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The Relationship between Slack Resources and thePerformance of Entrepreneurial Firms: The Role of Venture Capital and Angel Investors

Tom Vanacker, Veroniek Collewaert and Ine PaelemanGhent University; Vlerick Business School; Ghent University

ABSTRACT In this study, we seek to further delineate factors that condition the relationshipbetween slack resources and rm performance. To do so, we develop and test a modelthat establishes the role of venture capital (VC) and angel investors as powerful externalstakeholders who positively moderate the slack–performance relationship. In addition, weprovide more insight into this relationship by examining differences between these two types of private investors and by examining the role of their ownership stakes. We test our hypothesesusing a sample of 1215 private rms, including VC-backed rms, angel-backed rms, andsimilar rms without such investors. We nd that the presence of VC investors positively

moderates the relationship between both nancial and human slack resources and rmperformance, while angel investors only positively moderate the effect of human resource slack.Further, VC investors are only marginally better at helping entrepreneurs to extract valuefrom human resource slack than angel investors and they are no better when it comes tonancial slack. Finally, we nd that the impact of nancial and human resource slack on rmperformance is more positive in VC-backed rms when investors hold high ownership stakes,an effect which is signicantly stronger than when angel investors hold high ownership stakes.

Keywords: angel nancing, entrepreneurship, rm performance, slack resources, venturecapital

INTRODUCTION

Whether it is the abundance or scarcity of slack resources that is most benecial to rmperformance is a heavily debated topic (e.g., Bradley et al., 2011a). Prior research hasgenerally argued for an inverted U-shaped relationship between slack resources and rmperformance to reconcile these opposite views (Bourgeois, 1981; George, 2005; Nohriaand Gulati, 1996; Tan and Peng, 2003). Nevertheless, simply having resources is notenough to create value out of those resources (Sirmon et al., 2007). More recently,

Address for reprints : Tom Vanacker, Department of Accountancy and Corporate Finance, Ghent University,Kuiperskaai 55E, 9000 Gent, Belgium ([email protected]).

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© 2013 John Wiley & Sons Ltd and Society for the Advancement of Management Studies

Journal of Management Studies 50:6 September 2013doi: 10.1111/joms.12026



scholars have therefore devoted increasing attention to when, where, and how slack resources benet rm performance. For instance, research has shown how the value of slack is inuenced by the environment in which these resources are deployed (Bradleyet al., 2011a; George, 2005). Moreover, how slack resources are managed may alsoinuence their performance consequences. Simsek et al. (2007), for instance, found thatmanagers’ capability to sense the market makes them better able to allocate slack towardsentrepreneurial actions, which enhances the performance consequences of theseresources.

While these prior studies have been informative in opening up the black box betweenslack resources and rm performance, they implicitly assume that managers ‘go it alone’when managing their resources. However, managers rarely operate in isolation and oftenhave to deal with powerful exchange partners who may affect managers’ discretion overresources (Hambrick and Finkelstein, 1987). Venture capital (VC) and angel investorsare such partners in entrepreneurial rms; in addition to providing nancial resources in

return for part of the ownership of rms, they actively monitor and inuence how theirportfolio rms are run after the investment (Mason and Harrison, 1996; Sapienza, 1992;Sapienza et al., 1996). In so doing, these investors may substantially affect entrepreneurs’discretion over their resources and thus inuence how these resources are managed(Hambrick and Finkelstein, 1987). Using insights from extant slack research and mana-gerial discretion theory, we set out to examine how powerful exchange partners, such asVC and angel investors, inuence the relationship between slack resources and rmperformance.

Our paper makes three primary contributions. First, we contribute to the literature on

slack resources. Researchers have recently acknowledged that resources do not exploitthemselves (Sirmon et al., 2007). In examining when, where, and how value is extractedfrom slack resources, specic attention has been paid to the role of industry environments(Bradley et al., 2011a; George, 2005) and managers (Bradley et al., 2011c; Simsek et al.,2007). Nevertheless, when managing their resources, entrepreneurs rarely act in isola-tion. Private investors, such as VC and angel investors, are typically one of the mostimportant owners in entrepreneurial rms, ranked second behind entrepreneurs them-selves (George et al., 2005). Unfortunately, the role of these private investors in resource value creation has been largely overlooked. We argue that VC and angel investors – through their active involvement in entrepreneurial rms – will act as a source of resource value creation. In so doing, we address an important gap in the literature asidentied by Barney et al. (2011, p. 1306): ‘start-up and growing rms may be whollyowned by their founders or have involvement of angel investors and venture capitalists.Studies to date have not explicitly compared the different processes of resource andcapability development in these different ownership contexts . . .’. Our study is one of therst to show how outside investors not only contribute resources as such, but also help increating value out of those resources.

Second, we contribute to the literature on managerial discretion. Researchers haveexamined a host of factors at the national, industry, rm, and individual level that

enhance or restrict the internal discretion of managers (e.g., Carpenter and Golden, 1997;Crossland and Hambrick, 2007; Finkelstein and Hambrick, 1990; Hambrick and Abrahamson, 1995). Despite this rich literature, Quigley and Hambrick (2012) recently

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noted that little attention has been paid to the idea that powerful parties might inuencemanagerial discretion. This is especially the case for powerful external parties, such as VCand angel investors. Our study suggests that managerial discretion is distributed acrossorganizational boundaries, with powerful external parties that are not managers of therm. [1] Moreover, contrary to the general assumption that powerful parties may restrictdiscretion (e.g., Hambrick and Finkelstein, 1987), we argue and show that these externalparties may also enhance discretion, something which was only briey suggested byThompson (1967).

Finally, our study contributes to governance research. Researchers have long studiedthe relationship between ownership and rm performance, especially in established andnew public rms, but mixed ndings have emerged (Dalton et al., 2003). We do not focuson the direct relationship between ownership and rm performance, but rather studyhow different private investors (VC versus angel investors) with different ownershipstakes (high versus low) inuence resource value creation in the context of private rms.

While prior research has argued that different types of owners may have different goalsfor investing (Zahra, 1996) and different preferences regarding the allocation of slack resources (Kim et al., 2008), this evidence again comes from investors in public rms.Our study highlights some important differences between different types of privateinvestors, challenging the basic assumption that outside blockholders of equity (as ahomogeneous group) by denition have both the incentive and inuence to affect rmdecision making (Dalton et al., 2003). We provide new insights into the role of investorprofessionalism, ownership, and their joint effects on the performance consequences of slack resources in private rms.

THEORY

Slack Resources, Firm Performance, and Managerial Discretion

Slack resources are dened as ‘potentially utilizable resources that can be diverted orredeployed for the achievement of organizational goals’ (George, 2005, p. 661). Most of our insights regarding the consequences of slack come from samples of public rms(Bromiley, 1991; Danneels, 2008; Greenley and Oktemgil, 1998; Kim et al., 2008;Mishina et al., 2004), multinational organizations and their divisions (Nohria and Gulati,1996, 1997), or established privately-held rms (George, 2005; Mellahi and Wilkinson,2010; Simsek et al., 2007). It is only more recently that scholars have studied slack in young, small entrepreneurial rms as we do in this study (Bradley et al., 2011a, 2011b;Patzelt et al., 2008). While entrepreneurial rms are often portrayed as being resourceconstrained, this does not imply that slack cannot be present in these rms. First,entrepreneurial rms differ signicantly in their early resource endowments (Shane andStuart, 2002). Second, because all resources controlled by a rm are rarely ever fullyutilized, there is always some slack (Bradley et al., 2011b).

Although slack resources vary in type, the majority of previous studies have focused

on nancial slack and more specically the level of liquid assets in excess of thoseneeded for basic operating expenses (e.g., Bradley et al., 2011a, 2011b; George, 2005;Kim et al., 2008). In entrepreneurial rms, nancial slack may result from early

T. Vanacker et al.1072




operations as well as from the initial stock of capital (Bradley et al., 2011a). A relativelysmall but growing stream of research has also focused on human resource slack, whichrefers to the number of employees in excess of those needed for operational demands(e.g., Mellahi and Wilkinson, 2010; Mishina et al., 2004; Welbourne et al., 1999). Wefocus on both types of resource slack because both nancial and human resources arecritical for the emergence and development of entrepreneurial rms (Cooper et al.,1994).

Multiple theoretical perspectives have been advanced to understand the perform-ance consequences of slack resources, but opposing insights have emerged. On the onehand, behavioural theorists argue that slack resources will increase experimentation,innovation, and risk-taking (Bromiley, 1991; Cyert and March, 1963; George, 2005).Slack allows rms to experiment with new projects, such as introducing products andentering markets (Hambrick and Snow, 1977). Although such projects may be risky,they are critical for the performance of entrepreneurial rms (Sapienza et al., 2006;

Zahra, 1995). Moreover, slack shields rms from environmental turbulence (O’Brien,2003). On the other hand, organizational economists argue that slack is detrimental forrm performance as it reduces the discipline that is exercised in the selection, support,and termination of investment projects (Nohria and Gulati, 1997). Additionally,resource constraint theorists argue that slack decreases entrepreneurial ingenuity(Baker and Nelson, 2005; Mosakowski, 2002). Finally, slack may make entrepreneurscomplacent and overly optimistic (Kim et al., 2008). Debruyne et al. (2010), forinstance, show that the presence of more nancial resources makes managers believethey are able to react effectively to competitive attacks, but also makes them less

motivated to do so.[2]

Scholars have reconciled these opposing views by arguing for an inverted U-shapedrelationship between slack and rm performance. Specically, the performance benetsof slack as advanced by behavioural theorists increase rapidly with slack, but then leveloff because there are only so many valuable opportunities out there which entrepreneurscan imagine (Hambrick and Finkelstein, 1987; Nohria and Gulati, 1997). The costs of slack are limited when slack is low, but start to increase rapidly when slack gets beyonda certain level (Nohria and Gulati, 1997). The net effect of these two mechanisms, aninverted U-shaped relationship, suggests that holding an intermediate level of slack isoptimal in any organizational setting (Nohria and Gulati, 1997).

Slack resources, however, do not exploit themselves and simply possessing resourcesis not enough to create value out of those resources (Sirmon et al., 2007). Recently,scholars have started to open up the black box between slack resources and rmperformance by examining when, where, and how these resources inuence perform-ance. In this regard, specic attention has been paid to the role of managers (Simsek et al., 2007). Bradley et al. (2011c), for instance, showed that managers in rms withgreater resource constraints learn to use their resources more effectively over time. Animplicit assumption in these studies is that managers have signicant discretion overtheir resources. According to managerial discretion theory though, managers vary in

how much discretion they possess, ranging from very little to a great deal (Hambrick and Finkelstein, 1987). That this may be important to consider was hinted at byBradley et al. (2011a) who showed that nancial slack is most benecial for rm





performance in low-discretion environments (i.e., hostile and stable industries). Nev-ertheless, much remains to be learned about factors that may condition the slack– performance relationship.

Managerial discretion theory stipulates that the degree of managerial discretion is afunction of individual, organizational, and environmental factors (Hambrick andFinkelstein, 1987). At the individual level, discretion is determined by the degree towhich managers are able to envision multiple courses of action (Carpenter and Golden,1997; Hambrick and Finkelstein, 1987; Thompson, 1967). Similar to the entrepreneurialprocess of discovering, evaluating, and exploiting opportunities (Shane, 2000;Venkataraman, 1997), the different courses of action managers are able to envision islargely determined by their cognitive limitations. At the organizational level, slack resources themselves play a key role in inuencing managerial discretion (Finkelstein andBoyd, 1998; Finkelstein and Hambrick, 1990), while at the environmental level factorssuch as product differentiability and capital intensity inuence discretion (Finkelstein and

Hambrick, 1990; Hambrick and Abrahamson, 1995).Most relevant to our study, and typically ignored by previous research, discretion

theory indicates that powerful stakeholders may inuence managerial discretion(Hambrick and Finkelstein, 1987). Specically, stakeholders may create discretion byraising opportunities that will not come to mind of entrepreneurs who act alone.Thompson (1967), for instance, argues that outside investors may help organizations innding more domains that are protable. Conversely, stakeholders may constrain entre-preneurs’ deviant discretion or latitude to take action whenever such ‘an action liesoutside the “zone of acceptance” of [those] powerful parties who hold a stake in the

organization’ (Hambrick and Finkelstein, 1987, p. 374). In this study, we focus on outsideinvestors, including VC and angel investors, who are generally viewed as powerfulstakeholders in entrepreneurial rms. We argue that these investors will inuence entre-preneurs’ discretion over resources through their active involvement. Consequently, theyare expected to inuence the underlying mechanisms which drive the performanceconsequences of slack resources in entrepreneurial rms. Below, we elaborate on thisclaim.

How VC and Angel Investors Inuence Entrepreneurial Discretion and

their Impact on the Performance Consequences of Slack ResourcesVC and angel investors are typically among the most important owners in entrepre-neurial rms, ranked second behind entrepreneurs themselves (George et al., 2005). Bothtypes of investors are value-adding investors or providers of ‘smart money’ (Bruton et al.,2010; Mason and Harrison, 1996; Sapienza et al., 1996). This implies they extensivelymonitor the progress of their portfolio rms and contribute value-adding services inaddition to merely providing nancial resources. As such, the presence of VC and angelinvestors may inuence an entrepreneur’s discretion over slack resources in a number of ways.

First, entrepreneurs are limited in the number of opportunities they can perceive orcreate (Shane, 2000; Shane and Venkataraman, 2000), which explains a decreasing value for each additional amount of slack (Nohria and Gulati, 1997). VC and angel





investors, however, often spend a considerable amount of time contributing value-adding activities to portfolio rms. Gorman and Sahlman (1989), for instance, showed that VCinvestors who sit on the board devote on average 80 hours of on-site time and 30 hoursof phone time per year in direct contact with each of their portfolio rms. Angel investorsare known to be even more actively involved in their portfolio rms compared to VCinvestors (Van Osnabrugge, 2000). Further, VC and angel investors often bring anetwork of ties with potential suppliers, customers, nanciers, and other critical stake-holders (Hochberg et al., 2007; Prowse, 1998). They provide entrepreneurs with a rangeof contacts that are typically unavailable when entrepreneurs proceed on their own(Maurer and Ebers, 2006; Prowse, 1998). In addition, VC and angel investors possessknowledge on the market, industry, and competitors of their portfolio rms, whileentrepreneurs are often described as lacking a broader view on their markets (Fiet, 1995;Maula et al., 2005). As a consequence of these value-adding activities, entrepreneursobtain access to new information, which signicantly affects their ability to discover new,

value-creating opportunities (Shane and Venkataraman, 2000). By enabling entrepre-neurs to consider these opportunities that they would otherwise not have been aware of,VC and angel investors increase entrepreneurial discretion (Thompson, 1967). Slack resources exactly benet rm performance by providing entrepreneurs with the means topursue new opportunities. Thus, VC and angel investors are expected to amplify slack resources’ benecial effect on rm performance by broadening entrepreneurs’ range of valuable opportunities to pursue.

Second, both VC and angel investors will generally advocate running rms in the mostefcient way possible (Berkus, 2006; Chemmanur et al., 2011). Not only will they try to

avoid the wasteful use of their investments, they will also push entrepreneurs’ limits tomake them stretch their resources as much as possible. Board and voting rights provideVC and angel investors with the power to enforce such entrepreneurial behaviour by, forexample, requiring investor ratication before key strategic decisions can be executed(Cumming, 2008). Additionally, contracting and active monitoring should form animportant deterrent against overoptimistic behaviour on the entrepreneur’s part. To thisend, investors can again rely on board and voting rights, which often include rights todismiss or replace the top management team (Parhankangas and Landström, 2006).Developing a more personal relationship with entrepreneurs may be another way toavoid overoptimistic and complacent behaviour (Fiet, 1995). Forbes (2005) indeed showsthat entrepreneurs whose rms have attracted VC are less overcondent than entrepre-neurs whose rms have not. As such, VC and angel investors should decrease entrepre-neurs’ deviant discretion and limit the costs of slack resources related to lax discipline.Thus,

Hypothesis 1: The impact of (a) nancial slack and (b) human resource slack on rmperformance is more positive in VC-backed rms compared to their peers withoutsuch investors.

Hypothesis 2: The impact of (a) nancial slack and (b) human resource slack on rmperformance is more positive in angel-backed rms compared to their peers withoutsuch investors.





Differences between and among VC and Angel Investors, their Inuenceon Entrepreneurial Discretion, and the Performance Consequences of Slack Resources

All stakeholders in a rm will have tolerance for some actions and intolerance for others

(Hambrick and Finkelstein, 1987). Because many entrepreneurial actions will almostalways lie outside the ‘zone of acceptance’ of some stakeholder, discretion theory indicatesthat scholars should focus on those stakeholders whose withdrawal or resistance toentrepreneurial actions would pose a serious setback to entrepreneurs (Hambrick andFinkelstein, 1987). For entrepreneurial rms, VC and angel investors, as blockholders,are often portrayed as qualifying this condition. Blockholders are assumed to have boththe incentives and inuence to affect managerial discretion (Dalton et al., 2003). Never-theless, treating private investors as a monolithic group of blockholders ignores someimportant differences between and among these investors (e.g., Bruton et al., 2010). Toget more insight into the incentives and inuence of VC and angel investors to shapeentrepreneurs’ discretion, we focus on differences between these investors and on the roleof their ownership stakes.

VC versus angel investors. So far, we have argued that both VC and angel investors willincrease the value and decrease the costs of slack, without explicitly hypothesizing differences between both types of investors to do so. Despite their similarities, however,VC and angel investors also have their differences. Specically, VC investors are pro-fessional investors that use institutional money to invest (Sapienza, 1992). Angel investors

are individuals who often have entrepreneurial experience and use their personal fundsto invest (Mason and Harrison, 1996). Due to differences in operation modes betweenthese two types of investors, we argue that VC investors will be better able than angelsto inuence entrepreneurs’ discretion over slack resources, thereby more stronglyenhancing slack’s value while curtailing its costs.

First, VC investors invest in a larger portfolio of rms than angels among whom thosewith three or more investments are a minority (Van Osnabrugge, 1998). This is impor-tant because individuals are better able to assimilate and exploit knowledge when theyhave previously accumulated experience within related knowledge domains (Cohen andLevinthal, 1990; De Clercq and Dimov, 2008). While angel investors often tend to investin industries in which they have gained some experience, the limited empirical researchthat is available suggests they have less industry experience than VC investors (VanOsnabrugge, 2000). Additionally, within a VC rm one can draw on the expertise andexperience of multiple investment managers, whereas angels mainly have to rely onthemselves to provide valuable advice and information. Taken together, this suggeststhat VC investors may have a broader knowledge base than angels to draw from,enabling them to bring more opportunities to the entrepreneur’s attention and to offerthem a broader perspective. As such, they should be better able to increase the value of a given level of slack resources than angel investors.

Second, VC investors tend to write extensive contracts by putting together specicshareholder agreements and stipulating differentiated shareholder rights (Cumming,2008). Angel investors, however, rely more on relational governance than contractual





governance (Fiet, 1995). This implies that angel contracts are generally moreentrepreneur-friendly, have weaker control rights, use less contractual provisions, andare used more from a transactional than a control point of view (Goldfarb et al., 2007).Prowse (1998), for instance, argues that a large proportion of the angel community isunsophisticated in terms of ensuring adequate protection in the investment contractsthey write. Consequently, VC investors are expected to decrease the cost of slack and laxdiscipline more so than angel investors as they can rely on more professional andcomplete contracts to force entrepreneurs not to invest in projects guided by overcon-dence, to work more efciently and to avoid complacent behaviour. Thus,

Hypothesis 3: The impact of (a) nancial slack and (b) human resource slack on rmperformance is more positive when rms are backed by VC compared to angelinvestors.

VC and angel investors’ incentives through ownership. The notion of VC and angel investorsinuencing entrepreneurial discretion is rooted in these investors’ involvement in theirportfolio companies. Discretion theory indicates that to qualify as a constraint to theentrepreneur’s discretion, investors who perceive a certain action as being outside of their ‘zone of acceptance’ must not only be powerful, but must also be able to reactrelatively immediately to the action itself (Hambrick and Finkelstein, 1987). The latter isunlikely when investors are not sufciently involved in portfolio rms.

Providing extensive value-adding services and monitoring entrepreneurial actions,however, are costly activities to investors (Manigart et al., 2002). Hence, without the

proper incentives investors may not be sufciently motivated to be actively involved intheir portfolio companies and thus to inuence entrepreneurial discretion. An importantdeterminant of investors’ incentive to expend effort on portfolio rms is their ownershipstake (Brush et al., 2000; Zahra, 1996). A high ownership stake should make investorsmore motivated and committed to provide information and advice to entrepreneurs andto monitor their actions. When investors hold a low ownership percentage, however,they may be less willing to bear the costs of being actively involved and will likely beinsufciently incentivized to do so (Brush et al., 2000). Being more incentivized toprovide value-adding services, investors with a high ownership stake should increase thediscretionary set of entrepreneurs more so than if they had a low ownership stake.Moreover, when investors have a high ownership stake, they also have more incentivesto closely monitor entrepreneurs, thereby limiting entrepreneurs’ ability to become laxwhen pursuing new projects (Zahra, 1995). Together, private investors with high own-ership stakes are expected to enhance the performance consequences of slack resourcesmore so than private investors with low ownership stakes as they allow entrepreneurs topursue more valuable opportunities with a given set of slack resources, while reducing their ability to pursue lax investments.

We expect this line of reasoning to hold especially true for professional VC investorsgiven that nancial return maximization and cost efciency is an absolute priority to

them (Cumming et al., 2007). Without the necessary incentives, they will have aninclination to prefer to execute less rather than more costly activities, such as activepost-investment involvement. Contrary to VC investors, angels conduct investments





both for their return potential and for personal motives, such as fun and gaining prestigein the entrepreneurial community (Mason and Harrison, 2002). Angel investors arehence likely to be more generally incentivized and more active investors irrespective of their equity stakes (Van Osnabrugge, 2000). Thus, the impact of ownership stake on therelationship between slack and rm performance is likely to be negligible in angel-backedrms. Professional VC investors, on the other hand, are more likely to require a highownership percentage to become more actively involved in their portfolio rms. Takentogether, this leads us to hypothesize:

Hypothesis 4 : The impact of (a) nancial slack and (b) human resource slack on rmperformance is more positive in VC-backed rms when VC investors have a highownership stake as compared to a low ownership stake.

Hypothesis 5 : The impact of (a) nancial slack and (b) human resource slack on rm

performance is more positive when VC investors have a high ownership stake ascompared to when angel investors have a high ownership stake.

METHODS

Sample and Data Sources

We use a sample of 1215 Belgian rms. Given our research interest, we constructed asample of rms that raised VC or angel nancing and similar rms that did not raise such

nancing. The Belgian research setting provides us with two advantages. First, we hadaccess to databases from professional associations and networks which allowed us toconstruct a sample comprising both formal VC and angel investments. Studies compris-ing both VC and angel investments are rare (Bruton et al., 2010). Second, all Belgianrms (with limited liabilities of shareholders) are required to le nancial statements withthe Belgian Central bank. As such, we have detailed information on all VC- andangel-backed rms and were able to construct a sample of similar rms which did notraise such nancing. Again, the detailed data we have available on entrepreneurial rmsis rare (Brav, 2009).

We identied 120 rms backed by formal VC investors by using a database providedby the Belgian Venture Capital and Private Equity Association (BVA). The BVA wasfounded in 1986 and is the professional association that represents the Belgian VC andprivate equity community. The BVA provided us a random sample of one third of allinitial VC investments conducted by its members between 1994 and 2004. It includesseed and start-up investments and the provision of growth capital, but excludes buy-outs.We also identied 90 rms backed by informal VC investors that received initial angelnancing between 1994 and 2004. For this purpose we used multiple data sources,including deal lists from Business Angel Networks, data from the Global Entrepreneur-ship Monitor and directories of high-technology rms. Additionally, for each VC- and

angel-backed rm, we searched for information on their ownership structure by collect-ing legally required statements on capital increases that are published in the Belgian LawGazette.





For each VC- and angel-backed rm, we selected up to ve comparable rms which didnot raise such nancing. For this purpose, we used a database comprising all nancialaccounts of Belgian rms. Our matching procedure is similar to that used by Puri andZarutskie (2012). We rst selected rms with the same age, dened as the number of yearssincelegal incorporation because theaccumulation of slack resourcesand theeffectiveness bywhich rms deploy slack is age dependent (George, 2005). Within the group of rms foundedin the same year, we selected rms operating in the same industry based on 4-digit industrycodes (or 3-digit codes whenever 4-digit codes were unavailable). This is important becausethe industry environment inuences the value of slack (Bradley et al., 2011a; George, 2005).Finally,within the group ofrmsfounded inthe sameyearand operating in the sameindustryenvironment, we selected rms with a similar size because slack is size dependent (George,2005).Unreported statistics conrmthat oursamples of VC-backedrms andtheir peers andangel-backed rms and their peers respectively do not differ signicantly from each other intermsofage, industry,andsizedistribution(these unreported statistics andinsightsfrom other

matching procedures are available upon request).

Dependent Variable

We operationalize performance as gross prot, dened as the difference between salesand the cost of goods produced (as in George, 2005). This measure is scaled by totalassets to make it comparable for rms with a different size and to reduce heteroscedas-ticity concerns (Brav, 2009). We do not use performance measures based on net incomebecause variability in the tax treatment of income in private rms undermines thereliability of such performance estimates (George, 2005). Gross prot on total assets ismeasured three years after investment (or equivalent year for the rms which do not raiseVC or angel nancing) to help establish the direction of causality (Bradley et al., 2011b).It should also allow for sufcient time for entrepreneurs to transform slack into usableresources and address new opportunities or threats, which will subsequently inuenceperformance. Additionally, prior research suggests that most value added by VC inves-tors is created in the rst years after initial investment (Bertoni et al., 2011) and that exitsstart to emerge from three years after the investment (Puri and Zarutskie, 2012). As such,we believe three years represents a valid time frame for our study. [3]

Independent VariablesFinancial slack. We measure nancial slack as the amount of cash and cash equivalentsscaled by total assets (Kim et al., 2008; Voss et al., 2008) in the year of investment forVC- or angel-backed rms and equivalent year for their peers. This measure is adjustedfor industry norms by subtracting the median ratio of cash and cash equivalents to totalassets for all rms in the same 3-digit NACE (Statistical Classication of Economic Activities in the European Community) industry as the focal rm (Bromiley, 1991;George, 2005). Thus, nancial slack represents a close estimate of excess cash resourcesheld by rms compared to industry norms.

Human resource slack. We dene human resource slack as employment cost relative to totalassets in the year of VC or angel investment and equivalent year for rms which do not





raise such nancing. This measure is adjusted for industry norms by subtracting themedian ratio of employment cost on total assets for all rms in the same 3-digit NACEindustry as the focal rm. Larger values indicate greater levels of human resource slack.Our measure is similar to previous studies which dened human resource slack as thenumber of employees relative to sales adjusted for industry norms (Mellahi andWilkinson, 2010; Mishina et al., 2004; Voss et al., 2008). However, it differs in tworegards: (1) we use employment cost instead of the number of employees; and (2) we scaleemployment cost by total assets rather than sales.

We use employment cost rather than the number of employees because the formerforms an integral part of the prot and loss account, while the latter is only reported ina supplementary social balance sheet. The drawback of using the number of employeesis that our sample size decreases due to missing data. Fortunately, the available dataindicates that employment cost on total assets and number of employees on total assetsare highly correlated (0.76; p < 0.001). We hence use employment cost as a proxy for the

number of employees in a rm. Moreover, employment cost not only captures thequantity, but also the quality of employees. Employees with more human capital (highereducation and more experience) will be more costly to employ. However, individualswith more human capital are likely to be more effective and efcient in performing aparticular task (Becker, 1964). Hence, by using employment cost rather than the numberof employees we take into account that not all employees contribute the same amount of human capital.

We scale employment cost by total assets rather than using sales for two reasons. First,VC investors are known to invest in high-growth, high-potential rms without immediate

sales prospects, even in low-tech industries (Puri and Zarutskie, 2012). With zero sales wewould have been unable to calculate human resource slack at the time of investment forsome VC-backed rms. Second, only large Belgian rms are required to report their salesgures. Using sales would thus lead to a bias in that we would only be able to calculatehuman resource slack for these larger rms.

VC (angel) dummy. We construct a VC (angel) dummy variable which equals one whenrms raise nancing from a VC (angel) investor and zero for their peers without suchnancing. Prior research indicates that the mere presence of VC (angel) investors asshareholders may inuence the operations of their portfolio rms (e.g., Ehrlich et al.,1994; Puri and Zarutskie, 2012). Further, we calculate the interaction between the slack measures and VC (angel) dummy to study whether the relationship between slack andperformance is moderated by the presence of VC (angel) investors.

High ownership percentage. We make a distinction between VC or angel investors with ahigh versus low ownership stake in their portfolio rms. To this end, we create a dummy variable which equals one for investors with ownership stakes starting at 25 per cent andzero otherwise. We focus on the lead investor because this investor typically plays themost important role in the governance of the investment and provision of value-adding

services (Wright and Lockett, 2003). Further, we use a dummy variable rather than acontinuous ownership variable because it is better aligned with our theoretical argu-ments. Specically, investors (and especially VC investors) may require a sizable part of





the ownership to be sufciently incentivized to bear the costs of being actively involvedin their portfolio rms (Brush et al., 2000). Moreover, the 25 per cent threshold reectsan important blocking minority, hence allowing investors to control a rm’s strategicagenda and to inuence its strategic choices (Zahra, 1995). The 25 per cent threshold isimportant in Belgian legislation because as of this threshold shareholders are able toblock important corporate decisions, including decisions regarding capital increases,mergers, and acquisitions. We also calculate interactions between the high ownershippercentage dummy and our slack variables.

Control Variables

We control for rm, industry, and year effects. With regard to rm effects, we control forrm age, rm size, patents, and other forms of slack. Firm age is measured as the yearssince formal incorporation at the year of investment or equivalent year for matched

rms. Firm size is measured as the natural logarithm of property, plant, and equipmentat investment or equivalent year for matched rms. We also control for the knowledgeintensity of rms by including the natural logarithm of the cumulative number of patentsapplied for (plus one) up to investment or equivalent year for matched rms. We furthercontrol for the effect of other forms of slack. Potential slack is measured as the debt-to-equity ratio adjusted for industry norms at investment or equivalent year for matchedrms (George, 2005). Recoverable slack is measured as the sum of accounts receivablesand inventory on total assets adjusted for industry norms (Bradley et al., 2011a) and isalso measured at investment or equivalent year for matched rms.

There exists non-randomness in the probability that rms raise VC or angel nancing (Cosh et al., 2009). Specically, entrepreneurs search for (or eschew) certain outsideinvestors and/or outside investors seek out certain types of rms. If studies fail to controlfor such selection issues regressions may produce biased results (Cosh et al., 2009). Toaddress this possibility, we include an Inverse Mills Ratio or selection correction as acontrol. Following prior research (Cosh et al., 2009), we employed the Heckman proce-dure. First, we develop selection models predicting the receipt of VC or angel nancing respectively using insights from previous research on nancing decisions in entrepre-neurial rms (Cosh et al., 2009; Vanacker and Manigart, 2010). Specically, cash ow tototal assets, intangible assets to total assets, the natural logarithm of the number of patents applied for (plus one), tangible assets to total assets, overdue short term prioritydebt on total assets, debt-to-equity ratio, interest coverage ratio, and the natural loga-rithm of rm size were used in the selection models. The ratio of intangible assets to totalassets, for instance, is described as a good proxy for growth potential (Villalonga, 2004)and is related to the probability of raising outside equity nance (Vanacker andManigart, 2010). The number of patents applied for is another variable on whichespecially VC investors are likely to select (Hsu and Ziedonis, 2013). Second, a correctionis made for selection by incorporating the Inverse Mills Ratio obtained from the selectionmodels into the regression models of interest. [4]

With regard to industry effects, we control for industry protability measured as thenatural logarithm of median gross prot based on all rms in the same 3-digit NACEindustry as the focal rms. High industry protability is not only likely to inuence the





performance level of the focal rms, but may also indicate opportunities to generate slack (George, 2005). We further included industry dummy variables to capture broaderindustry-level effects: manufacturing, construction, wholesale and retail trade, informa-tion and communication technologies (ICT), biotechnology, business services, and other.We used ICT as the reference industry.

Finally, we include dummy variables for the years in which rms received nancing from VC or angel investors and the years in which their peers entered our dataset. These variables help to control for the effects of any general economic event or trend.

RESULTS

Descriptive Statistics

Table I provides an overview of the means, standard deviations, and correlations

between the variables used in the empirical models. Panel A focuses on the sample of VC-backed rms and their peers without such investors, while Panel B focuses on thesample of angel-backed rms and their peers without such investors. We note that bothfor VC-backed rms and their peers and angel-backed rms and their peers correlationsbetween nancial and human resource slack are low and insignicant. This indicates thatrms with nancial slack do not necessarily have human resource slack or vice versa.

Hypothesis Tests

Table II gives the results of the OLS regressions using the sample of VC-backed rmsand their peers, and angel-backed rms and their peers respectively. Models 1 and 2 arethe baseline models without interactions. In Model 3, we focus on VC-backed rms andtheir peers and add interactions between the VC dummy and our slack measures. Thismodel is used to test Hypothesis 1. In Model 4, we focus on angel-backed rms and theirpeers and add interactions between the angel dummy and our slack measures. Thismodel is used to test Hypothesis 2. Variance ination factors (VIFs) in all models are wellbelow 10 and hence do not indicate that multicollinearity may be unduly inuencing ourresults (Kutner et al., 2005).

Hypothesis 1 stated that the impact of (a) nancial slack and (b) human resource slack on performance would be more positive in VC-backed rms compared to their non-VC-backed peers. The moderating effect of VC on the nancial slack–performancerelationship is signicant and positive in the main effects interaction from Model 3( b = 0.057; p < 0.10). As illustrated in Figure 1, the relationship between nancial slack and performance is more positive in VC-backed rms than in non-VC-backed rms.With regard to human resource slack, although the VC–human resource slack interac-tion is not signicant, the interaction between the VC dummy and the squared term of human resource slack is positive and signicant ( b = 0.038; p < 0.05). Figure 2 shows thatan increase in human resource slack increases performance in a linear fashion in non-

VC-backed rms, but in an exponential fashion in VC-backed rms. Hence, a givenincrease in human resource slack results in a stronger increase in performance forVC-backed rms. Overall, we nd support for Hypothesis 1a and 1b.





T a b l e I . D e s c r i p t i v e s t a t i s t i c s a n d c o r r e l a t i o n m a t r i x

P a n e l A : V C - b a c k e d r m s a n d t h e i r p e e r s ( n = 6 3 1 )

V a r i a b l e

V C - b a c k e d r m s

P e e r g r o u p

C o r r e l a t i o n s

M e a n

S D

M e a n

S D

1

2

3

4

5

6

7

8

9

1 0

1 F i r m a g e

6 . 2 3

8 . 6 5

6 . 2 5

8 . 2 2

1 . 0 0

2 F i r m s i z e

4 . 3 7

2 . 5 9

3 . 9 5

2 . 1 2

0 .

2 9

1 . 0 0

3 P a t e n t s

0 . 1 0

0 . 4 4

0 . 0 1

0 . 1 2

0 .

0 8

0 .

1 2

1 . 0 0

4 P o t e n t i a l s l a c k

2 . 8 5

9 . 7 6

4 . 8 0

1 0 . 2 4

- 0 . 0 7

0 . 0 6

0 . 0 2

1 . 0 0

5 A b s o r b e d s l a c k

- 0 . 2 0

0 . 2 6

- 0 . 0 7

0 . 2 9

0 .

1 3

- 0

. 1 5

- 0 . 0 6

0 .

1 8

1 . 0 0

6 I n d u s t r y p r o t a b i l i t y

5 . 9 3

0 . 5 8

5 . 9 2

0 . 5 8

0 . 0 2

0 .

1 0

0 .

1 0

- 0 . 0 6

0 . 0 4

1 . 0 0

7 V C d u m m y

1 . 0 0

0 . 0 0

0 . 0 0

0 . 0 0

0 . 0 0

0 . 0 7

0 .

1 5

- 0 . 0 7

- 0

. 1 6

0 . 0 1

1 . 0 0

8 H i g h o w n e r s h i p %

0 . 5 2

0 . 5 0

N . A .

N . A .

- 0 . 0 5

- 0 . 1 6

- 0

. 2 4

- 0 . 1 4

0 . 0 1

- 0

. 2 3

N . A .

1 . 0 0

9 F i n a n c i a l s l a c k

0 . 1 2

0 . 2 4

0 . 0 8

0 . 2 0

- 0 . 0 6

- 0

. 2 2

0 . 0 7

- 0

. 2 1

- 0

. 3 0

0 . 0 1

0 . 0 7

0 . 1 5

1 . 0 0

1 0 H u m a n

r e s o u r c e s l a c k

- 0 . 1 1

0 . 2 5

- 0 . 0 9

0 . 2 8

0 .

1 3

0 . 0 4

0 . 0 1

0 . 0 1

0

. 2 1

0 . 0 7

- 0 . 0 3

0 . 1 7

- 0 . 0 5

1 . 0 0

1 1 P r o t a b i l i t y

0 . 3 4

0 . 3 4

0 . 4 0

0 . 3 6

0 .

0 9

0 . 0 5

- 0 . 0 3

- 0 . 0 2

0

. 2 4

0 . 0 2

- 0 . 0 6

0 . 1 2

- 0 . 0 4

0 .

4 7

P a n e l B : A n g e l - b a c k e d r m s a n d t h e i r p e e r s ( n = 4 4 6 )

V a r i a b l e

A n g e l - b a c k e d r m s

P e e r g r o u p

C o r r e l a t i o n s

M e a n

S D .

M e a n

S D

1

2

3

4

5

6

7

8

9

1 0

1 F i r m a g e

3 . 7 6

5 . 5 3

3 . 9 7

5 . 6 6

1 . 0 0

2 F i r m s i z e

3 . 6 4

1 . 8 7

3 . 5 6

2 . 0 0

0 .

2 2

1 . 0 0

3 P a t e n t s

0 . 0 2

0 . 1 3

0 . 0 2

0 . 1 5

0 .

1 0

0 . 0 6

1 . 0 0

4 P o t e n t i a l s l a c k

6 . 2 8

1 2 . 9 9

4 . 5 2

1 0 . 7 9

0 . 0 0

0 . 0 6

0 . 0 1

1 . 0 0

5 A b s o r b e d s l a c k

- 0 . 0 8

0 . 3 0

- 0 . 0 8

0 . 2 8

0 .

1 8

- 0

. 2 3

0 . 0 6

0 .

1 2

1 . 0 0

6 I n d u s t r y p r o t a b i l i t y

5 . 9 6

0 . 6 1

5 . 9 5

0 . 5 8

- 0

. 2 4

0 . 0 4

0 . 0 2

- 0

. 1 0

- 0

. 1 3

1 . 0 0

7 A n g e l d u m m y

1 . 0 0

0 . 0 0

0 . 0 0

0 . 0 0

- 0 . 0 1

0 . 0 2

0 . 0 2

0 . 0 6

0 . 0 0

0 . 0 1

1 . 0 0

8 H i g h o w n e r s h i p %

0 . 5 8

0 . 5 0

N . A .

N . A .

- 0

. 2 7

- 0 . 0 2

- 0 . 1 4

- 0 . 0 3

- 0 . 1 6

0 . 0 0

N . A .

1 . 0 0

9 F i n a n c i a l s l a c k

0 . 1 0

0 . 2 3

0 . 0 9

0 . 2 1

- 0 . 0 8

- 0

. 2 5

- 0 . 0 3

- 0

. 2 6

- 0

. 3 0

- 0 . 0 2

0 . 0 3

0 .

2 5

1 . 0 0

1 0 H u m a n

r e s o u r c e s l a c k

- 0 . 0 8

0 . 2 9

- 0 . 1 0

0 . 3 0

0 . 0 4

0 . 0 1

- 0 . 0 3

0 . 0 6

0 . 0 3

0 . 0 1

0 . 0 3

0 . 0 7

0 . 0 2

1 . 0 0

1 1 P r o t a b i l i t y

0 . 3 9

0 . 3 9

0 . 3 9

0 . 3 5

0 . 0 6

0 . 0 9

- 0 . 0 3

0 . 0 5

0

. 1 6

- 0 . 0 7

0 . 0 0

- 0 . 2 2

- 0 . 0 6

0 .

2 7

N o t e s : C o r r e l a t i o n s s i g n i c a n t a t p < 0 . 0 5 a r e i n b o l d . V a r i a b l e s 7 a n d 8 a r e b i n a r y , t h u s t h e i r c o r r e l a t i o n s s h o u l d b e i n t e r p r e t e d w

i t h c a r e .





T a b l e I I .

T h e p e r f o r m a n c e c o n s e q u e n c e s o f s l a c k r e s o u r c e s i n V C - b a c k e d

r m s , a n g e l - b a c k e d r m s , a n d t h e i r r e s p e c t i v e p e e r s a

M o d e l 1

M o d e l 2

M o d e l 3

M o d e l 4

D i f f . M o d e l

3 v s . 4


a n d p e e r g r o u p







F i r m a g e

0 . 0 0 1

( 0 . 0 0 2 )

0 . 0 0 1

( 0 . 0 0 3 )

0 . 0 0 1

( 0 . 0 0 2 )

0 . 0 0 1

( 0 . 0 0 3 )

F i r m s i z e

0 . 0 1 5 *

( 0 . 0 0 6 )

0 . 0 2 3 *

( 0 . 0 1 0 )

0 . 0 1 5 *

( 0 . 0 0 7 )

0 . 0 2 1 *

( 0 . 0 1 0 )

P a t e n t s

0 . 0 2 2

( 0 . 0 5 2 )

- 0 . 0 9 9 *

( 0 . 0 4 0 )

0 . 0 3 6

( 0 . 0 5 4 )

- 0 . 1 0 0 * * ( 0 . 0 3 8 )

*

P o t e n t i a l s l a c k

0 . 0 0 7 †

( 0 . 0 0 4 )

- 0 . 0 0 3 †

( 0 . 0 0 2 )

0 . 0 0 7 †

( 0 . 0 0 4 )

- 0 . 0 0 4 *

( 0 . 0 0 2 )

*

P o t e n t i a l s l a c k s q u a r e d

0 . 0 0 0 †

( 0 . 0 0 0 )

0 . 0 0 0 * * ( 0 . 0 0 0 )

0 . 0 0 0 †

( 0 . 0 0 0 )

0 . 0 0 0 * * ( 0 . 0 0 0 )

* *

R e c o v e r a b l e s l a c k

0 . 1 9 8 * * * ( 0 . 0 5 5 )

0 . 2 7 5 * * ( 0 . 0 8 1 )

0 . 1 8 0 * * ( 0 . 0 5 5 )

0 . 2 4 5 * * ( 0 . 0 8 2 )

R e c o v e r a b l e s l a c k s q u a r e d

- 0 . 3 7 4 * * ( 0 . 1 3 2 )

- 0 . 2 2 9

( 0 . 1 6 3 )

- 0 . 4 0 0 * * ( 0 . 1 3 0 )

- 0 . 2 5 6

( 0 . 1 6 2 )

I n d u s t r y p r o t a b i l i t y

- 0 . 0 6 7 *

( 0 . 0 2 6 )

- 0 . 0 1 6

( 0 . 0 2 6 )

- 0 . 0 6 5 *

( 0 . 0 2 6 )

- 0 . 0 2 4

( 0 . 0 2 6 )

I n v e r s e M

i l l s R a t i o

0 . 1 3 4 * * * ( 0 . 0 3 8 )

0 . 0 4 6

( 0 . 0 4 0 )

0 . 1 4 3 * * * ( 0 . 0 3 8 )

0 . 0 3 1

( 0 . 0 4 1 )

*

F i n a n c i a l s l a c k

0 . 0 2 7

( 0 . 0 3 1 )

0 . 0 7 3 * * ( 0 . 0 2 7 )

0 . 0 0 5

( 0 . 0 3 6 )

0 . 0 6 1 *

( 0 . 0 2 7 )

F i n a n c i a l s l a c k s q u a r e d

- 0 . 0 2 4

( 0 . 0 2 8 )

- 0 . 0 6 3 *

( 0 . 0 2 8 )

- 0 . 0 0 8

( 0 . 0 3 3 )

- 0 . 0 5 1 †

( 0 . 0 3 0 )

H u m a n r e s o u r c e s l a c k

0 . 1 6 4 * * * ( 0 . 0 1 6 )

0 . 1 0 4 * * * ( 0 . 0 1 7 )

0 . 1 6 4 * * * ( 0 . 0 1 7 )

0 . 0 8 7 * * * ( 0 . 0 1 9 )

* *

H u m a n r e s o u r c e s l a c k s q u a r e d

- 0 . 0 0 3

( 0 . 0 1 8 )

- 0 . 0 6 0 * * ( 0 . 0 1 9 )

- 0 . 0 1 5

( 0 . 0 1 9 )

- 0 . 0 5 6 *

( 0 . 0 2 1 )

V e n t u r e c a p i t a l ( V C ) d u m m y

0 . 0 2 7

( 0 . 0 3 1 )

- 0 . 0 4 0

( 0 . 0 4 3 )

A n g e l d u m m y

- 0 . 0 4 1

( 0 . 0 4 7 )

0 . 0 3 5

( 0 . 0 6 5 )

F i n a n c i a l s l a c k ¥ V C ( a n g e l ) d u m m y

0 . 0 5 7 †

( 0 . 0 3 6 )

0 . 0 3 8

( 0 . 0 5 0 )

F i n a n c i a l s l a c k s q u a r e d ¥ V C ( a n g e l ) d u m m y

- 0 . 0 4 6

( 0 . 0 3 6 )

- 0 . 0 5 3

( 0 . 0 4 8 )

H u m a n r e s o u r c e s l a c k ¥ V C ( a n g e l ) d u m m

y

0 . 0 0 5

( 0 . 0 1 7 )

0 . 0 4 2 *

( 0 . 0 2 1 )

H u m a n r e s o u r c e s l a c k s q u a r e d ¥ V C ( a n g e l )

d u m m y

0 . 0 3 8 *

( 0 . 0 1 9 )

- 0 . 0 1 9

( 0 . 0 2 7 )

†

C o n s t a n t

0 . 6 5 7 * * * ( 0 . 1 8 3 )

0 . 5 3 2 * * ( 0 . 1 7 8 )

0 . 6 3 5 * * * ( 0 . 1 8 2 )

0 . 5 8 5 * * * ( 0 . 1 7 7 )

F - s t a t i s t i c

9 . 3 2 0

4 . 8 4

8 . 4 7 0

4 . 5 2 0

P r o b .

0 . 0 0 0

0 . 0 0 0

0 . 0 0 0

0 . 0 0 0

R - s q u a r e d

0 . 2 8 4

0 . 2 0 6

0 . 3 2 6

0 . 2 7 2

N

6 3 1

4 4 6

6 3 1

4 4 6

N o t e s : a I n d u s t r y a n d y e a r c o n t r o l s a r e

i n c l u d e d b u t n o t r e p o r t e d . U n s t a n d a r d i z e d c o e f c i e n t s a r e r e p o r t e d ; s t a n d a r d e r r o r s a d j u s t e d f o r h e t e r o s c e d a s t i c i t y a r e i n p a r e n t h e s e s . † 0 . 1 0 ;

* p < 0 . 0 5 ; * * p < 0 . 0 1 a n d * * * p < 0 . 0 0 1 ( i n t h e r e g r e s s i o n m o d e l s , o n e - t a i l e d t e s t s a r e u s e d

f o r h y p o t h e s i z e d e f f e c t s , t w o - t a i l e d t e s t s f o r c o n t r o l v a r i a b l e s ) .





0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6

P e r f o r m a n c e

( g r o s s p r o t o n t o t a

l a s s e t s )

Mean - 1 S.D. Mean nancial slack Mean + 1 S.D.

VC-backed rms Non-VC-backed rms

Figure 1. Moderating effect of VC investors on the relationship between nancial slack and rmperformance

Mean - 1 S.D. Mean human resource slack Mean + 1 S.D.

VC-backed rms Non-VC-backed rms

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6

P e r f o r m a n c e ( g r o s s p r o t o n t o t a

l a s s e t s )

Figure 2. Moderating effect of VC investors on the relationship between human resource slack and rmperformance





Hypothesis 2 stated that the impact of (a) nancial slack and (b) human resourceslack on performance would be more positive in angel-backed rms than in theirnon-angel-backed peers. Model 4 shows that the presence of angel investors does notinuence the relationship between nancial slack and rm performance, but it doesalter the relationship between human resource slack and performance. Specically, theangel dummy and human resource slack interaction is positive and signicant( b = 0.042; p < 0.05). As illustrated in Figure 3, an increase in human resource slack ismore positive for performance in angel-backed rms compared to non-angel-backedrms. Thus, while we fail to nd support for Hypothesis 2a, we do nd support forHypothesis 2b.

Hypothesis 3 stated that the impact of (a) nancial slack and (b) human resource slack on performance would be more positive when rms are backed by VC investors ascompared to angel investors. While qualitative comparisons of the coefcients betweenModel 3 and Model 4 are instructive, they do not provide a formal statistical test for ourhypothesis. We use seemingly unrelated estimation (Suest) to formally test for differencesin the size of the coefcients across regression models (Wade et al., 2006). We nd nostatistical difference for the moderating role of VC investors versus angel investors on therelationship between nancial slack and rm performance. Our results show that theinteraction effect between the VC dummy and human resource slack squared in Model

3 is signicantly greater as compared to the interaction between the angel dummy andhuman resource slack squared in Model 4 (p < 0.10). Thus, we nd no support forHypothesis 3a and weak support for Hypothesis 3b.

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6


( g r o s s p r o t o n t o t a l a s s e t s )


Angel-backed rms Non-angel-backed rms

Figure 3. Moderating effect of angel investors on the relationship between human resource slack and rmperformance





Table III reports the OLS regressions on the sample of only VC-backed rms andangel-backed rms respectively. VIFs indicated that multicollinearity was a concernfor our slack measures and their interaction with the high ownership dummy variable.We therefore employed the ‘orthog’ command in Stata to generate orthogonalizedmeasures. This technique ‘partials out’ the common variance, creating transformed variables that are uncorrelated with one another (Bradley et al., 2011a; Pollock and Rindova, 2003). Although this procedure makes direct interpretation of coef-cients more difcult, it still allows for evaluation of the strength and direction of relationships (Pollock and Rindova, 2003). After this procedure, all VIFs were wellbelow the critical threshold, indicating that multicollinearity is no longer a concernin the reported models. Models 5 and 6 are baseline models, which only include maineffects. In Models 7 and 8, we add the interaction effects to study the moderating impact of a high ownership percentage on the relationship between slack andperformance.

Hypothesis 4 stated that in VC-backed rms the impact of (a) nancial slack and(b) human resource slack on performance would be more positive when investorshave a high ownership percentage. Based on the sample of VC-backed rms only,Model 7 shows that the product term between nancial slack and the high ownershipdummy variable is positive and signicant ( b = 0.068; p < 0.01). Figure 4 shows thatthe relationship between nancial slack and performance is more positive inVC-backed rms when the lead investor has a high ownership stake. Further, wend that the moderating effect of high VC ownership stake on the human resourceslack–performance relationship is signicant and positive in the main effects interac-

tion from Model 7 ( b = 0.089; p <

0.01), while marginally signicant and positive forthe squared human resource slack-high ownership stake interaction term ( b = 0.050;p < 0.10). As Figure 5 illustrates, the relationship between human resource slack andrm performance is signicantly steeper in rms backed by a VC investor with a highownership stake. Overall, we nd strong supporting evidence for Hypothesis 4a and4b.

Hypothesis 5 stated that the impact of (a) nancial slack and (b) human resourceslack on performance would be more positive when VC investors have a high own-ership stake as compared to when angel investors have a high ownership stake. Weindeed fail to nd an impact of high ownership stakes on the relationship betweenslack and performance in the sample of angel-backed rms (Model 8). Nevertheless,as argued above, qualitative comparisons of the coefcients between Model 7 andModel 8 do not provide a formal test of our hypothesis. We use seemingly unrelatedestimation (Suest) to formally test for differences in the size of the coefcients acrossregression models. Our results show that the interaction between nancial slack and ahigh VC ownership percentage in Model 7 is signicantly greater as compared tothe interaction between nancial slack and a high angel ownership percentage inModel 8 (p < 0.05). In addition, the interaction between human resource slack and ahigh VC ownership percentage in Model 7 is signicantly greater as compared to the

interaction between human resource slack and a high angel ownership percentagein Model 8 (p < 0.01). This provides strong supporting evidence for Hypothesis 5aand 5b.





T a b l e I I I .

S l a c k r e s o u r c e s , p e r f o r m a n c e , a n d t h e o w n e r s h i p s t a k e o f V C i n v e s t o r s a n d a n g e l i n v e s t o r s a

M o d e l 5

M o d e l 6

M o d e l 7

M o d e l 8

D i f f . M o d e l

7 v s . 8

O n l y V C - b a c k e d r m s

O n l y a n g e l - b a c k e d r m s

O n l y V C - b a c k e d r m s

O n l y a n g e l - b a c k e d r m s

F i r m a g e

0 . 0 0 1

( 0 . 0 0 4 )

- 0 . 0 0 3 ( 0 . 0 0 8 )

- 0 . 0 0 1

( 0 . 0 0 3 )

- 0 . 0 0 1 ( 0 . 0 0 8 )

F i r m s i z e

0 . 0 5 0 * * * ( 0 . 0 1 2 )

0 . 0 1 2 ( 0 . 0 3 7 )

0 . 0 4 7 * * * ( 0 . 0 1 1 )

- 0 . 0 0 8 ( 0 . 0 3 5 )

P a t e n t s

0 . 1 2 2

( 0 . 0 8 4 )

- 0 . 7 5 5 * ( 0 . 3 1 5 )

0 . 1 7 8 *

( 0 . 0 7 9 )

- 0 . 6 8 6 * ( 0 . 3 1 0 )

* *

P o t e n t i a l s l a c k

- 0 . 0 0 1

( 0 . 0 0 6 )

- 0 . 0 0 9 ( 0 . 0 0 6 )

0 . 0 0 2

( 0 . 0 0 6 )

- 0 . 0 0 7 ( 0 . 0 0 7 )

P o t e n t i a l s l a c k s q u a r e d

0 . 0 0 0

( 0 . 0 0 0 )

0 . 0 0 0 ( 0 . 0 0 0 )

0 . 0 0 0

( 0 . 0 0 0 )

0 . 0 0 0 ( 0 . 0 0 0 )

R e c o v e r a b l e s l a c k

0 . 5 2 5 * * ( 0 . 1 6 9 )

0 . 3 1 3 ( 0 . 2 6 4 )

0 . 5 8 6 * * ( 0 . 1 6 8 )

0 . 2 1 6 ( 0 . 2 3 0 )

R e c o v e r a b l e s l a c k s q u a r e d

0 . 4 7 1

( 0 . 3 1 5 )

- 0 . 2 2 8 ( 0 . 3 9 7 )

0 . 3 6 6

( 0 . 2 6 5 )

- 0 . 1 3 7 ( 0 . 4 1 4 )

I n d u s t r y p r o t a b i l i t y

- 0 . 0 3 3

( 0 . 0 6 4 )

- 0 . 0 6 2 ( 0 . 0 8 4 )

- 0 . 0 8 0

( 0 . 0 6 3 )

- 0 . 0 4 7 ( 0 . 0 7 8 )

I n v e r s e M

i l l s R a t i o

0 . 2 9 5 * * ( 0 . 0 9 2 )

0 . 4 6 9 * ( 0 . 2 0 5 )

0 . 3 0 1 * * ( 0 . 0 8 8 )

0 . 4 1 0 * ( 0 . 2 0 0 )

F i n a n c i a l s l a c k

0 . 0 5 4 † ( 0 . 0 3 1 )

0 . 0 2 2 ( 0 . 0 7 6 )

0 . 0 6 4 *

( 0 . 0 2 8 )

0 . 0 0 8 ( 0 . 0 7 6 )

F i n a n c i a l s l a c k s q u a r e d

- 0 . 0 4 8 † ( 0 . 0 2 7 )

- 0 . 0 7 1 ( 0 . 0 6 7 )

- 0 . 0 2 0

( 0 . 0 2 6 )

- 0 . 0 6 5 ( 0 . 0 6 0 )

H u m a n r e s o u r c e s l a c k

0 . 1 8 0 * * ( 0 . 0 5 3 )

0 . 1 2 2 ( 0 . 0 7 6 )

0 . 2 0 1 * * * ( 0 . 0 4 0 )

0 . 1 5 6 † ( 0 . 0 8 1 )

H u m a n r e s o u r c e s l a c k s q u a r e d

0 . 0 7 4 † ( 0 . 0 4 2 )

- 0 . 0 1 3 ( 0 . 0 5 3 )

0 . 0 7 3 *

( 0 . 0 3 6 )

- 0 . 0 0 1 ( 0 . 0 5 3 )

H i g h o w n e r s h i p %

0 . 0 4 4

( 0 . 0 3 3 )

- 0 . 0 5 1 ( 0 . 0 5 4 )

0 . 0 4 3

( 0 . 0 2 8 )

- 0 . 0 5 3 ( 0 . 0 5 1 )

†

F i n a n c i a l s l a c k ¥ H i g h o w n e r s h i p %

0 . 0 6 8 * * ( 0 . 0 2 8 )

- 0 . 0 7 8 ( 0 . 0 5 8 )

*

F i n a n c i a l s l a c k s q u a r e d ¥ H i g h o w n e r s h i p %

0 . 0 3 4

( 0 . 0 3 0 )

0 . 0 7 4 ( 0 . 0 6 1 )

H u m a n r e s o u r c e s l a c k ¥ H i g h o w n e r s h i p %

0 . 0 8 9 * * ( 0 . 0 3 0 )

- 0 . 0 6 5 ( 0 . 0 4 3 )

* *

H u m a n r e s o u r c e s l a c k s q u a r e d ¥ H i g h

o w n e r s h i p %

0 . 0 5 0 †

( 0 . 0 3 1 )

- 0 . 0 3 2 ( 0 . 0 5 0 )

C o n s t a n t

0 . 1 7 8

( 0 . 4 9 9 )

0 . 1 7 3 ( 0 . 6 5 3 )

0 . 5 2 8

( 0 . 4 6 6 )

0 . 2 0 5 ( 0 . 6 5 2 )

F - s t a t i s t i c

3 . 3 5 0

1 . 5 7 0

3 . 9 0 0

1 . 4 4 0

P r o b .

0 . 0 0 0

0 . 1 0 3

0 . 0 0 0

0 . 1 5 1

R - s q u a r e d

0 . 5 5 4

0 . 4 8 4

0 . 6 3 7

0 . 5 2 8

N

1 0 1

6 5

1 0 1

6 5

N o t e s : a I n d u s t r y a n d y e a r c o n t r o l s a r e

i n c l u d e d b u t n o t r e p o r t e d . U n s t a n d a r d i z e d c o e f c i e n t s a r e r e p o r t e d ; s t a n d a r d e r r o r s a d j u s t e d f o r h e t e r o s c e d a s t i c i t y a r e i n p a r e n t h e s e s . † 0 . 1 0 ;

* p < 0 . 0 5 ; * * p < 0 . 0 1 a n d * * * p < 0 . 0 0 1 ( i n t h e r e g r e s s i o n m o d e l s , o n e - t a i l e d t e s t s a r e u s e d

f o r h y p o t h e s i z e d e f f e c t s , t w o - t a i l e d t e s t s f o r c o n t r o l v a r i a b l e s ) .





0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6


( g r o s s p r o t o n t o t a l a s s e t s )

High ownership% (25% or more) Low ownership% (less than 25%)

Mean - 1 S.D. Mean nancial slack Mean + 1 S.D.

Figure 4. Moderating effect of VC ownership percentage on the relationship between nancial slack andrm performance for VC-backed rms

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,55

0,6

P e r f o r m a n c e ( g r o s s p r o t o n t o t a

l a s s e t s )

High ownership% (25% or more) Low ownership% (less than 25%)


Figure 5. Moderating effect of VC ownership on the relationship between human resource slack and rmperformance for VC-backed rms





DISCUSSION AND CONCLUSION

In this study, we considered how powerful external stakeholders inuence the relation-ship between slack resources and rm performance. We nd that the presence of VC andangel investors positively moderates the slack–performance relationship. Additionally,

this moderating effect is conditioned by the type of investor under consideration (i.e. VC versus angel) and investor ownership stake (i.e. high versus low). Our study contributes tothe management and entrepreneurship literatures in multiple ways.

First, we add to the slack literature by further unpacking the black box between slack resources and rm performance. Specically, our ndings underscore the importance of considering outside investors in debates regarding the value of resource slack in privaterms given that these investors are likely to inuence the discretion by which entrepre-neurs can use their slack resources, which subsequently affects the performance conse-quences of these resources. To illustrate the value of investors for the slack–performancerelationship, consider the following numbers. VC-backed rms showed a 1.40 timesincrease in performance from minus one to plus one standard deviation of nancial slack compared to a 1.03 times increase for their peers without such investors. An increasefrom minus one to plus one standard deviation of human resource slack benets theperformance of VC-backed rms more than the performance of similar rms withoutsuch investors (2.22 versus 2.20 times increase), where the benet is especially importantfor VC-backed rms when human resource slack moves beyond the mean level (seeFigure 2). An increase from minus one to plus one standard deviation in human resourceslack had a greater impact on rm performance for angel-backed rms compared to theirpeers without such investors (2.50 versus 1.75 times increase) (see Figure 3).

We also contribute to the slack literature by focusing on both nancial and humanresource slack, whereas prior studies largely focused on the former (e.g., Bradley et al.,2011b; George, 2005). This is important as we note some differences in the performanceconsequences of these two types of slack. Unreported results (using the full sample of private rms) show that the linear effect of human resource slack on performance is morepositive than that of nancial slack (p < 0.001). We fail to nd a difference with regard toquadratic effects. This entails that an increase in human resource slack has a morepositive effect on rm performance compared to nancial slack. An important avenue forfuture research is to increase our understanding why different types of resource slack

have a different impact on rm performance. Additionally, it raises some concernsregarding measures that combine nancial and human resource slack (e.g., Nohria andGulati, 1996) as such scales may mask key differences between different types of slack.

Second, we extend the managerial discretion literature by exploring the idea thatpowerful parties may enhance or restrict discretion. Most discretion research has focusedon the internal discretion of managers but has largely ignored the ideas that (1) discretionmay be distributed across organizational boundaries, with powerful actors that are notmanagers of the rm, and (2) that these powerful outside actors may also serve toenhance rather than just restrict managerial discretion, both of which we support withour study.

Third, our study contributes to governance and slack research by examining the roleof investor type and ownership stake in the slack–performance relationship. As for the





former, we nd only weak evidence that VC investors are better than angel investors atpushing entrepreneurs to extract more value out of a given set of human resource slack and no evidence that they are better at extracting value out of nancial slack. These weak results may be explained by the huge heterogeneity among these investor groups.Specically, whereas we argued that VC investors on average have a broader knowledgebase to draw from, we must acknowledge that this assumes both VC and angel investorsto be rather homogeneous groups of investors with regard to their relevant experience.However, previous research has indicated that substantial variation exists among bothgroups with regard to their experience related to industries they invest in and to making investments as such (e.g., De Clercq and Dimov, 2008; Dimov and De Clercq, 2006;Wiltbank, 2005). Moreover, we know these differences in experience matter; forinstance, angel investors tend to achieve higher returns whenever they do invest in areasthey have expertise in (Wiltbank and Boeker, 2007). A highly experienced angel investormay hence serve an entrepreneur equally well as a highly experienced VC investor when

it comes to helping them to see opportunities they would otherwise not have seen.Getting a clear picture on how VC and angel investors may differentially affect theslack–performance relationship may thus require a more detailed measurement of ourhypothesized underlying mechanisms (e.g., their knowledge base), thereby allowing researchers to take into account the signicant heterogeneity that exists among bothgroups of investors.

Another fruitful avenue for future research would be to measure the type of assistanceprovided by these investors. This may allow us to answer the question of why VCinvestors are marginally better than angel investors at helping entrepreneurs creating

value out of their human resources, but not out of their nancial resources (see Table II). A possible explanation may be found in some of the early research on differencesbetween VC and angel investors. Ehrlich et al. (1994) for instance suggested that angelinvestors are less likely to change the make-up of their portfolio companies’ managementteams and are less likely to provide assistance regarding stafng issues (whereas entre-preneurs do rely on both types of investors to provide assistance in nancial issues). Thedifference may hence be not so much in the level of value-adding provided by VC andangel investors, but rather in the type of value-adding provided.

Despite the weak results related to differences between VC and angel investors withregard to their inuence on the slack–performance relationship as such, we do nd animportant difference when we take into account ownership stakes. Specically, resultssuggest that especially professional VC investors act as a source of resource valuecreation when they have high ownership stakes in their portfolio rms (Figures 4 and 5).Having a high ownership stake resulted in a 1.78 times increase in rm performancefrom minus one to plus one standard deviation in nancial slack for VC-backed rms,while having a low ownership stake resulted in a 1.42 times increase in rm performancefrom minus one to plus one standard deviation in nancial slack. For human resourceslack, an increase from minus one to plus one standard deviation had a greater impacton rm performance for rms backed by VC investors with a high ownership stake (3.04

times increase) compared to those with a low ownership stake (2.38 times increase). Wefail to nd an impact of angel ownership on the relationship between slack resources andrm performance. These results tie into a signicant stream of governance research





which has focused on the relationship between ownership and rm performance. Ourndings show that for VC investors high ownership stakes matter, but not so for angelinvestors. Together these ndings thus reveal more complex links between investor type,ownership concentration, and the performance consequences of slack resources inprivate rms. The typical assumption in extant governance research that outside block-holders of equity are by denition incentivized and able to monitor their investments ishence unwarranted.

Finally, our study contributes to the entrepreneurial nance literature. While researchon the value-adding or coaching role of VC investors is rather elaborate (e.g., Sapienza,1992; Sapienza et al., 1996), few studies have demonstrated the processes by which VCinvestors increase rm performance. We argue that VC investors, through their value-adding and monitoring roles, help entrepreneurial rms to allocate their slack resourcesin a way that increases rm performance. This is especially the case for VC investorswhen they have a high ownership stake in their portfolio rms. Second, we focus not only

on VC investors, but also on angel investors. While there is an extensive literature on VCnancing, the literature on angel nancing is more limited. This is unfortunate as theangel investment market is expected to be multiple times larger than the venture capitalmarket (Mason and Harrison, 1996). Although our study shows similarities between VCand angel investors, it also points towards differences between these private investors assources of resource value creation.

Our paper also has several practical implications. First, entrepreneurs may learn aboutthe optimal level of nancial and human resources in their rms. Further, one of themain incentives for entrepreneurs to search for VC or angel nancing is a need for

nancial resources to support their high-growth ambitions. Our ndings, however, showthat these investors do not necessarily provide their portfolio rms with more slack resources, but that these investors do help entrepreneurs to make the most out of theresources at hand. Second, results are interesting for VC and angel investors as well. Ourndings demonstrate that both investor types are able to (at least partially) reduce thenegative consequences of holding too much slack. VC and angel investors typically havethe difcult task of deciding how many resources they will contribute to their portfoliorms without stimulating entrepreneurs to become less creative or pursue dubiousinvestments. Our study demonstrates that especially in VC-backed rms, where theseinvestors have a large ownership stake, the performance consequences of having a bufferof nancial resources is largely positive. VC investors should hence be careful not toprovide their portfolio rms with too little resources (for instance by using excessivestaged nancing) in an effort to reduce potential agency problems.

Conclusion

Previous research on the performance consequences of slack resources in private rmsignored a critical reality: many entrepreneurs do not ‘go it alone’ when pursuing theirambitions but rather obtain support from outside investors, such as VC and angel

investors. In this study, we have explored how different ownership contexts inuence theperformance consequences of slack resources in private rms. We argued that VC andangel investors inuence entrepreneurs’ discretion over their slack resources, which is





expected to inuence the performance consequences of these resources. Specically, weargued and showed that both VC and angel investors increase the value (decrease thecost) of slack resources. VC investors with a high ownership stake are especially instru-mental as a source of resource value creation. Our study points to complex links betweendifferent types of outside investors, slack resources, and the performance of private rms.

ACKNOWLEDGMENTS

We thank three anonymous JMS reviewers, Donald Bergh (the editor), Dan Forbes, Mirjam Knockaert, IanMacMillan, and James Thompson among others, for their helpful feedback. This paper further benetedfrom seminars at The Sol C. Snider Entrepreneurial Research Center (The Wharton School), EindhovenUniversity of Technology, Université Catholique de Louvain, and Maastricht University. Tom Vanackeracknowledges the nancial support of the Research Foundation Flanders (Grant FWO11/PDO/076) andthe Hercules Fund.

NOTES

[1] We thank an anonymous reviewer for drawing our attention to this contribution.[2] Scholars have also used agency theory as a framework to explain the negative performance conse-

quences of slack resources (e.g., Nohria and Gulati, 1997). Several scholars, however, have argued thatagency issues are minimized in private rms because entrepreneurs are typically the most importantowners (George, 2005). This is even the case for private rms that raise outside equity nance.Moreover, private investors use several mechanisms to minimize potential agency issues (Gompers,1995). Although agency arguments with respect to the misuse of slack may not hold for the type of rmswe study, the core assumption of a possible negative relationship between slack and rm performanceremains valid (Mellahi and Wilkinson, 2010). Indeed, while there is no apparent concern regarding the

objectives of entrepreneurs, they may still have discretion to take actions that lie outside the ‘zone of acceptance’ of investors and invest in dubious projects. Entrepreneurs, for instance, may take actionswith the intent to create value, but which are driven by overcondence. This demonstrates an importantdistinction between ‘latitude of objectives’ and ‘latitude of actions’ (Hambrick and Finkelstein, 1987),where our study is mainly concerned with the latter.

[3] All Belgian rms are required to report gross prot, irrespective of their size. Small rms, however, onlyneed to provide gross prot and not the individual components (including sales and cost of goodsproduced). Also note that our results are robust for alternative time lags in measuring rm performance(two and four years).

[4] Obviously, one can never rule out selection issues entirely and other selection issues may exist. Forinstance, the ‘best’ entrepreneurial rms may match with the ‘best’ investors. To address this possibility,we added controls reecting VC investor quality (such as VC age and a dummy for the oldest, mostestablished Belgian VC investor) in our regression models including VC-backed rms. Results did notchange. Unfortunately, we do not have such data for angel investors.

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