.090* − 0.107** − 0.092* − 0.135** (0.051) (0.052) (0.052) (0.054) (0.055) 913 1 3 Industry‑specific specialization inventure capitalists’… Table 6 (continued) Probability of a cross-border deal Model 1 Model 2 Model 3 Model 4 Model 5 Log (Size of firm) 0.073*** 0.073*** 0.063** 0.038 0.073*** (0.025) (0.025) (0.025) (0.025) (0.027) Global entrepreneurship score (target market) 0.011 0.011 0.009 0.010 0.008 (0.008) (0.008) (0.008) (0.009) (0.008) Efficiency legal system (target market) − 0.416*** − 0.414*** − 0.412*** − 0.521*** − 0.462*** (0.097) (0.097) (0.097) (0.111) (0.104) Financial openness (target market) − 0.196 − 0.205 − 0.159 − 0.038 0.162 (0.502) (0.501) (0.501) (0.554) (0.546) Same religion dummy − 2.777*** − 2.769*** − 2.792*** − 2.717*** − 2.712*** (0.332) (0.330) (0.330) (0.337) (0.335) Same language dummy − 2.727*** − 2.732*** − 2.721*** − 2.885*** − 2.790*** (0.194) (0.195) (0.195) (0.199) (0.192) Legal effects (target market) Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Intercept 5.139*** 5.080*** 5.197*** 4.274*** 5.346*** (0.973) (0.967) (0.967) (1.056) (1.043) Number of observations 46,525 46,525 46,525 42,532 34,011 Pseudo R-squared 0.4441 0.4454 0.4469 0.4567 0.4430 Binomial logistic regression results: The impact of industry specialization of venture capital investments on the probability of investing abroad in a worldwide sample of venture capital deals from 2001 to 2019 Results show a binomial logistic regression with the probability of investing domestic or cross-border as the dependent variable. As the independent variable, firm’s proportional industry specialization as measured by its past investments within a 5-year-rolling-window and their industry classifications is used. Values can therefore range from 0 to 1. Interactions are performed with variables that are expected, indicating the degree of information asymmetries between the portfolio company and the investment firm. As other independent variables, several measures referring to the institutional environment of the investment and the experience of the investing firms were included. Heteroscedasticity robust standard errors are in parentheses. Significance at the 1%, 5%, and 10% levels is denoted by ***, ** and *, respectively. Standard errors are clustered by venture capital firms 914 A.Nörthemann 1 3 in their home countries. Local demand is significantly negative correlated with the probability of a cross-border deal. In line with previous literature, it can be seen that the international investment experience of the investor has a positive influence on the decision to invest abroad. On the opposite, the likelihood of a cross-border deal decreases with higher experience in the domestic market. Larger investment firms are also more likely to invest abroad. It is also apparent that older investment companies are more likely to invest domestically. Referring to the characteristics of the target market, results show that the probability of a cross-border deal decreases with the increasing efficiency of the legal system abroad. The financial openness of the target country has no significant impact on the likelihood of investing abroad. In addition, the entrepreneurship index of the target market indicates a positive but insignificant correlation with the likelihood of investing abroad. Investing in a country with the same language or religion is negatively correlated with the probability of a cross-border deal. Regarding the moderation effects, as hypothesized in H2a, the assumption can also be confirmed that the probability of a cross-border deal depending on a firm’s industry specialization is attenuated by the portfolio company being located in a start-up hotspot. To aid interpretation, the effect of industry specialization on the likelihood of a cross-border deal is illustrated graphically (see Fig.2a). Figure2a compares the interaction effect of the start-up hub dummy on the relationship between industry specialization and the probability of a cross-border deal while keeping all other variables at means among the two possible scenarios of the dummy variable. The red curve shows the effect of industry specialization on Pr (cross-border investment) = 1 for investments where the portfolio company under consideration is located in a start-up hub. The blue curve shows the effect of industry specialization on Pr (cross-border investment) = 1 for investments in which the portfolio company under consideration is not located in a start-up hub. For the cases where Fig. 1 Effect of industry specialization on the probability of a cross-border investment. Note: figure presents the calculated predictions of Model 1 (Table6). I show predictive margins with 95% confidence intervals 921 1 3 Industry‑specific specialization inventure capitalists’… Christian investors investing in non-English-speaking and non-Christian environments where culturally similar investors are outnumbered. A reason for this might be that the availability of equity financing opportunities is still limited in most countries. Venture capital investors are mainly located in financial centres and high-tech regions (Colombo etal. 2019a, b), which are prevalent in English-speaking, Christian countries. Even if portfolio companies from other regions globally would prefer investors from the same cultural background, it might be that there are no such investors available. Referring to the moderation effects, results indicate that the positive relationship between industry specialization and the probability of a cross-border deal is moderated by the portfolio company’s location. If the portfolio company under consideration is located in a start-up hub, the effect of industry specialization on the probability of a cross-border deal is weakened. This impact may stem from the supporting and assisting effects of entrepreneurial ecosystems (Gertler 2003; Powell etal. 2005), which attenuate the need for investors to employ tacit knowledge. Consequently, the decisions of investors to deploy capital are less tied to their industry specialization and former learning. Furthermore, start-up hubs are characterized by a high density of investors, which may reduce the likelihood of foreign investor participation. Specialized investors may be more likely to participate in deals attracting less attention and being located outside of start-up hubs but still fit their industry specialization. On the other hand, an investor’s specialization may become more important if supportive conditions in the target company’s region are less given, which could explain the reinforcing effect. I could not find a moderating effect of the number of funding rounds on the relationship between industry specialization and internationality. Although it is known from previous literature that each additional funding round carries certification effects from previous rounds (Ruhnka and Young 1987; Gompers 1995; Wang and Zhou 2004), thus lowering information asymmetries, this might not be true for all business models. This might be especially true for research-intensive products whose development phase is longer and comprises more financing rounds than, for example, consumer goods. It may be that there is still as much expertise needed in later phases, as decisions are equally complex and specific. In addition, if such companies want to grow and expand their operations, high investment sums are required (Aernoudt 2017). The financial risk of investments in later rounds in such cases is high, which also tends to suggest that specific industry knowledge is needed at least to the same extent as in early rounds. Furthermore, investments might become more complex with each additional round. It is likely that the number of other investors already involved in the company, is higher for investments in higher rounds. For the new investor, this results in new information asymmetries including not only the portfolio company itself, but also other stakeholders and their specific goals. I also found that the positive relation between industry specialization and internationality is weaker if the portfolio company under consideration has received higher amounts of funding before the date of the investment decision. One reason for this effect may be that higher funding indicates a portfolio company’s heterogeneity in terms of social capital and associated capabilities (Hsu 2007). If capabilities and specific experience are already present in the portfolio company, the investor’s 922 A.Nörthemann 1 3 support in this respect is less necessary. Investors with a lower level of specialization are less likely to invest abroad if the company has received less financing, and it may be that this effect is less meaningful for specialized investors since they want to make a greater contribution to the financing and further development of the company. Contrary to the assumption that the effect of industry specialization on the probability of investing abroad is less strong for older companies, the results show that the opposite effect occurs. Industry specialization gains more weight the older the portfolio companies are, perhaps due to two reasons. First, older companies are likely to have a more complex business. Products and processes that are already established are more difficult to change or improve. Therefore, more specific experience might be needed to successfully develop older companies and increase their value. Second, companies in later growth phases are also faced with different decisions than companies that are still in a very early financing phase. These decisions might include whether to enter additional markets, introduce new product lines or expand abroad. Industry specialization might be an important success factor in achieving these goals. 6 Conclusion This paper analyzes the effects of industry specialization on venture capital firms’ decisions to invest abroad. Furthermore, the study also investigated the interplay between firms’ industry specializations and the characteristics of the target markets, as well as the financing-specific characteristics of portfolio companies. The results show that the probability of investing abroad increases with higher degrees of industry specialization even though international deals are often associated with higher risk. This relationship illustrates how the liability of foreignness has a varying influence on an investor’s investment decision depending on the investor’s specialization in the specific industry to which the deal is assigned. Thus, the results contribute to previous research on strategies to compensate for the liability of foreignness (Devigne etal. 2018). The study provides new insights into the decision-making behaviours of venture capital firms. First, there is evidence that in addition to the frequently studied country-level or network determinants (De Prijcker etal. 2012; Vedula and Matusik 2017), firm-level determinants such as industry specialization also play a role in investment decisions. Therefore, the literature is enriched by analyzing firms primarily based on their own capabilities. Second, by interacting the firm-level characteristic of industry specialization with countryand company-level characteristics, this study adds to the literature by examining the interplay of firm-level determinants, target market determinants and portfolio company-level determinants in investment decisions, as these may not operate independently (Vanacker etal. 2014; Devigne etal. 2018). If exante information asymmetries are lower—for instance, due to an evolved entrepreneurial ecosystem or when investing in companies that received higher funding to date—the importance of a company’s specialization for internationalization decisions decreases. 923 1 3 Industry‑specific specialization inventure capitalists’… Therefore, these findings refine the investigation of factors influencing the decisionmaking behaviours of venture capital investors in an international context. This study is subject to some limitations. Within this framework, industry specialization is considered to be equally relevant in all industries. However, it may be that industry specialization is more important, for example, in research-intensive industries or for complex business models than for accessible business models. Furthermore, such a finding would mean that investors’ specialization processes are slower in more complex industries than others. Therefore, it might be useful to score the provided industry specializations differently according to their complexity. In addition, this study assumes that the structure of a venture capital firm does not change during the period under consideration. However, investment decisions may also be driven by personnel changes, specific knowledge about industries and markets or ties to a foreign country. It would also be interesting to investigate whether venture capital firms have local offices in the target countries from which they could more easily contact the target company and intensify their relationship with the specific country. Local offices are also likely to positively influence investors’ internationalization decisions. Likewise, it would be interesting to further examine syndication in the context of internationalization and industry specialization. As various partners in the syndicate bring different skills and capabilities to the table, it might be worth exploring which skills complement each other to what extent and how different skills and their composition in the syndicate affect firms’ internationalization decisions. From a deal flow perspective, investors with higher levels of specialization may be more likely to identify specific deals that intersect with their specializations in foreign markets due to more choices of investment opportunities on an international scale. Specialized investors may have a more dispersed global network due to the high specificity of their investment focus. These investors might also have to search more intensively for suitable deals, and the only investment opportunities available may be in foreign countries. Hence, it would be interesting to investigate whether a venture capital investor had other investment options in addition to the chosen international deal. Furthermore, it is likely that the number of other interested investors in the deal under consideration also plays a role. One could investigate the competitive situation faced by venture capitalists in their decision-making processes. Further research is needed to address these points and more specifically examine venture capital firms’ industry specializations in the context of international investment decisions. Funding Open Access funding enabled and organized by Projekt DEAL. Availability of data and material Available at: https:// eikon. refin itiv. com. This resource is only available on standalone computers where the database is installed. The URL is the best available for information about the database. Code availability I used Stata SE 15 to conduct the analyses. Stata code is available upon request. 924 A.Nörthemann 1 3 Declarations Conflict of interest There are no relevant financial or non-financial interests to disclose. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Abell P, Nisar TM (2007) Performance effects of venture capital firm networks. 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