Abstract
Building on the strategic entrepreneurship perspective and upper echelon theory, we examine the importance of private equity (PE) functional human capital and socio-demographic diversity for the success of the acquisitive growth strategy. Our focus is on the buyout lead partner team rather than on human capital at the collective PE firm (or fund) level. The results of our panel data survival models suggest that gender and age diversity, as well as the financial background, significantly accelerate both first and subsequent add-on acquisitions. The results are robust to alternative proxies, model specifications, and endogeneity checks.
Introduction
Over the past decade, diversity practices have been promoted and enforced globally by various regulators as part of the environmental, social, and governance (ESG) agenda (EU Directive 2013/36, SEC, 2021, etc.). These regulations apply not only to public companies, but also to private companies and their private equity (PE) and venture capital (VC) investors. However, the lack of gender, and racial and ethnic diversity in the global PE/VC industry has been well documented (Preqin, 2019; BVCA, 2023b; Gompers & Kovvali, 2018). More recently, investors in PE/VC funds are driving the ESG agenda and putting pressure on general partners (GPs) through the regular fundraising process. GPs appear to be more aware of gender and ethnic diversity, recognizing that diversity can prevent “groupthink” and contribute to better organizational health. However, there are debates and conflicting views in industry and academia and it is not clear whether the diversity agenda should be promoted because it is the “right thing to do” or because diversity is an enabler of better performance. We address the above question by examining the importance of functional human capital (work experience and education) and socio-demographic diversity (gender, age, and nationality) of the lead partner team (LPT) for the acquisitive growth of PE-backed buyouts.
LPT is an established term in both industry (see the PitchBook database) and academic literature (see Gompers et al., 2016; Hammer, Pettkus, et al., 2022). It refers to a team of professionals assigned to a portfolio company by the PE firm immediately following the buyout deal. 1 LPTs take full control of the portfolio company and its board (Gompers et al., 2016) and typically manage the buyout during their holding period (Hammer, Pettkus, et al., 2022). PE firms that already own their main investment (i.e., a platform buyout) will sometimes make further add-on acquisitions as a part of their growth strategy. As key decision makers, LPT members play a pivotal role in identifying and capitalizing on the growth opportunities in buyouts (Jelic et al., 2019; Wright et al., 2000). We hypothesize that LPTs with greater skill and diversity would be more effective in identifying and capitalizing on opportunities and finalizing add-on acquisitions. Consequently, they would be able to make decisions and complete add-on acquisitions in less time.
The PE governance model is characterized by enhanced managerial incentives, high leverage, and active PE firm monitoring and intervention (Jensen, 1989), thus reducing agency costs. However, with its emphasis on monitoring, agency theory may be inadequate in addressing the potential upside benefits of buyouts and overlooks the human capital of PE professionals (Jelic et al., 2019; Wright et al., 2000). Our theoretical framework, therefore, integrates the strategic entrepreneurship perspective and upper echelon theory (UET). The strategic entrepreneurship perspective uses growth as a central element of entrepreneurship (Delmar et al., 2003) and recognizes the importance of resources and capabilities in the pursuit of growth opportunities (Hitt et al., 2011; Ireland et al., 2003). It suggests that the entrepreneurial and managerial mindsets and skills of the top management team and board of directors are essential to ensuring efficiency of strategic and entrepreneurial actions (Ireland et al., 2001; Penrose, 2009). Previous studies based on strategic entrepreneurship perspective have shown that PE firms and professionals acting as monitors and advisors promote the entrepreneurial and managerial cognitions of buyout managers, while they use their own unique entrepreneurial and managerial skills and resources to facilitate organic growth in divisional (Meuleman et al., 2009) and secondary management buyouts (SBOs; Jelic et al., 2019). PE professionals therefore bring in new knowledge, expertise, skills, and resources and contribute to growth strategy decision-making and the realization of entrepreneurial growth opportunities that were not possible under the previous ownership regime (Meuleman et al., 2009; Wright et al., 2000).
The strategic entrepreneurship perspective, however, is silent on socio-demographic human capital, especially its diversity. We therefore draw on UET as a complementary theory, which emphasizes that strategic decision-making and execution are associated with the human capital of top decision makers (Hambrick & Finkelstein, 1987; Hambrick & Mason, 1984). This theory suggests that strategic decision-making is the result of information filtering and processing based on the values, cognitive biases, and behaviors of top managers. These values, cognitions, and behaviors are reflected in managers’ socio-demographic characteristics (gender, age, and nationality). Thus, socio-demographic diversity should be important when multiple individuals are involved in strategic decision-making and implementation.
Our analysis is based on manually collected panel data for 1,665 LPT members in 829 U.K. buyout companies. We track the LPT members of sample buyouts and their add-on acquisitions from 2004 to 2021. First, we examine the effect of LPT members’ diversity and skills on the time taken to complete the first add-on acquisition, using an accelerated failure time (AFT) survival model based on panel data. Following the first add-on acquisition, some PE-backed portfolio companies make several subsequent add-on acquisitions as a part of their acquisition-led growth strategy. We, therefore, extended our analysis by developing a conditional risk set model for the multiple add-on acquisitions. To the best of our knowledge, this type of analysis has not yet been attempted in related literature. Our results show that greater gender and age diversity, as well as a professional financial background, significantly reduce the time required to complete the first acquisition, both statistically and economically. The effects on subsequent multiple add-on acquisitions are also statistically significant, albeit smaller in economic terms. These results remain consistent when alternative proxies, model specifications, and endogeneity checks are considered.
Our study makes the following contributions to the literature. First, we demonstrate the importance of the human capital of PE professionals for the success of inorganic growth strategies. Embracing socio-demographic diversity is not only the right thing to do in order to comply with the ESG agenda, but it also enhances entrepreneurial growth.
Second, we join the call to extend the strategic entrepreneurship perspective to study new phenomena or combine it with other perspectives (Audretsch et al., 2009; Ireland et al., 2023). We extend previous studies that use strategic entrepreneurship perspective in the PE context (Jelic et al., 2019; Meuleman et al., 2009; Wright et al., 2001) to the acquisitive growth strategy and provide fine-grained insights to understand the role of PE professionals in fostering entrepreneurial growth.
Third, our analysis addresses recent calls to examine the micro-level factors that influence how PE investors adopt new practices, particularly in light of a shift in the PE model from an initial emphasis on efficiency to a more recent focus on growth (Verbouw et al., 2025). Specifically, we integrate UET with the strategic entrepreneurship perspective to improve our understanding of the impact of the socio-demographic diversity of PE professionals on the success of acquisitive growth strategies.
Fourth, our research adds a new dimension to previous studies, which primarily focused on human capital at the PE fund or firm level (Cornelli et al., 2017; Fuchs et al., 2022). We argue that distinguishing between the collective human capital of PE firms and that at the buyout (i.e., deal) level is important, since PE firms typically allocate a subset of professionals to LPTs. Treating PE firms (and funds) as homogeneous overlooks the heterogeneity of human capital among the team of PE professionals managing a given portfolio company. In other words, human capital is likely to vary from deal to deal and over time and may therefore differ from collective human capital at the PE firm/fund level.
Fifth, given that some firms prioritize growth over profitability and/or other goals (Gaba & Joseph, 2013; Opper et al., 2017), we respond to calls to examine goal variables other than immediate firm profitability (Greve, 2008). For example, the previous literature on entrepreneurial growth has rarely focused on the acquisitive growth strategy. The literature has treated growth in an overly simplistic way, mainly assuming internal (organic) growth and thus not examining the acquisitive growth mode (Davidsson & Wiklund, 2000; Gilbert et al., 2006). By examining the importance of LPTs for acquisitive growth, we also make a contribution to the body of research investigating the impact of organizational goals on team-level characteristics (Aguilera et al., 2024).
Sixth, although research is emerging on the role of time in decision-making, this is not explicitly linked to goal-setting and goal-attainment (Aguilera et al., 2024). By using acquisition completion time as the key variable, we can evaluate performance and explicitly attribute it to the success of the acquisitive growth strategy. Furthermore, although time-related measures tend to be influenced by the organizational context in which decision-making processes are embedded (Baum & Wally, 2003), they have rarely been examined in the PE context. 2 This is surprising, given the exit pressure forcing PE firms to race against time (Jelic et al., 2021), as well as the uniqueness of the PE governance model, which is not present in other labor market segments (Metrick & Yasuda, 2010). By focusing on completion time, we extend previous studies that predominantly used growth in sales and employment to measure the contribution of PE firms to entrepreneurial growth (see Nason & Wiklund, 2018). Finally, our study makes an important methodological contribution by developing a model for determinants of completion times for multiple add-on acquisitions.
The rest of the paper is structured as follows. Section “Theoretical Background and Hypotheses Development” discusses the theoretical framework and hypotheses. Section “Data and Methodology” presents the data and methodology. Descriptive statistics are presented in Section “Descriptive Statistics.” Section “Panel Data Survival Analysis” discusses the results of the AFT survival model for first add-on acquisitions. The conditional risk set survival model for multiple sequential add-on acquisitions is presented and discussed in Section “Conditional Risk Set Model for Multiple Acquisitions.” The robustness checks are discussed in Section “Robustness Checks.” Section “Discussion and Conclusion” concludes.
Theoretical Background and Hypotheses Development
Strategic Entrepreneurship and PE-Led Acquisitive Growth
Similar to the entrepreneurship literature, the PE literature has focused on traditional strategies that rely on “tailwinds” such as falling interest rates and stable GDP growth (Bain & Co., 2019). However, this has recently changed due to the popularity of the acquisitive growth strategy in the PE industry. In some cases, a well-positioned buyout will make three or more subsequent acquisitions, which is known as a “buy-and-build strategy.” Acquisitions have been found to increase the value of PE-backed buyouts by providing opportunities for further growth, synergy realization, and diversification (Gompers et al., 2016; Hammer, Marcotty-Dehm, et al., 2022). For example, Gompers et al. (2016) found that acquisitions were identified by their PE investors as the second most important source of value creation, just behind revenue growth.
The acquisition-led growth strategy embodies the entrepreneurial spirit (Trottier, 1995) and necessitates the strategic managerial and entrepreneurial knowledge and abilities (Wright et al., 2000) that empower entrepreneurial enterprises to identify and convert growth prospects into value creation by accessing and integrating relational resources. However, unlike organic growth, acquisitive growth requires skills relating to identifying potential targets in the complex external environment, negotiating, evaluating strategic alignment with the platform company, conducting financial evaluations, obtaining financial resources, carrying out due diligence, and completing the takeover process. The incumbent buyout management team usually has little acquisition experience or the necessary resources to successfully accomplish these tasks. With limited internal knowledge, they may lack the entrepreneurial mindset to consider broader opportunities for business expansion and the management skills required to integrate two businesses and achieve synergies. Furthermore, they may be reluctant to pursue acquisitions because they are risky and expensive growth investments. This may also be due to limitations in managerial cognition with regard to making strategic changes (Wright et al., 2001). Consequently, the human capital of LPTs (both functional and socio-demographic) is a key determinant of the success of acquisitive growth.
In PE-backed portfolio companies, the PE firm is the primary investor, while incumbent managers typically own a significant proportion of the equity in the company (Wood & Wright, 2009). In addition to annual fees (2%), GPs in PE firms receive 20% of fund returns (carried interest) only when portfolio companies are successfully exited. Career prospects and lifetime compensation of PE professionals are determined by successful performance of the portfolio companies (Chung et al., 2012). Therefore, PE firms have a long-term commitment to buyouts and are well-placed to influence management through board representation, the ability to replace managers, and additional control rights via preferred shareholding in portfolio companies (see Acharya et al., 2009; Kaplan & Stromberg, 2003). Following investments, PE firms assign professionals to LPTs in the selected portfolio companies. The LPTs then take complete control of the portfolio company, setting targets, requesting interim information, and acting as a source of professional contacts for managers (Beuselinck et al., 2006; Bloom et al., 2015; Sapienza et al., 1996). They also play a pivotal role in identifying and capitalizing on growth opportunities in buyouts, providing resources and capabilities, and monitoring management efficiency (Jelic et al., 2019; Wright et al., 2000). For example, they identify growth opportunities and evaluate whether acquisition targets align with the portfolio company’s growth strategy and risk/return profile. Following the preliminary screening of acquisition targets by the CEO and management team, the LPTs provide final due diligence. Thus, the PE governance model enables LPTs to implement an acquisitive growth strategy that aligns with the objectives of PE firms.
LPTs’ Functional Human Capital
Functional human capital, which includes skills based on prior work experience and human capital based on education, is a critical resource for strategic entrepreneurial behavior (Ireland et al., 2003). The expertise, skills, and cognitions accumulated through previous work experience and education represent an individual’s know-how, their capacity to compete in their industry, and their ability to identify and exploit entrepreneurial opportunities and complete specific tasks (Barney, 2014). Entrepreneurship studies have demonstrated a strong relationship between human capital and firm performance, as well as strategic decision-making (e.g., de Villiers et al., 2011; Meuleman et al., 2009). Previous studies have also documented the importance of collective human capital for VC and PE firm/fund performance (Bottazzi et al., 2008; Degeorge et al., 2016; Dimov & Shepherd, 2005; Fuchs et al., 2022; Zarutskie, 2010). These studies tend to analyze the collective (i.e., average) background of individuals working within PE/VC firms. However, PE/VC firms usually allocate a team of professionals (e.g., LPT) to a portfolio company. As PE/VC firms have several funds with numerous companies, it is highly likely that the average firm’s human capital differs from that of the deal team. For example, Acharya et al. (2013) report the presence of heterogeneous skills at the deal-partner level in PE transactions. Focusing on the average would treat a PE firm’s human capital as homogeneous, thus overlooking the heterogeneity among PE professionals (Jelic et al., 2019). Therefore, the distinction between firm-level and deal-level human capital is very important.
More recent studies examining human capital at a deal-level report that it contributes to the operating performance, organic growth, and exit routes of portfolio companies. However, the effects vary depending on the aspect of human capital and the stage of investment (e.g., Acharya et al., 2013; Hammer, Pettkus, et al., 2022; Jelic et al., 2019; Pelucco & Vismara, 2025). For instance, Pelucco and Vismara (2025) exploit the unique decision-making autonomy of angel investors to directly measure the influence of an investor’s background on their investment choices and, consequently, the results of their investments. They found that angels with entrepreneurial backgrounds underperformed relative to those with other upper-echelon backgrounds, such as former venture capitalists. Jelic et al. (2019) report that the financial experience of PE directors has a substantial impact on post-SBO profitability, while an MBA is especially important for enhancing post-SBO growth performance. Building on the above studies, we examine the work experience and educational background of LPT members at the deal level. Specifically, we analyze the two most common professional backgrounds of PE professionals before they entered the industry: financial (e.g., former accountant, financial controller, and banker) and operational (e.g., management consultant and a professional in (non-financial) industry operations). Regarding education, we analyze the higher education qualifications (MBA and MSc/PhD) of PE professionals.
PE professionals with financial experience can influence the speed of add-on completion in two ways. Firstly, their financial background is typically associated with the Big Four and investment banks, where M&A is a core activity. They may have acquired extensive M&A skills through their previous experience at banks or accountancy firms. These skills help PE professionals quickly identify, evaluate, and negotiate targets. Secondly, financial experience is particularly important when buyouts require additional capital to finance the add-on, as these professionals will have a better understanding of the type and source of finance required for a particular deal.
According to Kor and Sundaramurthy (2009), PE professionals with operational experience are likely to develop a deeper knowledge of specific industry dynamics and conditions. Such expertise can provide valuable insights into operational and managerial issues, helping firms to achieve operational improvements and generate organic growth (Jelic et al., 2019). Some acquisitions may require the in-depth industry knowledge and sophisticated managerial skills of professionals to facilitate quick decision-making with complex information, ensure smooth acquisition integration and effective synergies, and guarantee the timely completion of deals and successful expansion. Overall, we expect that having financial and operational experience will reduce the time taken to complete both the first and subsequent add-on acquisitions.
Education is usually an intense and formative experience that shapes the way decision makers think, what they know, and the skills they have to understand and deal with the complexity of environments (Hambrick & Mason, 1984). Consequently, education is regarded as an objective and reliable indicator of managerial competence and entrepreneurial insight (Peterman & Kennedy, 2003). Different levels of education have different focuses and qualities; therefore, they indicate the quality of cognition and the skills of individuals (Urquhart & Zhang, 2022). For instance, MBA programs cover a broad spectrum of business topics (e.g., leadership, decision-making, and organizational management), offering a more practical approach compared to MSc/PhD programs. These programs often incorporate case studies and scenario-based learning, which are designed to enhance real-world managerial competencies. MBA holders tend to be more responsive to the complexity and uncertainty of firms and markets, as well as to growth opportunities (Bertrand & Schoar, 2003; Jelic et al., 2019). Furthermore, MBA programs enhance leadership qualities (Hambrick & Mason, 1984) by incorporating teamwork and negotiation techniques, which could facilitate decision-making and negotiating complex transactions such as acquisitions (Francis et al., 2016). The business school alumni networks of MBA holders could also benefit various aspects of decision-making (Fuchs et al., 2022). Overall, previous literature suggests that MBA holders can contribute additional expertise and leadership skills to LPTs in portfolio companies.
While the educational background of MBA holders has been extensively examined in previous related studies (e.g., Acharya et al., 2013; Jelic et al., 2019; Pelucco & Vismara, 2025), the academic backgrounds of MSc and PhD holders have received less attention. However, entrepreneurship literature suggests that MSc/PhD holders possess superior cognitive abilities and skills, demonstrating greater problem-solving ability and receptiveness to change (Barker & Mueller, 2002; Wally & Baum, 1994). Studies have also found that MSc and PhD holders are associated with greater innovation (Wiersema & Bantel, 1992; Zona et al., 2013), sustained investment (Bertrand & Schoar, 2003), valuable alliances (Palmer & Barber, 2001), and improved firm performance (Urquhart & Zhang, 2022). In the context of our study, we expect MSc/PhD graduates to help other LPT members understand and manage complex acquisition transactions and design innovative deal structures, potentially accelerating their completion.
We formulate the following hypotheses related to the LPTs’ functional human capital:
Hypothesis 1: LPTs with members who have financial experience will exhibit shorter add-on acquisition completion times.
Hypothesis 2: LPTs with members who have operational experience will exhibit shorter add-on acquisition completion times.
Hypothesis 3a: LPTs with members who have an MBA degree will exhibit shorter add-on acquisition completion times.
Hypothesis 3b: LPTs with members who have an MSc/PhD degree will exhibit shorter add-on acquisition completion times.
LPTs’ Socio-Demographic Human Capital
According to the UET, managers’ values, cognitive biases, and behaviors are reflected in the socio-demographic characteristics of groups. Diversity in groups improves information elaboration and knowledge sharing within teams (van Knippenberg et al., 2004), leading to an efficient and more comprehensive decision (Gruenfeld et al., 1996), which will enhance the decision execution process. For instance, when team members debate, they are likely to draw on their socio-diversity sets to support their arguments, and ultimately achieve comprehensive decisions (Simons et al., 1999). Furthermore, teams whose members draw from different pools of information resources and experience will make more effective decisions and deliver more creative products than units whose members draw from the same pool, increasing a firm’s competitive advantage (Finkelstein & Hambrick, 1996; Harrison & Klein, 2007). It has also been documented that cognitively diverse groups outperform groups of high ability individuals from homogeneous backgrounds in problem solving (Page, 2007). The author explains this by arguing that the more varied perspectives of diverse groups foster new solutions. In addition, greater demographic diversity helps firms to adapt to different stakeholders, increase understanding of markets (Robinson & Dechant, 1997), and improve communication with different outsiders (Ancona & Caldwell, 1992), which in turn affects creativity and innovation (Boeker, 1997; Campbell & Minguez-Vera, 2008). In contrast, heterogeneity can slow down the strategy formulation process, reduce commitment and communication within the team, and impair decision-making performance (Hambrick et al., 1996; Tsui et al., 1992). Van Knippenberg and Schippers (2007) recognize that although diversity can disrupt group processes, it can also provide synergies that lead to performance benefits. With a diverse team, resources and knowledge can be combined and synergized in creative ways, leading to more entrepreneurial activity (Elenkov et al., 2005). Overall, the above evidence predicts a positive effect of diversity on team effectiveness and broader decision-making. It is therefore reasonable to expect that more socio-demographically diverse LPTs would make more effective and comprehensive decisions about potential targets, due diligence, and other critical aspects of acquisitions. This would reduce potential errors and delays in the acquisition process, resulting in shorter completion times. We expect the socio-demographic diversity of LPTs to reduce the time taken to complete both the first and subsequent add-on acquisitions.
Previous empirical studies are broadly consistent with the above theoretical predictions. For example, diversity leads to better performance of mutual funds (Gottesman & Morey, 2006), hedge funds (Li et al., 2011), and VC funds (Zarutskie, 2010). The evidence on the importance of diversity in the PE industry is limited. In a rare study, Hammer, Pettkus, et al. (2022) report that socio-demographic diversity (gender, age, and ethnicity/nationality) of LPTs is associated with significantly higher, whereas occupational diversity (work experience, education, and university affiliation) is associated with significantly lower buyout performance, as measured by firm value growth rates and multiple expansion.
When it comes to individual components of socio-demographic diversity, most empirical evidence focuses on the importance of gender diversity. For example, previous research has shown that boards with greater gender diversity are less prone to financial misconduct (Cumming et al., 2015). The presence of women on boards tends to be positively correlated with return on invested capital, better governance, and corporate social responsibility (Ben-Amar et al., 2017). Social identity theory suggests that the gendered division of labor provides men and women with different skills and cognitive abilities (Eagly, 2013). Female directors on male-dominated boards have been reported to improve the quality of board deliberations (Stephenson, 2004), especially when dealing with complex issues such as acquisitions. At the same time, research has found that female members tend to be less conformist and more likely to express their independent views (Adams & Ferreira, 2009), which will foster good communication between the group of PE professionals and the management team thus enabling them to pursue the add-on acquisition strategy within shorter time. Compared to men, women tend to have many favorable characteristics in terms of value judgment, risk taking, and decision-making (Ray, 2005). In addition, female PE professionals may be more likely to challenge the CEO/other insiders and push them to consider a wider range of options and pros and cons when making strategic corporate decisions (Chen et al., 2024), which in turn promotes effective decision-making. Because men and women differ in the unique and complementary understandings, perspectives, temperaments, and relational ties they bring to the external world, gender diversity in teams should lead to more informed and comprehensive strategic decisions that maintain a fit between the firm and its changing environment (Hillman et al., 2002; Miller & de Carmen Triana, 2009), and ultimately shorten the decision-making process.
Diversity of nationality is an important source of diversity that helps firms cope with the different institutional contexts they face. For example, Nielsen and Nielsen (2013) suggest that demographic background influences the mindset and behavior of top managers in perceiving and exploiting the entrepreneurial (i.e., growth) opportunities and combining the skills, knowledge, and resources to effectively complete the entrepreneurial oriented strategies. Teams of different nationalities have deep and complementary knowledge of their home institutions and markets (Boone et al., 2019) and are more likely to succeed in fostering a wide-open and geocentric attitude (Nielsen, 2010). This helps them to scan and interpret international information and to identify and exploit acquisition opportunities on a global scale, thereby accelerating the speed of acquisition decisions and completions.
Age diversity tends to be less job-related than other demographic variables. For example, although age diversity may capture job-related experiences, networks, and perspectives, these experiences represent a small fraction of the total set of perspectives and experiences that age diversity captures (Zenger & Lawrence, 1989). The age of decision maker has been identified as an important variable in the M&A literature (Yim, 2013). In the PE context, younger professionals tend to be more risk-taking, but they have less acquisition experience or possess experience only from lower levels (Nadolska & Barkema, 2014). Conversely, older professionals are more risk-averse, but they have extensive acquisition experience (Berger et al., 2014). Furthermore, older professionals may have formed opinions about what works well and what might be challenging based on their extensive experience in acquisitions. However, their risk-averse attitudes may cause them to be more critical of risky potential targets than their younger counterparts. Age diversity, therefore, balances sensitivities to growth opportunities, risk, and experience in acquisition decisions, which may improve the timely completion of an acquisition transaction. Therefore, based on theoretical predictions and empirical evidence, we expect the following:
Hypothesis 4: More gender-diverse LPTs will exhibit shorter add-on acquisition completion times.
Hypothesis 5: More nationality-diverse LPTs will exhibit shorter add-on acquisition completion times.
Hypothesis 6: More age-diverse LPTs will exhibit shorter add-on acquisition completion times.
Data and Methodology
Data and Sample Selection
Our analysis combines data from several sources. For example, we identified management buyouts (MBOs), management buy-ins (MBIs), institutional buyouts, and SBOs from Orbis M&A and the Centre for Management Buyout Research (CMBOR) database. Data on PE funding and PE firm entry and exit dates are sourced from Thomson Refinitiv, Orbis M&A, and the CMBOR database. Accounting information on portfolio companies is obtained from the FAME database. The above data collection resulted in detailed data on 829 buyouts backed by 271 PE firms.
While the buyout vintage years cover the period from 2004 to 2018, our sample of add-on deals covers the period from 2004 to 2021. We therefore track LPT members and add-on acquisitions at least 3 years after the buyout transaction, until December 31, 2021. The 3-year cut-off point was chosen based on the average time taken for the first add-on acquisition. Information on all completed add-on acquisitions of the sample buyouts was collected from Orbis M&A. To rule out the possibility of missing data in Orbis M&A, we randomly cross-checked information in LexisNexis, Google News, and on the websites of portfolio companies and PE firms. In total, the sample buyouts made 512 add-on acquisitions. Overall, our panel dataset consists of 4,548 buyout year observations for first and 5,743 observations for subsequent add-on acquisitions.
PE firms do not disclose the assignments of their professionals to their funds and/or portfolio companies. We therefore identify LPT members by establishing that, following investments, PE firms assign professionals (i.e., LPT members) who then take full control of the portfolio companies by sitting on their boards. We manually identify LPT members by matching a list of all board members (available on the FAME database) with the names of PE professionals found on PE firms’ websites, the Bloomberg Professional website, Companies House, LinkedIn, the Orbis M&A database, and deal announcements on Google. Gender, nationality, and date of birth for each PE professional were also manually collected from PE firms’ websites, the Bloomberg Professional website, Companies House, and LinkedIn. Work experience and education data were manually collected from PE firm websites, the Bloomberg Professional website, and LinkedIn. We were able to collect detailed data on the skills and socio-demographic characteristics of 1,665 PE professionals. The majority of the sample professionals use the titles “partner” (e.g., general, managing, or founding partner) and “managing director.” We also track any changes in the composition of LPTs and make sure that PE professionals were on the LPT before the completion date of the acquisitions. We find cases where professional diversity and skills at the PE firm level have not necessarily been passed on to the portfolio company. 3 This is to be expected, given that PE firms invest in numerous portfolio companies, resulting in a higher number of companies than there are professionals working in the PE firm. For example, we found some LPTs with no female professionals, despite the respective PE firm employing several female professionals. We also observed changes in the composition of LPTs during the sample period. For instance, some professionals initially assigned to an LPT left the team because they were reassigned to another portfolio company or left the corresponding PE firm. These changes affected the socio-demographic and skill composition of the LPTs. Crucially, our panel dataset captured these changes as it tracks each LPT over time. This is particularly important in the context of our study, as we examine both initial and subsequent multiple acquisitions. The above examples further highlight the importance of focusing on the portfolio company level and tracking LPT changes over time.
Table 1 shows the distribution and coverage of our sample of buyouts and PE firms over the sample period. The distribution of buyouts in the sample illustrates the cyclical nature of PE investment. In particular, the highest numbers are recorded in 2006 and 2007, followed by a significant decline from 2008 to 2009 due to the financial crisis. A recovery can be seen from 2010 to 2018, the most recent PE boom. This pattern is also consistent with the global buyout trend reported in the Bain & Co report (2019). The table shows that our annual coverage ranges from 19% to 29% of total U.K. buyouts, with an average coverage of 24%. The trend in sample buyouts follows the trend in total U.K. buyouts, as tracked by Orbis M&A.
Distribution and Coverage of Sample PE Firms and Buyouts.
Note. This table presents distribution of PE firms and 829 sample buyouts during the sample period. Columns with PE firms show the total number of PE/VC firms with the U.K. presence (source: BVCA report on investment activity, various issues), number of the PE firms investing in the sample buyouts, and resulting sample coverage, in respective (vintage) years. Columns with buyouts, show the number of U.K. buyouts (source: Orbis M&A), sample buyouts, and resulting sample coverage, in respective (vintage) years.
Our sample of PE professionals covers around 51% of employees with an investment role in all PE firms with a U.K. presence in 2018. 4 Overall, we cover the activities of about a quarter of all PE firms with a U.K. presence over the sample period (Table 1). To the best of our knowledge, our sample coverage compares favorably with the coverage reported in previous related studies.
Panel Data Survival Models
Contrary to other methods, the survival model analysis does not implicitly assume that portfolio companies can maintain consistent and linear growth over time. This is important since firm growth is not linear and varies over time (Shepherd & Wiklund, 2009), especially in the context of acquisition-led growth. Furthermore, other statistical models (e.g., Logit, OLS, Tobit) do not consider differences in the timing of add-on acquisitions and do not control for each firm’s period at risk. Therefore, they can only estimate covariate effects for the sub-sample of uncensored observations. Omitting the time to censoring would however lead to a skewed distribution of completion times (Amini et al., 2023; Giot & Schwienbacher, 2007). Our AFT survival model corrects for censored observations and incorporates first and subsequent add-on acquisitions into our panel data. It also uses information conveyed in the time-to-censoring processes. We first examine the effect of PE skills and diversity on the time to complete the first add-on acquisition using the AFT random effect panel data survival model:
The dependent variable is the natural logarithm of the number of days between the buyout and the first add-on acquisition date or, for buyouts without acquisitions, the natural logarithm of the number of days between the buyout and either exit or the end of the sample period. The completion time calculation has minimal data requirements and allows straightforward comparisons. Unlike the monetary performance measures used in the PE industry, such as the internal rate of return (IRR) and the public market equivalent (PME), this calculation does not require numerous assumptions. 5 It is also free from systematic biases present in PE fund performance reporting (Jelic et al., 2021).
The dependent variable is presented as a linear function of the covariates, where β0, . . ., β p are the parameters to be estimated, X1, . . ., Xp are the covariates, and εi,t is the error term with a specific distributional form that determines the model. The above model is estimated as a panel data random effect parametric survival model with an exponential distribution. 6 We also use the Huber/White sandwich estimator to obtain robust estimates that are clustered at the level of the acquisition.
Explanatory Variables
Our specific human capital variables are measured using biographical information on PE professionals. For skills, following previous studies (Acharya et al., 2013; Degeorge et al., 2016; Jelic et al., 2019), we identify each PE professional by whether he/she worked in finance, accounting, or banking (financial experience) or in non-finance industry or management consulting (operational experience) before entering the PE industry. For education, we identify each PE professional by whether he/she holds an MBA or another MSc/PhD degree. We measure d_Finance (d_Operation, d_MBA, d_MSc/PhD) as a categorical variable equal to one if any of PE professionals on LPTs have relevant financial work experience (operational work experience, MBA degree, or other MSc/PhD degree) in a given year, otherwise zero. In terms of diversity, we construct the diversity variables to capture demographic differences in terms of gender, nationality, and age, following Hammer, Pettkus, et al. (2022) and Blau (1977). The diversity proxies are defined along with all other variables in Table 2.
Variable Definitions.
Following the previous literature, we control for several determinants of acquisition-related variables (Bloom et al., 2015; Wilson et al., 2022a, 2022b). First, as the quality of corporate governance affects strategic decisions, we include two board variables to measure the quality of corporate governance: the size of the board (LNBS) and the proportion of non-executive directors (pc_NED). Second, we control for characteristics of the portfolio company. We control for sales growth (SALG), as fast-growing companies may have more ambitious growth strategies and are therefore more likely to make acquisitions to expand the business. High levels of debt may constrain acquisition activity, so we include gearing (Gearing). We also include buyouts’ prior acquisition experience (Prior_ACQ) as buyouts with prior acquisition experience are more likely to continue with acquisitions. Older and larger firms are more likely to acquire other firms, so we include firm size (Size) and age (Age). Third, we control for MBIs as the literature recognizes differences between MBOs and MBIs. For instance, Wright et al. (1995) report that U.K. managers undertaking MBIs often encounter significant unexpected problems. We control for syndicated deals (Syndication), as PE funding from multiple investors could increase the likelihood of agency conflicts among syndicate members and thus make deals more complicated (Stromberg, 2008; Wright et al., 1995). For example, transaction and agency costs can arise from the need to coordinate syndicates (Wright & Lockett, 2003), from potential moral hazard issues (Pichler & Wilhelm, 2001), and ongoing involvement (e.g., coordination with external parties such as owners and lawyers) for each subsequent add-on acquisition (Hammer et al., 2017). For the above reasons, syndicated acquisitions are expected take longer to complete. Finally, we control for SBOs, as SBOs tend to have different value creation strategies (Hammer et al., 2017; Jelic et al., 2019).
Descriptive Statistics
Table 3 presents sample descriptive statistics stratified by variables on (a) PE firms; (b) add-on acquisitions; (c) buyouts; and (d) LPTs. 7 Our sample of PE firms comes from 24 countries. The dominance of U.K. (61%) and U.S. (24%) PE firms is consistent with data on global PE activity (Bain & Co., 2021). The average age of the PE firms in the sample is around 14 years. About 9% of the sample PE firms are considered more reputable, as they are listed among the top 50 firms in PE International (e.g., KKR & Co LP, Carlyle Group LP, Blackstone Group LP, Permira Partners LLP, and Bain LP). As expected, most PE firms are limited partnerships. About 10% of the PE firms in the sample are owned by banks or governments (i.e., captives). Only 3% of the sample PE firms are publicly traded.
Descriptive Statistics.
Note. This table presents descriptive statistics for the key variables used in our models. Mean, St. Deviation, Min, and Max are the average, standard deviation, minimum, and maximum for categorical and continuous variables, as defined in Table 2. N is number of sample PE firms, add-on acquisitions, buyouts, and firm-year observations for time-varying variables.
The average completion time for first add-on acquisitions is 745 days. The average time between the first and second add-on acquisitions is 517 days, and the average time between the second and third add-on acquisitions is 353 days. 8 The results are in line with Aktas et al. (2013) who, in a broader M&A context, report evidence of a significant decrease in completion time between subsequent acquisitions. More than 50% of the sample acquisitions are part of the buy-and-build growth strategy, which involves three or more subsequent add-on acquisitions by a sample buyout. The sectors with the highest percentage of buyouts making add-on acquisitions in our sample are finance and insurance (50%), human health and social work (47%), and information and communication (43%). The acquisition targets are mainly privately owned domestic companies in the same industry. 9
In terms of size, our sample of buyouts is comparable to the overall population of U.K. PE-backed buyouts. For example, the mean total assets of our sample in the buyout year were £74.8 million (median: £18.2 million), whereas the mean total assets of all U.K. PE-backed buyouts were £78.2 million (median: £11.5 million). 10 SBOs accounted for 28% of the sample buyouts, while MBIs accounted for 4%. Only 1 in 10 of the PE deals in the sample was syndicated.
A financial background features in 59% of sample LPTs, while an operational background features in 27%. Around 21% of LPTs have at least one PE professional with an MBA. A similar percentage (22%) is for MSc/PhD degrees. Only 7% and 25% of the sample LPTs have female and non-U.K. PE professionals, respectively. The low levels of gender and nationality diversity highlight the lack of diversity in the U.K. PE industry. The number of PE professionals on sample LPTs ranges from 1 to 6, with a mean value of 2 (median: 2) immediately after buyout deals. The average LPT size is consistent with the U.K. average reported by Hammer, Pettkus, et al. (2022). Overall, our buyout sample has relatively small LPTs with established PE professionals but limited diversity.
Panel Data Survival Analysis
AFT Model for First Add-On Acquisitions
The results of the AFT random effect panel data survival model (Equation 1), with exponential distribution. Are shown in Table 4. Our results for functional human capital factors suggest that the presence of PE professionals with a financial background on the LPT statistically reduce the time required to complete the first add-on acquisitions (Model 1).
Panel Data Survival Model for First Add-on Acquisitions.
Note. This table reports results of the AFT random effect panel data survival model (Equation 1) with exponential distribution. The dependent variable, in all models, is completion time as the natural logarithm of the number of days between the buyout and the first add-on acquisition date; or natural logarithm of number of days between buyout and either exit or end of the sample period, for buyouts without acquisitions. Models 1 and 2 show the separate effects of functional human capital and socio-demographic diversity factors. Model 3 shows results for the combined effect of both functional and socio-demographic factors. Model 4 reports results of Model 3 augmented with controls for characteristics of PE firms. The definitions of all independent variables are presented in Table 2. Industry and year dummies are included to control for unobserved heterogeneity. The robustness standard errors adjusted for clustering by buyouts are provided in parentheses. N is the firm-year observations.
p < .01. **p < .05. *p < .1.
We find no evidence for the importance of operational experience and MSc/PhD degrees for completion time of acquisitions. The positive and statistically significant coefficient for the MBA is a surprising result. There are several possible explanations. Firstly, it is possible that MBA holders possess information processing skills that are better suited to organic growth than inorganic growth (Penrose, 2009). This could be due to changes in MBA programs since the 1970s, which have become less favorable toward acquisitions, particularly in certain industries (Jung & Shin, 2019). This explanation would also align with that of Acharya et al. (2013), who reported that PE specialists with MBAs performed worse than those without MBAs when pursuing an inorganic growth strategy. Alternatively, it is possible that MBA degrees no longer provide the skills required for the success of PE/VC firms. The weak exit performance of VC funds and angel investors reported by some recent studies (Dimov & Shepherd, 2005; Pelucco & Vismara, 2025; Zarutskie, 2010) would be consistent with this argument.
In line with our hypothesis, gender and age diversity of LPTs (Model 2) tend to significantly reduce the time to first acquisition. For example, the statistically significant coefficients of −0.468 (−0.793) in Model 2 indicate that, all else being equal, a one-standard-deviation increase in gender (age) diversity leads to an approximate 8% (15%) reduction in the time to first acquisition. The results in Model 3, which combines socio-demographic diversity and skills, suggest that gender, age, and financial background remain statistically significant. The signs and significance of all other coefficients remain consistent with the results reported in models 1 and 2. Based on respective time ratios, age diversity has the greatest economic effect on completion time, while gender has a similar effect to financial background. 11 For example, increasing age diversity by one unit reduces completion time by 55%, compared to 35% for gender diversity. LPTs whose members have a financial background complete their first acquisition 34% sooner than those without.
The coefficients for the control variables are consistent with previous literature. For example, large buyout firms with prior acquisition experience tend to complete acquisitions earlier than their counterparts. As expected, the positive and highly significant coefficients for syndication suggest that add-on acquisitions take much longer for buyouts sponsored by two or more PE firms.
Previous PE studies have examined the importance of reputation and the ownership status of PE firms (e.g., Hammer, Pettkus, et al., 2022; Jelic et al., 2005, 2021; Stromberg, 2008). For example, more reputable firms may exhibit different human capital and investment behavior compared to their less reputable counterparts. Similarly, PE firms that are subsidiaries of investment banks or governments (i.e., captive PE firms) tend to exhibit different characteristics to independent PE firms (Kaplan, 1991). PE firms that are listed companies may be subject to a different regulatory framework (Lerner et al., 2002), which could impact their investment and other strategies. We therefore include PE firm characteristics (PE_reputable, PE_captive, and PE_listed) in our model (see Model 4 in Table 4). 12 The results are consistent with those of Model 3, reported in the same table. None of the coefficients on the PE firm characteristics are statistically significant, suggesting no direct effect on acquisition completion times.
Endogeneity Concerns
By design, our research attempts to reduce the potential impact of unobservable PE firm and buyout-level characteristics on the relationship between human capital and performance. For example, by collecting data for both small and large deals over a long sample period, we avoid a sample selection bias caused by focusing predominantly on the most recent and/or predominantly large PE deals. We also capture variation in LPTs’ diversity over time. With both time and industry fixed effects, we control for endogeneity arising from unobservable time trends and industry-level characteristics.
In line with previous related studies (e.g., Chahine & Goergen, 2011; Hammer et al., 2017; Jelic et al., 2019), we also use the Heckman (1979) model to control for the possibility that PE firms prefer certain types of portfolio companies (see Lerner et al., 2011). In the first step, we predict the probability of a PE investment by estimating a probit model and estimate the inverse Mills ratio (Lambda) using a large sample of 4,080 PE-backed and non-PE-backed buyout deals during 2004 to 2018. The dependent variable is a categorical variable equal to 1 if the firm received PE backing and 0 otherwise. Company characteristics (e.g., size, age, location, and industry) are identified in the previous literature as important determinants of a PE firm’s investment decisions (e.g., Chahine & Goergen, 2011; Jelic et al., 2021; Wilson et al., 2022a, 2022b). In addition, the previous acquisition experience of portfolio companies and PE market conditions may also influence the choice of PE firms. Following Hammer, Pettkus, et al. (2022), we divide the buyout years in our sample into three periods: buyout boom (2004–2007), financial crisis (2008–2010), and post-financial crisis (2011–2018). Categorical variables for the financial crisis and post-financial crisis periods are included in the model as additional controls. Lastly, we control for target company industry and include categorical variables for the manufacturing and business service industries. In the second step, we run our baseline AFT model with Lambda, to correct for selection bias.
The results of the probit model are presented in Panel A Table 5. The coefficient on buyout size is positive and highly statistically significant, confirming that PE firms prefer to invest in large private companies. As expected, PE backing is more likely in good market conditions, which is reflected in positive and highly statistically significant signs for the coefficient on PE capitalization and post crisis. Negative and highly statistically significant coefficients on age suggest that PE firms prefer to invest in younger companies. One explanation could be that companies earlier in their life cycle tend to have higher growth potential.
Heckman Two-Stage Panel Data Survival Model for First Add-on Acquisitions.
Note. This table presents the results of Heckman two-stage analysis. Panel A presents the results of a probit model for the probability of PE investment. Results of the baseline survival models for first acquisitions are presented in Panel B. The dependent variable, in models 1, 2, and 3, is completion time as the natural logarithm of the number of days between the buyout and the first add-on acquisition date; or natural logarithm of number of days between buyout and either exit or end of the sample period, for buyouts without acquisitions. The definitions of all independent variables are in Table 2. Industry and year dummies are included to control for unobserved heterogeneity. The robustness standard errors adjusted for clustering by buyouts are provided in parentheses. N is the firm-year observations.
p < .01. **p < .05. *p < .1.
The results of the second stage model are statistically and economically consistent with the results reported in Table 4. The coefficient on Lambda is not statistically significant, suggesting that selection bias does not seem to be very important in our study. We therefore interpret the better success in acquisitions as a causal effect of PE professionals, as the results of our robustness checks confirm that there is nothing inherent in the U.K. firms targeted by PE firms that would have caused their acquisition performance to improve if they had not been acquired by PE.
Conditional Risk Set Model for Multiple Acquisitions
The above analysis in Section 5, based on the time to first add-on acquisition, ignores relevant information from observations of subsequent add-on acquisitions. We therefore extend our analysis by setting up our panel data to include all (first and subsequent) add-on acquisitions. There are several ways to statistically model repeated events in survival analysis. For example, we can assume that future events follow a Markov process (Andersen & Gill, 1982; Prentice et al., 1981), assume dependence on shared random effects (frailty models) (Wei et al., 1989), or make assumptions about the means/rates of the counting process (Pepe & Cai, 1993). After careful consideration of the characteristics of our panel dataset and our hypotheses, we decided to adopt the first (i.e., Markov) modeling approach, that is, to assume that an acquisition depends only on the immediate past. Both Andersen and Gill (1982) and Prentice et al. (1981) use the time-to-event method and the counting process method to organize the data set. Unlike Andersen and Gill (1982), the Prentice et al. (1981) analysis ordered multiple events by stratification, implicitly assuming that a subject is not at risk of a second event until the first event has occurred, and so on. The approach allows for the possibility that the time increments between events may be conditionally correlated, given the covariates. In the context of our study, the Prentice et al. (1981) approach allows the risk of recurrence to vary between the consecutive acquisitions. Thus, it allows us to shed more light on the importance of diversity and skills for the success of the acquisitive growth involving multiple acquisitions. Specifically, we constructed our data using one of the Prentice et al. (1981) counting process methods and stratified the analysis by the order of acquisitions. For example, the buyout involving three add-on acquisitions would appear three times, in time (t), in the final dataset in the following order: first for the period from the buyout date to the date of the first add-on acquisition; second for the period from the date of the first add-on acquisition to the date of the second; and third for the period from the date of the second to the date of the third acquisition. In other words, we measure completion time of subsequent acquisitions by setting the clock to zero after each acquisition, rather than measuring it continuously from a buyout date. The implicit assumption is that the buyout is not at risk from a new add-on acquisition until the previous one has occurred. This is important given that the composition of LPTs may change between acquisitions. Therefore, the dependent variable in our models for multiple acquisitions is the natural logarithm of the number of days to each add-on acquisition completed until time (t), or the number of days between the buyout and either exit or the end of the sample period for buyouts without add-on acquisitions.
As with other estimates of parametric survival models, we need to select the most appropriate distribution. Based on the AIC criteria, we selected Weibull as the most appropriate distribution. Notably, the distributions for the first and multiple add-on acquisition models differ, which makes economic sense. For instance, the underlying distribution in the first add-on acquisition model is exponential, which implies a constant hazard rate. The model for multiple add-on acquisitions suggests that the underlying distribution is Weibull, indicating that the likelihood of a new acquisition increases monotonically over time.
Table 6 shows the results of our AFT random effect panel data survival models for the multiple acquisitions. Models 1 to 3 present the results of the baseline model, while models 4 to 6 present those of the second-stage Heckman model. These results are statistically consistent with those for the first add-on acquisitions presented in Tables 4 and 5. Although weaker, the economic effects of the key variables persist. For example, an increase of one standard deviation in gender (age) diversity, all else being equal, leads to a reduction in completion time of 2% (5%) (Model 2). A comparison based on the time ratios in Model 3 shows that the relative economic importance of the key variables remains the same. For example, increasing age diversity by one unit reduces completion time by 20%, compared to 12% for gender diversity. LPTs whose members have a financial background complete their add-on acquisitions 9% sooner than those without.
Conditional Risk Set Model for Multiple Add-on Acquisitions.
Note. This table reports results of the AFT random effect panel data survival models with Weibull distribution. The dependent variable is completion time as the natural logarithm of the number of days to each add-on acquisition completed until time (t); or the number of days between the buyout and either exit or the end of the sample period for buyouts without add-on acquisitions. Models 1 to 3 show results for the baseline model. Models 4 to 6 show results for the second-stage Heckman versions of the models. Lambda is the fitted probability of receiving PE funding estimated in Table 5. The definitions of all independent variables are presented in Table 2. Industry and year dummies are included to control for unobserved heterogeneity. The robustness standard errors adjusted for clustering by buyouts are provided in parentheses. N is the firm-year observations.
p < .01. **p < .05. *p < .1.
It is worth mentioning that the coefficient for d_MBA is no longer statistically significant (models 1, 3, 4, and 6). The difference in significance levels of d_MBA coefficients in models for the first and subsequent add-on acquisitions may be due to the ability of MBA holders to learn through experience, that is, by being involved in an acquisition for the first time. This interpretation aligns with the argument of Gibbons and Waldman (2004) that task-specific learning-by-doing leads to the accumulation of human capital.
Robustness Checks
We performed numerous robustness checks. First, as mentioned above, there are several classes of models that can be used in survival analysis. To provide further confidence in our results, we reran our models using complementary log-log (clog-log) and Cox proportional hazards models, with adjustments for standard errors via clustering (see Wilson et al., 2013). Notably the dependent variable in clog-log model is a categorical variable for an acquisition while the dependent variable in the Cox model is a hazard rate for an acquisition. The results of the models for the first and multiple add-on acquisitions show positive, statistically significant coefficients for the key explanatory variables. These positive coefficients indicate an increasing probability of acquisitions, which is consistent with the findings of our AFT models (see Table A4 in Appendix A). In addition, we employ propensity score matching to further address endogeneity concerns. The results, reported in Appendix A (Table A5), remain consistent with the originally reported results. Overall, our research design and robustness tests rule out a strong possibility that our key results could be explained by hidden unobservable characteristics of PE firms and buyouts.
We also consider different measures of diversity. Specifically, we use the Teachman (entropy) indices for gender and nationality. For nationality, we also consider cultural characteristics as defined in Hofstede (2011). For the continuous variable (i.e., age diversity), we employ the standard deviation and the coefficient of variation of age. We also control for the presence, rather than diversity, of LPT members with different socio-demographic characteristics. All unreported results are consistent with our main results and are available in Appendix A (Tables A6 and A7).
We examine possible complementarities in terms of diversity between LPT and non-PE board members. The results show a positive and statistically significant correlation between the percentage of female, foreign, and older PE professionals on LPTs and the respective percentages of non-PE board members. The positive correlation suggests a lack of complementarity across the diversity dimensions. We also reran our baseline model with controls for the diversity of non-PE board members. All unreported results are consistent with our main results and are available in Appendix A (Table A8).
We also examine the correlation between acquisition-driven growth and other PE performance measures. Due to a lack of publicly available data and other limitations of monetary PE performance measures (e.g., IRR and PMP), previous studies have used several exit-related measures, such as (a) the number (or proportion) of portfolio companies that exited via any route (see Bottazzi et al., 2008; Zarutskie et al., 2010); (b) the number/proportion of exits via initial public offerings (IPO; Dimov & Shepherd, 2005); and (c) the number/proportion of trade sale and IPO exits (Hochberg et al., 2007). The results indicate that sample buyouts exiting via the most desirable exit routes (IPOs and SBOs) are more likely to make a greater number of acquisitions and have shorter completion times than those exiting via trade sale. The results are reported in Appendix A (Table A9, Panel C) and are consistent with Hammer et al.’s (2017) findings. 13
Discussion and Conclusion
We examine the importance of PE human capital for the success of the acquisitive growth strategy in U.K. buyouts. We respond to calls from key research papers for a greater focus on “how” rather than “how much” firms grow, the alignment of growth mode with growth measures, and the increased use of sophisticated statistical methods (Coad, 2007; McKelvie & Wiklund, 2010; Shepherd & Wiklund, 2009). In particular, we capture the dynamics of “real hands” placed by PE firms by tracking changes in LPTs over time during the sample period. This approach explicitly links team-level characteristics to the success of the PE-led acquisitive growth strategy. By using completion time as our key variable, we can assess performance and attribute it to the success of the acquisitive growth strategy, rather than assuming what proportion of monetary performance (e.g., profitability or employment) is due to acquisitions (see Aguilera et al., 2024; Bauer & Friesl, 2024). Furthermore, we make a methodological contribution by developing a conditional risk set model for multiple sequential add-on acquisitions.
We contribute to the literature by combining different theoretical perspectives to study add-on acquisitions. For example, we extend the strategic entrepreneurship perspective by incorporating UET which suggests that socio-demographic diversity tends to be important when strategic decisions are made by group members. By examining both the functional human capital and socio-demographic diversity of PE professionals, we add a new dimension to previous research on human capital in the financial industry. We also provide insights into the role of PE professionals in fostering entrepreneurial growth, and more generally into the importance of investors’ human capital for their performance.
By examining the importance of human capital at the LPT level, our study adds a new dimension to previous research, which has mainly focused on human capital at the PE fund or firm level. More broadly, our work contributes to research on organizational goals and team-level characteristics. Building on the strategic entrepreneurship perspective and UET, we find that PE professionals help to exploit inorganic growth opportunities. Our findings highlight the importance of financial background, gender, and age diversity in the context of strategic leadership provided by PE professionals. We show that PE teams that embrace diversity not only do the “right thing,” but also outperform in terms of growth. The above results have important policy implications for the U.K. market, which is the second largest PE market in the world. For example, our results inform the debate surrounding new regulatory frameworks designed to promote diversity and inclusion (see Bank of England, 2023; Financial Conduct Authority (FCA), 2023; and Bloomberg UK, 2024), as well as the efforts of the PE/VC industry to promote responsible investment (see BVCA, 2023a). Our results also provide valuable insights for both PE firms and entrepreneurial companies. For instance, the diversity-related results inform how they can recruit professionals and make their respective teams more effective.
Our study also has several implications for future research. For example, acquisitions are likely to affect not only final (exit) but also interim performance, which is reported to LPs as cumulative distributions of realized investments and net asset values (NAVs) of unrealized investments. Portfolio companies that grow through acquisitions (and have not yet been exited) are part of unrealized investments and therefore part of PE firms’ estimated NAVs (Brown et al., 2018). With new acquisitions, portfolio companies grow in size, further increasing their valuation and likely increasing the NAVs of unrealized investments. In addition, limited partners scrutinize both realized and unrealized investment performance and penalize poor performance by not investing in new funds. Further research should therefore empirically examine the effect of the timely completion of acquisitions on the fundraising and survival of PE firms.
We acknowledge that our sample coverage is around 50% for PE professionals and approximately 25% for U.K. buyouts. Consequently, we do not have a comprehensive understanding of how U.K. PE firms allocate professionals to their funds and individual deals. There is also a lack of publicly available information regarding other aspects of PE deals. This is particularly true of performance measures such as IRR, PME, multiples, and fund returns. PE firms also tend to remain silent about failed deals. Due to the lack of public data on PE professionals at both the fund and deal levels, it is difficult to link the performance of individual professionals to that of funds and their respective portfolio companies. Future studies may attempt to collect comprehensive data on the allocation of PE professionals to funds and portfolio companies, examining how the allocation of professionals with different talents and socio-demographic characteristics affects performance.
Footnotes
Appendix A
Further Statistics for Add-on Acquisitions and PE Exits.
| Panel A: Completion time for subsequent add-on acquisitions | |||||||
|---|---|---|---|---|---|---|---|
| Sequence of acquisitions | Average completion time | ||||||
| Mean | Median | ||||||
| 1st | 745 | 574 | |||||
| 2nd | 517 | 296 | |||||
| 3rd | 353 | 199 | |||||
| 4th | 295 | 129 | |||||
| 5th | 260 | 203 | |||||
| 6th | 182 | 165 | |||||
| Panel B: Mean completion times for add-on acquisitions by characteristics of targets | |||||||
| Acquisitions with | Observations | Mean completion time | |||||
| (1) | (2) | ||||||
| Domestic target | 407 | 525.499 | |||||
| Cross-border target | 105 | 662.438 | |||||
|
|
2.085** | ||||||
| Same industry | 353 | 561.552 | |||||
| Different industry | 159 | 535.887 | |||||
|
|
–0.44 | ||||||
| Total | 512 | 533.582 | |||||
| Panel C: Add-on acquisitions and exits | |||||||
| Exit type | N (%) | Mean completion time | Mean number of acquisitions | Percentage of buyouts making at least | |||
| First | Multiple | One acquisition | Two acquisitions | Three acquisitions | |||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
| IPO | 26 (4.5%) | 711 | 466 | 0.692 | 26.9% | 15.4% | 15.4% |
| SBO | 222 (39%) | 624 | 475 | 0.851 | 36.0% | 19.4% | 15.3% |
| Trade sale | 270 (47%) | 813 | 698 | 0.378 | 24.8% | 8.1% | 5.2% |
| Liquidation | 55 (9.5%) | 677 | 465 | 0.455 | 23.6% | 14.5% | 9.1% |
Note. This table reports further statistics for add-on acquisitions and completion times. Panel A reports the completion time for sequential add-on acquisitions. Average (mean) completion time is in number of days. We stopped at the sixth add-on acquisition, since number of sample buyouts drops below 10 with the seventh and subsequent add-on acquisitions. Panel B reports the mean completion times for all add-on acquisitions by the nature of acquisition targets. Panel C reports add-on acquisitions and exit performance. Number (N) and percentage (%) of sample buyouts exited via different routes are presented in column 1. Average (mean) completion time for first add-on acquisition, in days, is presented in column 2. Average (mean) number of days between subsequent add-on acquisitions is presented in column 3. Average (mean) number of all acquisitions across exit routes is presented in column 4. Percentages of sample buyouts making at least one, two, or three add-on acquisitions are presented in columns 5, 6, and 7, respectively.
p < .01. **p < .05. *p < .1.
Acknowledgements
We would like to thank the editor, Silvio Vismara, and the three anonymous reviewers for their valuable feedback. Thanks also go to the participants of the seminar at the University of Bologna Business School in June 2024, the 2023 International Corporate Governance Society (ICGS) Conference in Madrid and the 2022 European Financial Management (EFM) Annual Meeting in Rome for their comments. Special thanks go to Massimiliano Barbi, Abdul Mohamed and Kevin Amess for their helpful suggestions. Ranko acknowledges the support of the Economic and Social Research Council (ESRC) research grant (ES/W010259/1).
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Economic and Social Research Council [grant number ES/W010259/1].
Data Statement
All the data associated with this article has been fully referenced within the article.