the interaction between financial and human resource slack and
TRANSCRIPT
1
The Interaction between Financial and Human Resource Slack and its
Effect on Venture Performance: Evidence from European High-Tech
Ventures *
Ine Paeleman, Tom Vanacker and David Devigne
Ghent University; Ghent University; Vlerick Leuven Gent Management School
Abstract
We study how the interaction between financial and human resource slack affects venture
performance. For this purpose, we use a large-scale longitudinal database comprising privately-
held technology-based ventures located in six European countries. Results show that the effects
of both financial slack and human resource slack on venture performance are inverse U-shaped.
More significantly, the interaction between financial slack and human resource slack is negative.
Our findings indicate that having both high levels of financial and human resource slack or low
levels of financial and human resource slack are detrimental for venture performance. We
discuss these findings from both academic and practical points of view.
* We thank all members of the VICO consortium for their support in constructing the European database
used in this study. We further gratefully acknowledge the financial support of the EU VII Framework
Programme (VICO, Contract 217485). The second author also acknowledges the financial support of the
Special Research Fund (BOF10/PDO/046) and the Flemish Research Organisation for Entrepreneurship
and International Entrepreneurship (STOIO).
2
Introduction
The mobilization of resources is one of the key activities performed by entrepreneurs and plays a
critical role in theories of firm performance and growth. Nevertheless, whether it is the
abundance or scarcity of resources which is most beneficial to venture development has divided
scholars for multiple years. Resource slack, or “potentially utilizable resources that can be
diverted or redeployed for the achievement of organizational goals” (George 2005: 661), has
been argued to either solve many organizational problems in the behavioural theory of the firm or
act as a facilitator of inefficient behaviour in resource constraint theories. In order to reconcile
these opposing views, early slack theorists have argued for the existence of an inverse U-shaped
relationship between slack and performance (e.g., Bourgeois 1981; Sharfman et al. 1988). At
first the relationship between slack and performance is positive as it increases innovation and
functions as a safety net amongst other reasons, but after an optimal level of slack is reached, the
relation between slack and performance becomes negative, as entrepreneurs become increasingly
complacent and inefficient in their use of resources.
To date most scholars agree that the relationship between financial slack and performance
outcomes is inverse U-shaped, both within quoted and privately-held companies (e.g., Nohria and
Gulati 1996; Tan and Peng 2003; George 2005; Kim et al., 2008; Bradley et al., 2011).
Nevertheless, we lack studies on the role of other types of slack, such as human resource slack.
Although, some studies have researched the relationship between other types of resource slack,
besides financial slack, these studies have three drawbacks in common (e.g., Welbourne et al.
1999; Mishina et al. 2004; Love and Nohria 2005; Voss et al. 2008; Mellahi and Wilkinson
2010). First, they generally investigate the existence of linear relationships between slack and
performance. This may lead to incorrect inferences, however, when the true relationship is
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curvilinear as argued by early slack theorists (Bourgeois 1981; Sharfman et al. 1988). Second,
they typically use samples of quoted or established companies but ignore young privately-held
ventures. This is unfortunate as many ventures never reach the stage where they become public
companies, and may not even want to become public (Berger and Udell 1998). Third, the few
studies that investigated the impact of multiple types of resource slack, only investigated the main
effect of these different types of slack on venture performance. This is also unfortunate as
companies may need access to different types of resources at the same time in order to develop
and grow (e.g., Cooper et al. 1994; Ndofor and Levitas 2004). The present study addresses the
abovementioned gaps.
The goal of this study is to examine the impact of financial slack, human resource slack
and their interaction on the performance of young technology-based ventures. For this purpose,
we use a longitudinal database comprising 7,002 companies founded in one of six European
countries, including Belgium, Finland, France, Italy, Spain and the UK. We formulate two
opposite hypotheses. The traditional theories used in slack research, such as the behavioural
theory of the firm and resource constraint theory (sensu stricto), suggest that the interaction
between financial and human resource slack on venture performance will be positive.
Nevertheless, Baker and Nelson (2005) argue that resource constraints in multiple domains may
harm venture development. Resource constraint theory (sensu lato) indicates the existence of a
negative interaction between financial and human resource slack. Overall, this study will
contribute to slack theory, by demonstrating the role of resource slack in different types of
resources. We move beyond the often implicit but overly simplistic assumption in most prior
research that companies only need to mobilize one particular resource (e.g., financial resources)
to pursue new opportunities or that different resources function independently from each other.
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This paper is structured as follows. In the next section, we first provide a brief review of
prior research on slack and then develop hypotheses on the impact of the interaction between
financial slack and human resource slack on venture performance. Next, we describe the data
and methods including sample, measures and economic approach. Then, we present the results.
Finally, we conclude by discussing the results from both a theoretical and a practical perspective.
Literature Review and Hypotheses
The Relationship between Slack Resources and Venture Performance
Different theoretical frameworks provide fundamentally different insights into the role of slack
resources for venture performance. On the one hand, the behavioural theory of the firm indicates
that slack resources perform a number of critical functions, which are all expected to benefit
venture development. First, companies consist of political coalitions between different
stakeholders and slack may function as a conflict mediator in order to maintain and sustain these
coalitions (Cyert and March 1963). Second, slack resources may also induce risk taking and
experimentation and create an environment that stimulates innovation (Bourgeois 1981; Bromiley
1991). Further, slack may serve as an internal workflow buffer and protect ventures from
external shocks (Cyert and March 1963; Greve 2003; O’Brien 2003).
Resource constraint theories, on the other hand, argue that companies with fewer
resources will leverage their resources more efficiently (Starr and Macmillan 1990; Mosakowski
2002; Baker and Nelson 2005).1 Moreover, ventures with more resource slack may become
1 Agency theories also suggest that slack resources will negatively affect venture performance, as slack may enforce self-serving
behaviour by agents (Jensen and Meckling 1976; Williamson 1963, 1964). We do not discuss agency theory in more detail here,
since several scholars argue that it is less suitable as a theoretical framework in privately-held companies where principles
5
complacent and inert, thereby ignoring competitive pressures and investing fewer resources in
research and development (Kim et al. 2008; Debruyne et al. 2010). Reconciling these opposing
views, multiple scholars have argued that slack will first have a positive effect on venture
performance in line with the behavioural theory of the firm, but that this positive effect will
diminish as slack increases up until an optimal level of slack, after which the relationship will
become negative, as indicated in resource constraint theories (Bourgeois 1981; Sharfman et al.
1988; Nohria and Gulati 1996; George 2005).
Given the non-linear relationship between slack and performance, as described above,
multiple scholars have argued that the right question to ask is not whether slack is universally
good or bad for venture performance (e.g., Nohria and Gulati 1996; George 2005). Rather, there
is a need for a greater understanding of when slack resources are more or less positive (or
negative) for venture performance (e.g., George 2005; Kim et al. 2008; Bradley et al. 2011).
Previous studies, however, have tended to overlook the fact that ventures must mobilize different
types of resources with unique and different characteristics. Earlier studies have focused on
either financial slack (e.g. George 2005; Kim et al. 2008; Bradley et al. 2011) or human resource
slack (e.g., Welbourne et al. 1999; Love and Nohria 2005; Mellahi and Wilkinson 2010) in
isolation. We lack a deep understand whether and how much slack in different types of resources
relates to venture performance. Therefore, we will focus on slack in two types of resources that
are thought to be critical to the development of ventures: financial resources and human resources
(Ndofor and Levitas 2004). Contrary to the few studies that did focus on slack in different types
of resources (e.g., Mishina et al. 2004; Voss et al. 2008), we not only study the main effects of
different types of resource slack on venture performance, but also consider the interaction
(shareholders) and agents (entrepreneurs) are often the same individuals (George 2005). However, it is easy to augment our
argumentation with agency theory as it generally enforces the claims of resource constraint theory in its strict form.
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between different types of resource slack. To our knowledge, no study has addressed potential
interactions between financial and human resource slack, which is unfortunate as ventures
typically need access to a combination of different types of resources to develop and perform
(e.g., Cooper et al. 1994; Ndofor and Levitas 2004).
The Interaction between Financial Slack and Human Resource Slack
The behavioural theory of the firm indicates that slack resources in general will benefit venture
performance through increased innovation and experimentation amongst other reasons (Cyert and
March 1963; Greve 2003). Multiple scholars have argued that a positive relationship between
financial slack, which is one specific type of resource slack, and venture performance outcomes,
is in line with the behavioural theory of the firm (e.g., George 2005; Kim et al. 2008).
Nevertheless, even within the behavioural theory of the firm, the relationship between financial
slack and performance outcomes should not be positive. For instance, ventures with financial
slack, may still be constrained in the pursuit of new investment projects and exploration of new
solutions, if they lack other critical resources, such as human resource slack. As such, even
within a behavioural theory of the firm perspective, financial slack may be negatively associated
with performance, when ventures lack human resource slack, and are unable to productively use
their financial slack due to other resource constraints. This demonstrates the need to study
interactions between slack in different types of resources.
According to a behavioural theory of the firm the interaction between financial slack and
human resource slack on venture performance will be positive. This may be especially the case
in privately-held companies, since it is unlikely that companies with financial slack can just buy-
in human resources quickly to pursue new opportunities when they emerge. The main reason is
that many privately-held companies generally suffer from significant information asymmetries
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(Davila et al. 2003). The prospects of young privately-held ventures are highly uncertain and
difficult for potential employees to evaluate. Moreover, young and small private ventures often
lack the visibility and reputation vis-à-vis their more established counterparts, which is often the
first prerequisite to mobilize employees (Williamson 2000; Davila et al. 2003). The same counts
for privately-held companies with human resource slack which lack financial slack. For these
companies, it may be difficult to pursue new opportunities and finance investments when
financial slack is missing. The alternative is to mobilize external financial resources from
traditional financiers such as banks or private equity investors, but this is typically a time-
consuming exercise as well (Winborg 2009).
Being able to act quickly is important, however, since it is associated with long-term
advantages including dominant and enduring market positions that originate from a company’s
competitive head start over its peers (Kerin et al. 1992). Companies which have more financial
(human resource) slack, but facing a lack of human resource (financial) slack, will be less able to
pursue value creating opportunities rapidly as advanced by behavioural theorists. This may
negatively impact venture performance compared to ventures where there is both financial and
human resource slack. Indeed, ventures with financial and human resource slack will be able to
pursue more new opportunities, more quickly, as they do not face constraints in any of these
critical types of resources.
While predictions from the resource constraint theory are fundamentally different from
the behavioural theory of the firm, the resource constraint theory in the stricter sense also
indicates the existence of a positive interaction between financial slack and human resource slack
on venture performance. Resource constraints theorists, argue that companies with fewer
resources are more likely to leverage their resources more efficiently compared to companies
with more resources (Starr and MacMillan 1990; Mosakowski 2002; Baker and Nelson 2005).
8
Entrepreneurs, who have access to excess resources, may become complacent, risk averse and
inward looking, because they wish to protect their current positions (Stevenson 1983; Stevenson
and Jarillo 1990). Resource constraint theories (sensu stricto) hence argue resource constraints
will be beneficial for venture performance. Different from the behavioural theory of the firm,
which indicates that a combination of high levels of financial and human slack will be most
beneficial for venture performance, resource constraint theories argue that a combination of low
levels of financial and human resource slack will be most beneficial. This leads to the following
hypothesis:
Hypothesis 1A: The interaction between financial slack and human resource slack on
venture performance will be positive.
Nevertheless, Baker and Nelson (2005) indicate that when ventures lack resource slack
and have to make do with whatever is available in too many domains at the same time (“parallel
bricolage”) this is unlikely to support venture development. However, when entrepreneurs
engage in “selective bricolage” this is more likely to benefit venture development. Selective
bricoleurs also create something from nothing, but instead of doing so consistently and repeatedly
across multiple domains, as the parallel bricoleurs, they use it selectively (Baker and Nelson
2005). Being resource constrained in multiple domains may hence also be problematic in a
resource constrained theory. This implies that the interaction between financial and human
resource slack is expected to be negatively related with venture performance within the resource
constraint theory in its wider interpretation. In other words, while ventures may be resource
constrained in one domain, their performance may benefit from having resource slack in other
domains.
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Moreover, Mishina et al. (2004) indicated that more slack resources are not always better
for company growth, at least in the case of human resource slack. Human resources are
“stickier” compared to financial resources in that they are not as easily (re)deployed to alternative
uses (Mishina et al. 2004; Voss et al. 2008). This implies that human resource slack is context
dependent and more closely tied to existing organizational routines, which may constrain growth
into new areas that require different skills or human resource configurations than the existing
ones (Mishina et al. 2004). In other words, the stickiness of resources determines how easily
slack resources may be converted to expand a business (Mishina et al. 2004). Therefore, the
relationship between slack and venture performance will not only be determined by the amount
of slack, but also by the resource characteristics (Voss et al. 2008). As ventures with more
human resource slack, may be constrained in their ability to invest their financial slack in
exploring opportunities unrelated to the skills and domains of their existing employees, a large
buffer of human and financial slack may decrease venture performance. This is different for
companies with more financial slack, but less “sticky” human resource slack, which may allocate
their financial slack to unrelated domains and explore value creating opportunities outside those
that relate to existing skills and knowledge as well. This leads to the following hypothesis:
Hypothesis 1B: The interaction between financial slack and human resource slack on
venture performance will be negative.
Table 1 provides an overview of the different theoretical frameworks and their
implications for different combinations of financial and human resource slack.
*** Insert Table 1 about here ***
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Data and Methods
Sample and Data Sources
Data was collected through the VICO project, which is a multi-country project on the financing
of entrepreneurial companies in Europe. The database contains longitudinal data on 7,002
companies founded in one of six European countries, including Belgium, Finland, France, Italy,
Spain and the UK2. The sample comprises 624 venture capital backed companies, which
received initial venture capital between 1994 and 2004, and were less than ten years old at that
point in time. For each venture capital backed company, multiple non-venture capital backed
companies were added to the database that had similar characteristics (i.e., same country of
origin, same industry, same age and similar size measured in total assets). All companies in the
VICO database were independent at start-up (i.e., other organizations may have been minority
shareholders, but companies were not controlled by other business organizations). Furthermore,
all companies had to be active in high-tech industries, including aerospace, biotech, ICT
manufacturing, Internet, pharmaceutical, robotics, software, telecom, web publishing and other
R&D.
For each company detailed yearly financial statement data was collected and this for as
many years as possible. Financial statement data were collected through Amadeus and country
specific databases. Key items were recorded from the financial accounts, including sales, gross
profit, cash, equity, financial debt and inventories among others3. We further collected data on
the number of full-time employees and employment cost within the respective companies.
Experts from the different countries held several meetings and optimized the database to ensure
2 The VICO database also includes German companies. Nevertheless, German companies are not required to report the detailed
financial data we require in this paper. Hence, we exclude the German companies from the database.
3 All monetary values have been inflation adjusted (2008 = 100).
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that the data were comparable across countries. It is further important to note that the database
includes companies that eventually fail and hence results are less subject to survivorship bias,
which is an important shortcoming characterising most prior research in the entrepreneurial
finance domain (Cassar 2004).
After excluding the companies and company-years where we lacked some of the
necessary data for the purpose of this study, we obtained a final sample of 4,228 companies and
21,470 company-years. This implies we have on average some five years of data for each
company in the sample, with a minimum of one year-observation and maximum of 15 year-
observations. Table 2 provides an overview of the sample by country, industry and year.
*** Insert Table 2 about here ***
Dependent Variable
Performance is operationalized as gross profit, which is defined as sales less the value of the
bought-in materials and services. This performance measure provides both theoretical and
empirical consistency with prior research on the role of financial slack in privately-held
companies (George 2005). We calculate the natural logarithm of gross profit, which has the
advantage that it functions as a normalizing transformation and decreases the probability that
extreme observations will drive our findings (Hand 2005).
Independent Variables
The key independent variables measure financial slack, human resource slack, and their
interaction. We use one-year lagged measures for the independent (and control) variables to
minimize endogeneity concerns (e.g., Kim et al. 2008).
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Financial slack is measured as the absolute amount of cash resources available within a
venture (George 2005; Voss et al. 2008; Bradley et al. 2011). Cash resources are the most easily
deployed resources and provide entrepreneurs with the greatest degree of freedom in allocating it
to alternative use (George 2005; Kim et al. 2008). Slack is generally measured relative to a target
level rather than an absolute level of resources (Bromiley 1991). We therefore adjusted the
measure for industry norms by subtracting the median ratio of cash for all companies in the
industry in which the focal venture operates (George 2005). Our measure of financial slack
hence represents a close estimate of excess cash resources held by ventures compared to industry
norms. We take the natural logarithm of this measure as a normalizing transformation. Both
theoretical and empirical work suggest that the relationship between slack and performance may
be curvilinear (Bourgeois 1981; Sharfman et al. 1988; George 2005). We also calculate financial
slack squared to capture any curvilinear effect.
Human resource slack is measured as the number of full-time employees relative to sales,
where larger values indicate greater levels of human resource slack (Welbourne et al. 1999;
Mishina et al. 2004). The measure is adjusted for industry norms by subtracting the median ratio
of employment to sales for all companies in the industry in which the focal venture operates
(Mishina et al. 2004; Mellahi and Wilkinson 2010)4. We take the natural logarithm of this
measure as a normalizing transformation (e.g., Love and Nohria 2005). We also calculate human
resource slack squared to capture any curvilinear effect in the relationship between human
resource slack and performance. We finally calculate the interaction between financial slack and
human resource slack (financial slack x human resource slack) by multiplying both measures.
4 We also measured human resource slack as the ratio of employment cost to sales. While the number of employees only captures
the amount of human resources, employment cost is likely to capture both the amount and quality of human resources, as human
resources with more human capital are expected to be more costly. Results remain robust when we use this alternative measure.
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Control Variables
We control for firm, industry, year and country effects. For firm effects, we control for size, age,
growth potential, other forms of slack, and lagged performance. We control for firm size, since
larger ventures may have higher levels of slack resources. Firm size is measured as the natural
logarithm of total assets. Because slack is time-dependent in its accumulation, we also control
for firm age. Firm age is measured as the years since formal incorporation. The intangible
assets ratio, defined as intangible assets on total assets, is used a measure for the growth potential
of ventures (Villalonga 2004; Vanacker and Manigart 2010). Although we are interested in the
impact of financial and human resource slack on venture performance, we also control for the
effect of other forms of slack. Potential slack is measured as the debt to total asset ratio adjusted
for industry norms (George 2005; Kim et al. 2008). Absorbed slack is measured as inventory on
total assets adjusted for industry norms (Steensma and Corley 2001; Bradley et al. 2011). We use
the natural logarithm of these slack measures as a normalizing transformation. To account for
possible persistence in venture performance (Wiggins and Ruefli 2002), we included a lagged
performance measure. This has also been recommended as a means to control for unobserved
heterogeneity (Heckman and Borjas 1980).
We also control for industry effects on venture performance. We measure industry
profitability as the natural logarithm of median net profit for all ventures in the industry as the
focal venture. High industry profitability is not only likely to influence the performance level of
the focal venture, but may also indicate opportunities to accumulate slack (George 2005; Bradley
et al. 2011). The average size of competitors is calculated as the median number of employees
for all ventures in the industry as the focal venture. We further include industry dummy variables
to capture broader industry level effects. Next, we include year dummy variables to control for
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the effects of any general economic event or trend. Finally, country dummy variables are
included to control for differences between the countries in our sample.
Econometric Approach
Standard statistical techniques, such as Ordinary Least Squares (OLS) regressions, are not
appropriate to use when data consist of repeated measures that are correlated within subjects as it
invalidates the basic assumption of independence (Fitzmaurice et al. 2004). Researchers need to
account for the correlation between responses when estimating regression parameters otherwise
they can make misleading and even incorrect inferences (Ballinger 2004). We therefore use the
Generalized Estimating Equation (GEE) approach to estimate more efficient and unbiased
regression parameters relative to OLS regressions (Ballinger 2004). GEEs permit the
specification of a working correlation matrix that explicitly accounts for within-company
correlation of responses. The GEE framework is gaining increasing attention by management
scholars and recent applications in the management literature are available (e.g., Ahuja and Katila
2001; Schneper and Guillén 2004; Ballinger 2004)5.
Results
Table 3 presents the sample descriptive statistics and correlations.
5 There are multiple ways to account for the longitudinally clustered nature of our data. We have used the GEE approach. An
alternative is to specify firm fixed effects (FE). GEE and FE approaches provide different benefits and disadvantages (e.g.,
Schneper and Guillén 2004). On the one hand, FE models are more widely used and are generally regarded as providing a better
control for unobserved heterogeneity. On the other hand, the GEE approach uses the information to estimate regression
coefficients more efficiently and makes no distributional assumptions. We tested for the robustness of our findings to using
alternative econometric approaches by estimating firm FE regressions. Results remained qualitatively similar to the ones reported
below based on the GEE approach.
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*** Insert Table 3 about here ***
Table 4 presents the regression results with levels of significance reported for
conservative two-tailed tests. The maximum Variance Inflation Factor (VIF) score equals 5.65,
which lies below the critical value of 10, and indicates that our analyses are unlikely to have
serious problems with multicollinearity (Kutner et al. 2005). We develop increasingly complex
models. Model 1 only includes control variables. In Model 2 we add the slack measures and
their squared terms. Adding the slack measures and their squared terms significantly increases
the goodness of fit when compared with Model 1. In Model 3 we further add the interaction
between financial slack and human resources slack. Adding the interaction term significantly
increases the goodness of fit when compared to Model 2. In what follows we will limit our
discussion to the full model (Model 3).
*** Insert Table 4 about here ***
The insights obtained from the control variables are largely logical. Unsurprisingly larger
ventures have higher performance levels (β = 0.324; p < 0.001). Our results further show that
ventures with larger intangible asset ratios demonstrate lower performance (β = -0.421; p <
0.001). These ventures are more likely to invest in research and development which takes time to
materialize in higher performance. The relationship between potential slack and venture
performance is curvilinear. The coefficient for the potential slack term is positive and significant
(β = 0.433; p < 0.001) while the squared term is negative and significant (β = -0.152; p < 0.001).
Similar results are obtained for absorbed slack. The coefficient for the absorbed slack term is
positive and significant (β = 0.719; p < 0.01), while the squared term is negative and significant
(β = -3.212; p < 0.001). Lagged performance is positive and significant (β = 0.655; p < 0.001)
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which demonstrates there is at least some persistence in venture performance. Finally, the results
show that ventures operating in more profitable industries demonstrate higher performance
themselves (β = 0.012; p < 0.001).
Our results provide strong evidence that the relationship between financial slack and
venture performance is curvilinear. While the coefficient for the financial slack term is positive
and marginally significant (β = 0.008; p < 0.1), its squared term is negative and statistically
significant (β = -0.007; p < 0.001). In a similar vein, the results provide strong evidence that the
relationship between human resource slack and venture performance is curvilinear. The results
show a positive and significant coefficient for human resource slack (β = 0.094; p < 0.01) and a
negative and significant coefficient for its squared term (β = -0.060; p < 0.001). These results are
largely in line with previous studies arguing for the existence of an inverse U-shaped relationship
between financial slack and performance in privately-held ventures (George 2005) and indicate
that slack theories work equally well for human resource slack.
Let us now consider the interaction between financial slack and human resource slack.
The interaction term is negative and significant (β = -0.033; p < 0.001). This provides initial
supporting evidence for hypothesis 1B. To illustrate the complex interaction effect, we rely on a
surface plot using two standard deviations from the means of financial slack and human resource
slack, as illustrated in Figure 1, Panel A. For low levels of financial slack, the effect of human
resource slack on performance is positive, but levels off as human resource slack increases. For
mean levels of financial slack, the effect of human resource slack on performance is negligible.
For high levels of financial slack, however, the effect of human resource slack becomes
increasingly negative. Figure 1, Panel A provides additional supporting evidence for hypothesis
1B. Figure 1 Panel B shows a contour plot, which resembles a surface plot that is viewed from
above, and where different color shades indicate different levels of performance. The contour
17
plot demonstrates that, all else equal, both high levels of financial slack and high levels of human
resource slack or low levels of financial slack and low levels of human resource slack are
detrimental for firm performance.
*** Insert Figure 1 about here ***
Discussion and Conclusion
The goal of this study was to investigate whether and how the interaction between financial and
human resource slack influences ventures performance. For this purpose, we used a unique
longitudinal database comprising data on private technology-based ventures located in one of six
European countries. We developed two opposing hypotheses. Building on the behavioural
theory of the firm and resource constraint theory in its strict interpretation, we hypothesized that
the interaction between financial and human resource slack on venture performance would be
positive. Building on resource constraint theory in its wider interpretation, we hypothesized that
the interaction between financial and human resource slack on venture performance would be
negative.
The results show that the relationship between both financial slack and human resource
slack and venture performance are inverse U-shaped. This is in line with previous studies on
financial slack and supports the claim of early slack theorists. More significantly, the results
demonstrate that the interaction between financial and human resource slack is negative. Our
findings are hence more in line with resource constraint theory in its wider sense (e.g., Baker and
Nelson, 2005), which indicate that resource abundance is no guarantee for ventures success, but
that resource constraints in too many domains may also harm venture development. Specifically,
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we demonstrate that in our sample of relatively young high tech ventures, having both high levels
of financial and human resource slack or low levels of financial and human resource slack are
detrimental for venture performance.
Our results are remarkably different from previous studies that focused on the linear
relationship between human resource slack and firm performance. These studies typically
focused on publicly quoted companies and argued that human resource slack will negatively
affect venture performance (e.g., Mishina et al. 2004; Voss et al. 2008). Our findings are more in
line with Welbourne et al. (1999) who argued that human resource slack is positive related with
venture performance. We indicate that human resource slack is positively related to venture
performance, but after a certain optimal level may start to harm venture performance. We further
show that human resource slack may be especially harmful when ventures have a lot a financial
slack. This may explain why our results are different from the findings of previous studies
focusing on quoted companies as these companies are more likely to be located at the high-end of
the financial slack continuum compared to privately-held companies.
One important shortcoming of the current study is that we did not address whether the
negative interaction between financial and human resource slack on venture performance is
contingent upon certain company, industry or country characteristics. Further research may for
instance investigate whether the interaction between financial slack and human resource slack is
less negative when companies have significant growth potential. Or how the optimal
combination of different forms of resource slack depends on the development of the financial
markets and flexibility of the labour markets in different countries.
This study, despite its limitations, has important implications for entrepreneurs and policy
makers. To entrepreneurs it shows the danger of having too much cash resources available
compared to industry peers. Entrepreneurs should hence work on developing cash management
19
skills even when they have more cash resources available compared to their peers. For
entrepreneurs that raise financial resources from outside investors, the less efficient use of cash
resources may potentially endanger the relationship with these stakeholders. It also demonstrates
to entrepreneurs that their ventures can flourish and perform well, despite the difficulty to raise
sufficient cash resources. Finally, it shows that entrepreneurs may want to keep some buffer of
human resources within their venture to pursue new opportunities.
To government officials it shows the need to further broaden their policy measures
targeted towards innovative companies with high growth potential. While current policies have
largely focused on increasing the amount of financial resources available to these ventures, our
research indicates that policy measures which allow companies to mobilize human resources are
also important. For many young technology-based ventures with high potential it may be
difficult to mobilize qualified human resources given their high risk of failure and lack of
visibility vis-à-vis established companies. Nevertheless, our findings indicate that at least some
level of human resource slack may contribute to the development of young technology-based
ventures. Policy measures which ease the mobilization of employees for ventures with high
potential are hence well taken.
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Table 1:
Combinations of Financial and Human Resource Slack
Human resource slack
Low High
Fin
anci
al s
lack
High
Resource constrain theory (sensu lato)
Negative interaction; resource constraints
in too many domains may hamper venture
development.
Authors: Baker and Nelson (2005)
Behavioural theory of the firm
Positive interaction; combination of slack
resources needed to pursue more
opportunities more timely.
Authors: Cyert and March (1963),
Bourgois (1981)
Low
Resource constraint theory (sensu
stricto)
Positive interaction; resource constraints
stimulate more efficient and creative
behaviour.
Authors: Starr and MacMillan (1990),
Mosakowski (2002), Gibbert et al. (2006)
Resource constrain theory (sensu lato)
Negative interaction; resource constraints
in too many domains may hamper venture
development.
Authors: Baker and Nelson (2005)
26
Country distribution:
Belgium 0.12
France 0.12
Finland 0.07 Year distribution:
Italy 0.28 Pre 1997 0.02
Spain 0.20 1997 0.02
UK 0.21 1998 0.03
1999 0.04
Industry distribution: 2000 0.33
Aerospace 0.01 2001 0.07
Biotech 0.05 2002 0.07
ICT manufacturing 0.23 2003 0.08
Pharmaceutical 0.03 2004 0.08
Robotics 0.08 2005 0.09
Software 0.38 Post 2005 0.16
TLC 0.05
Web Publishing 0.04
Internet 0.08
Other 0.05
Table 2
Sample Description
Number of company-year observations equals 21 470.
27
Table 3 Descriptive Statistics and Correlations Mean S.D. 1 2 3 4 5 6 7 8 9 10
1 Performance 5.741 3.182 1.000
2 Financial slack 1.323 4.891 0.328 1.000
3 Human resource slack 0.132 0.792 -0.256 -0.130 1.000
4 Firm size 6.930 2.094 0.486 0.654 -0.208 1.000
5 Firm age 8.640 5.139 0.230 0.159 -0.176 0.232 1.000
6 Intangible assets ratio 0.098 0.160 -0.021 -0.054 0.095 0.037 -0.057 1.000
7 Potential slack 0.013 0.329 -0.059 -0.218 0.015 -0.107 -0.050 0.034 1.000
8 Absorbed slack 0.045 0.112 0.034 -0.120 -0.058 0.001 0.046 -0.060 0.089 1.000
9 Lagged performance 5.606 3.211 0.786 0.321 -0.309 0.462 0.275 -0.015 -0.098 0.040 1.000
10 Industry profitability -0.612 5.930 0.095 -0.008 -0.043 -0.011 0.108 -0.033 0.001 -0.008 0.096 1.000
11 Average size competitors 3.761 0.346 0.115 0.045 -0.010 0.140 0.073 0.020 0.003 -0.036 0.117 0.164
Number of company-year observations equals 21 470. Correlations significant at 0.05 level are in bold. Industry, year and country dummies are not reported.
28
Variable Coeff. S.E. Coeff. S.E. Coeff. S.E.
Financial slack 0.004 0.004 0.008† 0.004
Financial slack squared -0.006 *** 0.001 -0.007*** 0.001
Human resource slack 0.097 *** 0.028 0.094** 0.028
Human resource slack squared -0.064 *** 0.009 -0.060*** 0.009
Financial slack x Human resource slack -0.033 *** 0.003
Firm size 0.268 *** 0.008 0.309*** 0.010 0.324*** 0.010
Firm age -0.002 0.003 -0.003 0.003 -0.003 0.003
Intangible assets ratio -0.381 *** 0.082 -0.413*** 0.083 -0.421*** 0.084
Potential slack 0.537 *** 0.050 0.468*** 0.052 0.433*** 0.052
Potential slack squared -0.186 *** 0.029 -0.154*** 0.029 -0.152*** 0.029
Absorbed slack 0.882 *** 0.210 0.749*** 0.212 0.719** 0.213
Absorbed slack squared -3.562 *** 0.618 -3.242*** 0.622 -3.212*** 0.625
Lagged performance 0.680 *** 0.005 0.668*** 0.005 0.655*** 0.005
Industry profitability 0.012 *** 0.003 0.012*** 0.003 0.012*** 0.003
Average size competitors -0.001 0.059 0.010 0.059 0.005 0.059
Constant 0.317 0.224 0.265 0.225 0.254 0.225
Industry dummies
Annual dummies
Country dummies
N (Company-years)
Number of companies
Deviance
Change in log likelihood
76809 76410 76082
Included
Included
Included
21470
4228
21470
4228
21470
4228
399 *** 328 ***
Included
Included
Included
Included
Included
Included
Model 2 Model 3
GEE Regression Results
Table 4
Model 1
Where † p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001, conservative two tailed tests.
29
Figure 1
The Impact of Financial Slack and Human Resource Slack on Performance
Panel A: Surface Plot
Panel B: Contour Plot
Financial slack
Performance
Human resource slack
Low
High
High
Low
-8.459
-7.959
-7.459
-6.959
-6.459
-5.959
-5.459
-4.959
-4.459
-3.959
-3.459
-2.959
-2.459
-1.959
-1.459
-0.959
-0.459
0.041
0.541
1.041
1.541
2.041
2.541
3.041
3.541
4.041
4.541
5.041
5.541
6.041
6.541
7.041
7.541
8.041
8.541
9.041
9.541
10.041
10.541
11.041
11.541
Low Mean High
Human resource slack
HIGHEST PERFORMANCE
Low
M
ea
n
H
igh
Fin
an
cia
lsla
ck
LOWERPERFORMANCE
LOWERPERFORMANCE