Embed Size (px)
Citation preview
This is the author’s version of a work that was submitted/accepted for pub-lication in the following source:
McKelvie, Alexander & Davidsson, Per(2009)From resource base to dynamic capabilities : an investigation of new firms.British Journal of Management, 20(s1), S63-S80.
This file was downloaded from: https://eprints.qut.edu.au/26569/
c© Copyright 2009 Wiley-Blackwell Publishing Ltd.
Notice: Changes introduced as a result of publishing processes such ascopy-editing and formatting may not be reflected in this document. For adefinitive version of this work, please refer to the published source:
https://doi.org/10.1111/j.1467-8551.2008.00613.x
FROM RESOURCE BASE TO DYNAMIC CAPABILITIES: AN INVESTIGATION OF NEW FIRMS
ALEXANDER McKELVIE Department of Entrepreneurship & Emerging Enterprises
Whitman School of Management Syracuse University 721 University Ave. Syracuse, NY 13244
Ph: 315-443-7252 Fax: 315-443-2654
E-mail: [email protected]
PER DAVIDSSON Brisbane Graduate School of Business at Queensland University of Technology
and Jönköping International Business School, Sweden Address: QUT, Gardens Point Campus, Brisbane, 4001 Queensland, Australia
Ph: +617 3864 2051 Fax: +617 3864 1299
E-mail: [email protected]
Paper forthcoming in the British Journal of Management Special Issue on “The Practice of Dynamic Capabilities: Theory Development and Research”.
2
FROM RESOURCE BASE TO DYNAMIC CAPABILITIES: AN INVESTIGATION OF NEW FIRMS
ABSTRACT
Despite the numerous observations that dynamic capabilities lie at the source of competitive advantage, we still have limited knowledge as to how access to firm-based resources and changes to these affect the development of dynamic capabilities. In this paper, we examine founder human capital, access to employee human capital, access to technological expertise, access to other specific expertise, and access to two types of tangible resources in a sample of new firms in Sweden. We empirically measure four dynamic capabilities and find that the nature and effect of resources employed in the development of these capabilities vary greatly. For the most part, there are positive effects stemming from access to particular resources. However, for some resources, such as access to employee human capital and access to financial capital, unexpected negative effects also appear. This study therefore provides statistical evidence as to the varying role of resources in capability development. Importantly, we also find that changes in resource bases have more influential roles in the development of dynamic capabilities than the resource stock variables that were measured at an earlier stage of firm development. This provides empirical support for the notion of treating the firm as a dynamic flow of resources as opposed to a static stock. This finding also highlights the importance of longitudinal designs in studies of dynamic capability development. Further recommendations for future empirical studies of dynamic capabilities are presented.
Keywords: dynamic capabilities, new ventures, resource based view, capability development
3
INTRODUCTION
A growing body of literature has addressed the role of dynamic capabilities in
obtaining competitive advantage (Eisenhardt and Martin, 2000; Zahra, Sapienza and
Davidsson, 2006). The underlying assumption is that firms who are better able to
“integrate, build, and reconfigure internal and external competences” (Teece, Pisano,
and Shuen, 1997: 516) earn returns above their competitors, at least in turbulent
environments. Researchers have focused their investigations on how these
performance differences come about (Helfat and Peteraf, 2003), the types of different
capabilities used (Subramaniam and Youndt, 2005), and how these capabilities
develop over time (Ethiraj, Kale, Krishan, and Singh, 2005). The main results are that
capabilities develop based on path dependence (including previous knowledge and
resource bases of the firm), learning, and substantial time and investment into the
endeavor (Zollo and Winter, 2002; Ethiraj et al., 2005). Despite these advances, there
are surprisingly few investigations that focus specifically on the link between
resources and the mechanisms through which they are used in creating value for the
firms (Sirmon, Hitt & Ireland, 2007).
Thus far, the literature on dynamic capabilities and their development has
primarily been focused on large and established firms (e.g., Rosenbloom, 2000). In
this paper we apply the dynamic capabilities argument to new firms. In particular, the
main research question we pose in this paper is: To what extent do access and changes
to resource bases influence the development of dynamic capabilities in new firms?
By answering this question, we feel as though we contribute new knowledge
to the area of research aiming to understand the factors leading to the development of
dynamic capabilities in new firms. This is important as the value creating dynamic
capabilities and the factors leading to their respective development may be different
4
for new firms when compared to more established firms (Zahra et al., 2006; Chen and
Hambrick, 1995). Aside from this new empirical context for studying dynamic
capabilities, we provide two other main contributions. Firstly, we provide statistical
evidence on the relationship between specific firm-based resources and the subsequent
development of dynamic capabilities. While we do empirically measure four separate
dynamic capabilities, the concepts of dynamic capabilities and their underlying
resource components are inherently very challenging to research in a systematic
fashion. Admittedly, our sample and operationalizations have limitations that prevent
us from arriving at any solid, final answers in this research. We see our research as
one step towards overcoming the relative void of statistical estimations in dynamic
capabilities research (e.g., Rosenbloom, 2000; Verona and Ravasi, 2003) and hope
that it can inspire other researchers to undertake further refinements towards that goal.
Secondly, we provide novel empirical evidence assessing how temporal
changes to firm resource bases affect capability development. While dynamic
approaches to measuring resources and capabilities have previously been espoused
(e.g., Dierickx and Cool, 1989), the methodological demands for effectively capturing
the temporal dynamism at the firm-level have prevented many from employing this
approach in their research (e.g., Lichtenstein and Brush, 2001). We employ a
longitudinal design with self-report data, and thus also overcome limitations of
relying upon proxy measures for capturing firm-level resource dynamism.
Achieving these two contributions is made more manageable by the fact that
we study new, and generally small, firms. This type of firm often is less complex and
provides the opportunity to gather information from someone with (close to) full
knowledge of the firm and its operations (Sorensen and Stuart, 2000; Autio, Sapienza
5
and Almeida, 2000). The relative importance of resource changes over time may also
be greater for new firms.
This paper proceeds as follows: First, we apply dynamic resource-based theory
to explain the relationship between resources and dynamic capabilities. Second, we
discuss the role of resources and capabilities for the continued development of new
firms. We then develop five hypotheses regarding the effects of access and changes to
resource bases, on the development of dynamic capabilities. Next, we describe the
longitudinal data from a theoretically relevant sample that we use to test our
hypotheses, as well as how our constructs were operationalized. We then present the
results, which concretely show that different resources lead to different dynamic
capabilities and that dynamic temporal changes to resource bases have a greater
impact to dynamic capabilities than previous static stocks of resources. We conclude
by discussing the theoretical and methodological implications of these findings.
THEORY & HYPOTHESIS DEVELOPMENT
Dynamic capabilities
Resource-based theory views the firm as a bundle of resources and emphasizes
that competing firms possess heterogeneous resource bases (Grant, 1991). Resources
are defined as assets that are useful in the production process (Amit and Schoemaker,
1993). This approach suggests that the attributes of these resources (i.e. if they are
valuable, rare, inimitable, and non-substitutable) would confer upon the firm
competitive advantage, and by implication, affect its performance (Peteraf, 1993;
Peteraf and Barney, 2003).
Early proponents of this internal perspective noted that resources do not
generate rents per se, but rather must be employed in some way in order to be useful
(Grant, 1991). Penrose (1959), for instance, notes this difference: “The services
6
yielded by resources are a function of the way in which they are used – exactly the
same resources when used for different purposes or in different ways and in
combination with different types or amounts of other resources provides a different
service or set of services” (p. 24). Hence, the capabilities approach evolved, where
capabilities are seen as the ability to coordinate and deploy resources in order to
achieve the firm’s goals (Amit and Schoemaker, 1993). This implies that while
resources seldom lead to performance differences on their own, the application of
resources (i.e. capabilities) is what truly causes performance differences (Grant,
1991). This capabilities approach thus overcomes the critique of whether possession
or usage of resources is the primary concern (Wiklund and Shepherd, 2003). Further,
as they are not simply inputs into a productive process, capabilities cannot be
purchased from the market (Makadok, 2001).
Scholars have recently extended the resource-based thinking as they felt that
the present embodiment of resources was too static (Teece et al., 1997). Viewing the
firm as both a stock and a dynamic flow of resources is one outcome of this move.
Broadly defined, dynamic capabilities are seen as the firm’s ability to integrate and
change resource bases to address changing environments. Thus, dynamic capabilities
can be seen as those processes where resources are acquired, integrated, transformed,
or reconfigured to generate new value-creating firm-based activities (Eisenhardt and
Martin, 2000; Teece et al., 1997). Several authors have specifically noted that new
product development is one prototypical dynamic capability and/or argued that
innovation is the cornerstone of dynamic capabilities (Dosi, Nelson and Winter, 2000;
Eisenhardt and Martin, 2000; Helfat, 1997). This is also the perspective we employ in
7
this study1 and we focus on four different, yet potentially related, methods of how
new firms go about creating value via innovation. More specifically, we investigate
the following set of dynamic capabilities: idea generation capabilities; market
disruptiveness capabilities; new product development capabilities, and new process
development capabilities. These notions will be further described in the Method
section. Note, however, that in the hypothesis development below we do not
differentiate between different manifestations of dynamic capabilities. Hence, our
study is exploratory with respect to the potentially varying effects of the same type of
resource on different dynamic capabilities.
Development of dynamic capabilities in new firms
Two apparently contradictory stories on new firms and resources emerge from
the literature. On the one hand, most firms start with very limited resources (Census,
1992; Davidsson, 2006) and ‘barriers to entry’ are not as strong an inhibitor as theory
would have it (Geroski, 1995). Descriptions abound regarding how firms get going
with very limited resources (e.g. Baker and Nelson, 2005) and there are compelling
arguments that new firms sometimes succeed because they are not constrained by
existing resource endowments (Katila and Shane, 2006; Mosakowski, 2002). This
would suggest that their performance, whether or not contingent on dynamic
capabilities, has little to do with their resource endowments.
1 The notion that dynamic capabilities are the capabilities to change existing substantive
capabilities (Helfat & Peteraf, 2003, Zahra et al., 2006) has the attractive feature of separation of dynamic capabilities from a necessary association with a particular type of environment or outcome. However, defining dynamic capabilities as the “routines to change routines” poses grave operationalization difficulties for quantitative studies. Therefore, we stay closer to the original spirit of the dynamic capabilities as resource-base changes that facilitate innovation. To avoid tautology we do not, however, derive the existence of dynamic capabilities from favourable firm performance. We therefore leave it to other studies to determine under what conditions dynamic capabilities lead to performance advantages. Neither do we assume a necessary connection between dynamic capabilities and the dynamism of the environment.
8
On the other hand, much research shows that new ventures are characterized
by low survival (Geroski, 1995) and very limited growth (Davidsson, Achtenhagen
and Naldi, 2006), which many would attribute to their (resource-related) liabilities
(Aldrich and Auster, 1986; Stinchcombe, 1965). Similarly, studies like Brush, Greene
and Hart (2001) portray the build-up of an adequate resource base as the central
problem for entrepreneurs, and Borsch, Huse and Senneseth (1999) find that the most
resource-impoverished firms shun strategies involving product and market innovation.
Moreover, a number of empirical studies have found positive relationships between
firms’ initial resource endowments and their subsequent performance (e.g., Cooper et
al., 1994; Laitinen, 1992). This suggests that resource endowments are critically
important for new firms and that the development of dynamic capabilities is a likely
mechanism for their performance effect.
A reconciliation of this apparent contradiction is that while new firms may
well get successfully started with extremely limited resources, their continued
development is contingent on dynamic capabilities whose development requires a
somewhat richer resource base. For example, Geroski (1995) argued that while
barriers to entry may be close to non-existent, the barriers to survival for new firms
appear considerable. Similarly, and even more central to our argument, Baker and
Nelson (2005) have recently argued that extensive reliance on resource-frugal
bricolage tactics may lock the firm into a path that does not allow it to achieve growth
and dynamic development. Although excessive resources can sometimes be harmful,
the overarching theme of our hypotheses therefore is that more resources are better for
capability development. As of yet, little is known about the “black box” role of which
specific resources affect different dynamic capabilities (cf. Sirmon et al., 2007).
Indeed, this is an imperative issue considering the role of resources in determining
9
capabilities, which, in turn, form the basis for firm-level performance differences.
Understanding how and where specific resources affect the value-creating ability of a
new firm is a necessary condition for managers to make effective decisions
concerning their own resource investments, but also for aiding academics in deriving
more accurate theory. Below we develop broad and somewhat exploratory hypotheses
concerning these relationships. These also include the influence of recent resource
improvements on the development of differing types of dynamic capabilities.
Knowledge resources
Founder human capital. Discussions of new firms within the resource-based
view frequently consider the role of the human capital of the founder(s) (Alvarez and
Busenitz, 2001). Helfat and Lieberman (2002) as well as King and Tucci (2002) find
that appropriate managerial experience does play a role in the development of
dynamic capabilities. Hambrick and Mason (1984) as well as Bantel and Jackson
(1989) argue that the formal education of the founder/executive affects the knowledge
bases of the firm, and thus its organizational capabilities. Penrose (1959) also argues
that industry and firm experience lead to superior decisions concerning the knowledge
of the outcomes of resource allocation.
Empirical studies have not always been clearly supportive of the universally
positive effects of founder human capital. An important reason for this is that
founders with greater human capital may apply a higher threshold for what is deemed
satisfactory performance and thus exit or start multiple ventures in response, creating
a confounding effect (Davidsson, 2006; Gimeno, Folta, Cooper, and Woo, 1997). This
makes it even more important to relate founder human capital to the suspected
performance-enhancing mechanism, dynamic capabilities, rather than directly to
performance. Hence our first hypothesis:
10
H1: The founder’s level of human capital will positively influence the development of dynamic capabilities.
We follow the practice of approximating this type of human capital with the (arguably
relevant) education and experience of the founder (cf. Cooper et al., 1994). Hence, the
specific relationships to be tested empirically are as follows:
H1a: The founder’s level of education will positively influence the development of dynamic capabilities. H1b: The founder having business education will positively influence the development of dynamic capabilities. H1c: The founder having prior managerial experience will positively influence the development of dynamic capabilities. H1d: The founder having prior industry experience will positively influence the development of dynamic capabilities.
Employee human capital. A challenge in the development of a firm is to move
beyond the initial human capital of the founder (Brush et al., 2001). It is therefore
important to investigate the role of human capital in others actively engaged in the
firm, such as employees. Employee human capital of the firm refers to the knowledge,
skills, and abilities that employees possess and use in their work (Schultz, 1961).
Studies of employee human capital have found direct positive effects on firm
performance (e.g. Hitt, Bierman, Shimizu and Kocchar, 2001; Rauch, Frese and
Utsch, 2005). Further studies have examined the role of employee human capital as
enabling factors which allow the firm to acquire and apply new knowledge (Hitt et al.,
2001); to allow other resources and capabilities to be developed fully (Ranft and Lord,
2002), or to increase their gains from training (Branzei and Thornhill, 2006). These
studies signify that developing a talented and motivated pool of employees may be a
necessary stride towards competitive advantage.
11
This is further supported by Subramaniam and Youndt (2005) who find that
employee human capital enhances radical and incremental innovative capabilities.
Smith, Collins and Clark (2005) moreover find that employee human capital
stimulates knowledge creation capabilities while Tushman and Anderson (1986) find
that having more skilled employees will promote the firm to question prevailing
norms and to develop abilities to match technological change.
Importantly, while it can be justifiably assumed that in most cases the human
capital of founders will be invested in their firms, the distinction between resource
possession and resource use becomes more complicated in the case of employee
human capital. We would argue that it is important to assess not only the level of
human capital residing in employees but also their inclination to use it for the benefit
of the firm. Thus, we propose the following hypothesis:
H2: The firm’s access to knowledgeable and committed employees will positively influence the development of dynamic capabilities.
Access to specific expertise. Different individuals do not learn equally from
education and experience. In addition, knowledge resources that are important for the
firm’s development are sometimes provided by members of the firm’s network or
social capital, i.e., individuals who are neither founders nor employees (Aldrich and
Zimmer, 1986; Davidsson and Honig, 2003). It is therefore important to supplement
the above with direct assessment of the effects of specific expertise available to the
firm, regardless of who provides it and regardless of what specific mechanisms led to
the development of such expertise. We hypothesize that access to specific expertise
affects the development of dynamic capabilities:
H3: The firm’s access to specific expertise will positively influence the development of dynamic capabilities.
12
Based on empirical exploration (see below) we will distinguish between
technological expertise and other specific expertise. The former is associated with
R&D knowledge, patents, labs, and proprietary secrets (Dollinger, 2003). As such,
these generally are related to innovation and new product/service development. Lack
of technological knowledge resources constrains the search zones for new
opportunities of firms, thus reducing their ability to use knowledge from other sources
(Zahra and Filatotchev, 2005). Technological knowledge resources have also been
linked to flexibility and abilities to upgrade manufacturing processes or products
(Sanchez, 1995) as well as to development of radical or break-through technologies
(Abernathy and Utterback, 1978). Henderson and Cockburn (1994) find a relationship
between technological experience and innovative output while King and Tucci (2002)
find a positive effect on new market entry.
Not all expertise that is potentially important is technological. Hence we also
include other specific expertise, which captures proficiency in marketing and
management. Much of the human capital of an expert is tacit. Based on the benefits of
experience and tacit knowledge, experts provide a valuable possible source of
dynamic capabilities. For instance, Lord and Maher (1990) argue that experts have a
better understanding about how to apply their knowledge. Lane and Lubatkin (1998)
find that tacit knowledge has a higher probability of creating value for the firm via
absorptive capacity and Hitt et al. (2001) find that expert knowledge enhances a
firm’s ability to offer new products or services or expand into new customer markets.
This precipitates that possessing access to expert knowledge resources will allow the
firm to know how to develop dynamic capabilities. Hence, the following two aspects
of expertise will be tested under H3:
H3a: The firm’s access to technological expertise will positively influence the development of dynamic capabilities.
13
H3b: The firm’s access to other specific expertise will positively influence the development of dynamic capabilities.
Tangible resources
While knowledge resources are important, they need to be combined with
tangible resources, such as financial means and technical equipment, in order to have
full effect. As they constitute basic factors of production, tangible resources are often
the second type of resources that a new venture possesses following start-up,
immediately after founder-based resources. Tangible resources can be seen as the
physical resources such as the plants, equipment, computers and machinery that will
allow a new product or service to be produced and/or distributed (Dollinger, 2003). In
addition, having access to financial resources allows firms to strategically invest in
exploiting the physical and other resources it possesses as well as the flexibility of
purchasing other needed factors of production. Thus, having access to tangible
resources provides new firms with the ability to invest in dynamic capability
development.
H4: The firm’s access to tangible resources will positively influence the development of dynamic capabilities.
More specifically:
H4a: The firm’s access to financial capital will positively influence the development of dynamic capabilities. H4b: The firm’s access to modern plants and equipment will positively influence the development of dynamic capabilities.
Resource flows
Firms are typically assumed to be made up of dynamic systems and resources
that change over time (e.g. Dierickx and Cool, 1989). Indeed, one of the motives for
14
the concept of dynamic capabilities was the fundamental observation that firms are
not merely stagnant stocks of resources, but rather flows (Teece et al., 1997).
However, empirical studies of this subject generally tend to adopt a research design
that inherently treats firms as static resource stocks. The empirical findings of
Lichtenstein and Brush (2001) and Kor and Mahoney (2005) offer evidence that firms
behave based on their present resource bases. This may be particularly important for
new firms as, for this type of firm, between any two points in time the ratio of new to
total resources is likely to be higher than for more established firms. In fact, for
rapidly evolving new (and often initially small) firms, the resource base it had a few
years ago may be largely irrelevant to the firm’s current dynamic capabilities. Thus,
we feel that recent resource base improvements in the firm (as compared to an earlier
temporal stage) will have a positive effect on the development of dynamic
capabilities.
H5: The firm’s improvements to its resource bases will positively influence the development of dynamic capabilities.
Based on empirical exploration we will test this hypothesis with respect to
improvements in three specific types of resources, namely:
H5a: The firm’s improvements to its reputational resources will positively influence the development of dynamic capabilities. H5b: The firm’s improvements to its operational resources will positively influence the development of dynamic capabilities. H5c: The firm’s improvements to its technological resources will positively influence the development of dynamic capabilities.
METHOD
Sample
For the purpose of testing our hypotheses we wanted to obtain a theoretically
relevant sample, rather than one which exactly represents the empirical population of
15
‘new firms’ in a particular country at a particular time2. Thus, we wanted the sample
to have representation of different types of ‘new firms’ so as to capture the breadth of
that theoretical concept. We also wanted to ascertain enough variance in the variables
our hypotheses concern, while restricting the extent of unmeasured heterogeneity.
Given these theoretical considerations, the usual concerns about unwanted biases
apply. Thus, application of a random sampling mechanism as well as high response
rates from a restricted population with the sought-for properties remain desirable
aspects of the delineation of a theoretically relevant sample.
The best available sample we could obtain originates from a Swedish data set
which was designed for multiple purposes rather than specifically for the purpose of
the current research. The sampling frame was stratified by industrial sector (i.e.
manufacturing, professional services, and wholesale/retail). The sample was also
divided into two equally sized employment size strata, using the European Union’s
delimitations for small (10-49 employees) and medium-sized (50-249 employees)
firms. Thus, the vast group of micro-enterprises are excluded because they would not
have enough variance in several of our independent variables and may not adequately
reflect the theoretical entity ‘firm’ that dynamic capabilities theory makes statements
about. The sample was also stratified by type of governance into independent firms,
members of company groups with fewer than 250 employees, and members of
company groups with 250 employees or more. Again, this ensures coverage of the
theoretical concept ‘new firm’.
2 For example, the empirical population of new firms in a particular time-space configuration could, in principle, consist of 90% new fast food franchises and 10% all other types of new firms. This does not mean that the best test of a theory about ‘new firms’ should necessarily be performed on a sample consisting of 90% fast food franchises. As a more realistic example it has been pointed out that a random sample may contain 16 times more solo self-employed than firms with 10-49 employees without this meaning that the former category should be regarded as 16 times more theoretically relevant (Davidsson, 2004: 69).
16
A total panel of 2455 firms underwent two waves of phone plus mail
questionnaire interviewing conducted three years apart; 1997 and 2000. Complete
data from all four questionnaires were obtained for 803 firms. For our current
purposes we need to further delimit the sample to new firms. We include in our
research the 238 participating firms that were ten years of age or less at the time of the
second wave mail survey (cf. Yli-Renko, Autio, and Sapienza, 2001). Through the
second wave, 108 of these companies were still in existence and fully cooperating.
This, then, is the minimum size of the sample to be used in our analysis. Non-
response bias tests have been carried out without any significant results. It should be
noted, however, that because of the post-stratification by age and non-random attrition
the analyzed sample does not have equal representation of the original industry, size
and age strata. The sample used in this study was composed of 13% manufacturing
firms, 68% service firms, and the remaining 19% were retail or wholesale firms.
Relating to the size of the firms, 65% had between 10-50 employees, with the
remainder having more than 50 employees. Forty percent of the firms were
independent, while 23% were part of a small (less than 250 employee) business group
and 37% from large (250+ employees) business group.
The CEOs of the firms were the target of the data collection, as is common in
new firm research (McDougall et al., 1994). These individuals generally have the best
overall knowledge of the firm (Zahra, Neubaum, and El-Hagrassey, 2002), and in
many cases, the CEO is truly the driving factor and the only possible informant for the
firm. We acknowledge, however, that relying on a single respondent is a shortcoming
as regards the (relatively few) medium-sized firms in our sample.
Variables
17
Ideally, all of our operationalizations would have been carefully tested for
validity and reliability in prior research, and consistently applied in both waves of data
collection. However, although the authors had some involvement in the study’s design,
the ideas for the current paper were not fully developed prior to the first wave of data
collection, and we are left with a less ideal situation more reminiscent of working with
secondary data. Under this limitation, we have tried our best to arrive at meaningful and
reliable measures, albeit somewhat short of ideal ones.
The control variables, the firms’ resource base (including founder human
capital) and two dynamic capabilities were collected in the first wave of data collection.
In the second wave of the mail survey, three years later, we measured changes to
resource bases, and two more dynamic capabilities. We thereby overcome the
problem of reverse causality between resource access and capability development for
two dynamic capabilities; a common problem for cross-sectional data. By examining
these variables at different temporal periods, we are able to address how changes in
resource bases affect capability development.
Dependent variables: The empirical measurement of dynamic capabilities has
been carried out using a number of different operationalizations (Zahra et al., 2006).
One definitive measurement tool for dynamic capabilities has yet to emerge. Thus far,
the majority of studies using quantitative methods have used proxies with single items
as their dependent variables (Tsai, 2004). While this lack of formal measuring stick
does provide a challenge for the present study, we have attempted to appraise our four
different dynamic capabilities based on items from other studies. Studies such as
Dutta, Narasimham and Rajiv (2005) do provide valuable insights as to how to
measure capabilities. We have attempted to build upon these studies as much as
possible, although much work of that nature has appeared after our original survey
18
was designed. Nevertheless, we have attempted to identify different methods for
creating value in new firms, with a focus on innovation. The resulting four dynamic
capabilities that we measure are made up of multi-items. The specific items employed
for each variable and the Cronbach alpha reliabilities are included in the Appendix.
The first dynamic capability measured, idea generation capability, has its
roots in the entrepreneurship and innovation literatures. The ability of a firm to
develop new ideas for future entrepreneurial action is generally accepted as being a
precursor for firm-level innovative behavior and may be a source of competitive
advantage if firms are able to capitalize on their ideas (e.g. Hansen and Birkinshaw,
2007). The employed items are adopted from the operationalization of Stevenson’s
(e.g. Stevenson and Jarillo, 1990) perspective of firm-level entrepreneurial behavior
(Brown, Davidsson and Wiklund, 2001).
Market disruptiveness capability also delves from the literature on firm-level
entrepreneurial actions (Brown et al., 2001). Market disruptiveness specifically
examines the behavior of the firm in terms of the magnitude, aggressiveness, and
persistence of releasing innovations to the market. As such, it measures to what extent
the firm creates market dynamism. The five items that we used to measure this
capability are established in the literature on entrepreneurial behavior (e.g. Covin and
Slevin, 1991).
Unlike the above, the third and fourth dependent variables were measured
during the second wave of data collection. These variables follow the approach to
measuring capabilities that uses outcomes relative to competitors as proxies, which
Dutta and colleagues (2005) argue is a suitable method for measuring capabilities and
that Kor and Mahoney (2005) employed in their study. The items included in the
operationalizations of both capabilities reflect the multidimensional use of relative
19
measures used by Wiklund and Shepherd (2003). For new product development
capability we measured the firm’s performance concerning product/service
innovation, and the quality and quantity of new products/services relative to the firm’s
two major competitors. These were on a five-point scale ranging from “much worse
performance” to “much better performance”. The coefficient alpha of this measure is
0.60, which is only marginally acceptable (Nunnally, 1978).
The final dependent variable, new process development capability, was made
up of two items related to performance of process innovation and adoption of new
technology in the processes of the firm, both relative to competitors. The Cronbach’s
alpha is also slightly short of ideal (Nunnally, 1978).
Independent variables: We have presented six specific manifestations of two
general types of resources within our theoretical frame of reference. For the founder
human capital variables, representing hypotheses 1a-1d, we focus on four specific
indicators (level of education, business education, managerial experience, and
industry experience). We coded the answers as binary (0/1). The proportion sharing
the respective characteristics and thus being scored ‘1’ on the variables in question
were 54.9% for university education; 70.9% for business education of some form;
89.5% for previous managerial experience and 81.5% for previous industry
experience.
The remaining types of resources (employee human capital, technological
expertise, other specific expertise, and tangible resources) stem from the access to
resource measures developed by Chandler and Hanks (1994), although a few items
were modified. Respondents were asked to evaluate on seven-point scales their access
to resources compared to other companies in their industry.
20
We entered the 14 items into an exploratory factor analysis with PCA
extraction and Varimax rotation, retaining factors with Eigen-values above 1.0. Four
factors emerged, reflecting the division put forward in our theory section. All factor
loadings were greater than 0.65. One item with a cross-loading over 0.32 was
removed; all others had lower cross-loadings. The results of the factor analysis offer
evidence pertaining to the discriminant and convergent validity of the measures. To
test internal consistency we examined coefficient alphas for the indices corresponding
to the four factors. The resulting factor structure, including the items included per
factor and the Cronbach’s alpha reliability for each of the indices, is presented in the
Appendix.
The first factor to emerge reflected Access to employee human capital and was
measured by five items. The content of these items provides a clear distinction
between what might be considered the founder’s human capital and the employee’s
human capital. Further, this set deals specifically with the usage of the human capital
of the employees, and not simply access to a certain type or skill-level of employee. In
addition, it assesses a quality found across the collective set of employees, whereas
the measures concerning expertise may be more likely to reflect the knowledge of one
or a few individuals, rather than signifying the entire collective of employees. The
Eigen value of the factor was 3.53.
The second component, access to other specific expertise, was formed by three
items and had an Eigen value of 1.63. This construct is conceptually different from
the access to employee human capital as it focuses more on the expertise and
management of the firm rather than that of the employees. The third measure of
access to resources, Access to technological expertise, was constructed with two items
and had an Eigen value of 1.40. As the name implies, the focus of this factor is on the
21
technological expertise of the firm, as opposed to marketing, management, or general
human capital of the individuals within the firm.
The final factor, access to tangible resources, was measured using two items
(“access to financial capital” and “access to modern plants and equipment”). Although
emerging as a separate factor with an Eigen-value above unity (1.09), the Cronbach’s
alpha for this measure (0.49) falls well below the recommended boundary of 0.70
(Nunnally, 1978). The conceptual differences between these two items, and the fact
that the internal reliability does not support their integration, prompted us to treat
these as separate items in the analyses rather than an aggregated ‘tangible resources’
factor. This is reflected in the hypotheses we put forward above.
We entered the nine resource improvements items from the second wave of
data collection into a separate, exploratory factor analysis. The resultant factor
structure clearly reflected two types of resources—reputational and operational,
respectively, which are anchored in resource-based theory (Dollinger, 2003; Grant
1991). These were made up of multiple items each. A third factor, which we felt
provided a theoretically valuable distinction from the other two factors but was only
represented by one item, focused on technological resource improvements. The items
included in these three factors and the Cronbach’s alpha reliability for the indices, are
presented in the Appendix. The response options for these questions were on a five
point scale from “much worse” to “much better”, and thus reflected the spectrum of
potential changes to the firms’ respective resource bases.
Control variables: We control for the age and size of the firm due to concerns
for liabilities of smallness and newness (Aldrich and Auster, 1986; Stinchcombe,
1965) which might affect resource and capability relationships. Finally, we use a
dummy for manufacturing in order to induce some level of control for industry sector.
22
ANALYSES & RESULTS
The correlations and descriptive statistics for the non-categorical variables are
presented in Table 1.
----------------------------------------------------------------------- INSERT TABLE 1 ABOUT HERE
-----------------------------------------------------------------------
The first two regressions are displayed in Table 2. Starting with the leftmost
base model we note the control variables only explain two percent of the variance in
idea generation capability. In the next step we entered the founder human capital
variables. In support of H1b a significant positive effect emerges for business
education; however, the change in R2 for the entire block of founder-based human
capital is only slightly significant (p < .10). Finally, the variables representing the four
classifications of resources were entered in the third block. Although the total
variance explained is still modest (13%) the change in R2 is highly significant and the
“access to resources” variables account for the lion’s share of the total variance
explained. In particular, access to employee human capital and access to technological
knowledge resources are ascribed important positive effects. This provides at least
partial support of H2 and H3a. H3b and H4a, regarding access to other specific
expertise and access to financial capital, respectively, are not supported as their
individual effects are in opposite direction of what was hypothesized and are
statistically significant.
-----------------------------------------------------------------------
INSERT TABLE 2 ABOUT HERE
-----------------------------------------------------------------------
We repeated the same hierarchical procedure with market disruptiveness
capability as the dependent variable, as seen in the right hand side of Table 2. The
23
results for the first two blocks (the control variables and founder human capital) are
similar to those for idea generation capability. The final block – access to resources –
was once again the largest significant contributor to the variance explained. The
overall variance explained reached a level which is quite acceptable especially given
the fact that access to resources accounted for 16 of the 25 percent total variance
explained. Noteworthy also is that access to employee human capital, access to
technological expertise and access to plants and equipment are the most important
factors, whereas access to other specific expertise was insignificant. This provides
further support for H2, H3a, and H4b.
The analyses for new product development capability and new process
development capability are displayed in Table 3. Due to space considerations we have
left out the respective ’base models’, which yielded no significant effects and
minuscule R2 values. The dependent variables were in these cases measured three
years after the control, founder-based and access to resource variables. This allows us
to include the variables ‘reputational resources improvements’, ‘operational resources
improvements’ and ‘technological resources improvements’ in the last block.
-----------------------------------------------------------------------
INSERT TABLE 3 ABOUT HERE
-----------------------------------------------------------------------
Improvements to resources bases had highly significant effects on both
dynamic capabilities. For ‘New product development capability’, the block of
variables measuring ‘resource improvements’ was the most influential block.
However, the individual effects of the three types of resource changes had varying,
albeit always positive, effects. Improvements to reputational resources and to
technological resources were statistically significant for both ‘New product
24
development capability’ and ‘New process development capability’. ‘Operational
resource improvements’ was only statistically significant for new product
development capability. The findings largely support H5a, H5b, and H5c.
It is also noteworthy that both the level of education and managerial
experience of the founder had relatively strong, significant effects on the process
innovation development capability, providing partial support for H1a and H1c. Level
of education and business education were statistically significant for new product
development capabilities, thus providing support for H1a and H1b. The firms’ access
to other resources, as measured in the first wave of data collection, was not
statistically significant for new product development capability. However, for new
process development capability, access to employee human capital and access to
financial capital are ascribed rather strong negative effects on new process
development capability. Similarly, access to other specific expertise and access to
plants and equipment had positive effects on this capability. These results provide a
mixed picture of our hypotheses.
In summary, our overarching hypothesis that resource endowments affect the
development of dynamic capabilities gain some support in our analyses. However,
this support is somewhat partial and mixed. Our interpretations of the patterns that
emerged follow immediately below.
DISCUSSION
An interpretation of the results
We regard our study as an early attempt to assess resource - dynamic
capabilities relationships systematically with a survey-based approach. This is a
challenging task and our approach to it admittedly has shortcomings in terms of
operationalizations and sample size. For this reason, the results should be regarded as
25
tentative and we would caution against elaborate interpretation of their finer details.
This said, we summarize our results in Table 4. Due to our small and uneven sample
size and aiming at emphasizing effect size at least equally with risk level (Cohen,
1994; Oakes, 1986) we here evaluated the evidence based both on
direction/magnitude (stand. coeff. > .10) and statistical significance (< .10) of the
effects.
-----------------------------------------------------------------------
INSERT TABLE 4 ABOUT HERE
-----------------------------------------------------------------------
We hold that three aspects of our results are particularly noteworthy. First, the
results demonstrate that we have had some success in establishing meaningful
relationships among resource types and dynamic capabilities based on systematic
survey data. Second, in most cases our hypotheses get only partial support. This could
be for two reasons. One possibility is that our hypotheses were too broadly stated; in
reality, different types of resources have varying influence on different types of
capabilities. We are not the first to observe this type of variability (cf. Ethiraj et al.,
2005) and it seems plausible in hindsight that, for example, the effects of employee
human capital should vary across different manifestations of dynamic capabilities. For
instance, we here get the expected positive effects on short term idea generation and
market disruptiveness capabilities. The role of individuals and their knowledge-based
resources is generally considered central to the study of innovation (Branzei and
Vertinsky, 2004; Subramaniam and Youndt, 2005). By contrast, the effect we find is
negative for the long-term development of new process development capability. This
negative result may be explained by the observation that larger firms are often less
flexible (Chen and Hambrick, 1995) and that having more employees and higher
26
human capital in the firms may be a source of organizational inertia (Hannan and
Freeman, 1977). Note that the number of employees had a negative, although not
statistically significant, effect on the development of this capability. As process
innovation is essentially about efficiency (Utterback and Abernathy, 1975), letting
employees go may be one component of this. Resistance to change, in this case, is
natural.
Similarly, a substantive interpretation of the differential effects by capability
type is possible for the following set of results. No effects are reported for industry
experience whereas managerial experience appears important only for new process
development. Weak support for the latter plus a significant negative effect of other
expert knowledge being the only effect ascribed to ‘other specific expertise’ partly
contradicts our original hypotheses. But these findings seem compatible with the
notion of “incumbent” inertia or myopia (Cliff, Jennings, and Greenwood, 2006;
Leonard-Barton, 1992; Mosakowski, 2002), where experienced managers tend to
think in established ways and have difficulty with dealing with industry novelty and
developing creative new solutions. Our findings suggest that as regards dynamic
capabilities insider, expert type of knowledge is of little use other than for new
process development, which arguably reflects the fine honing of established routines.
Another possible reason for partial support of our hypotheses is, simply, that
limitations to the operationalizations and the small sample size conceals some real
relationships. It seems difficult to substantively explain, for example, why business
education should have a positive effect on all capabilities and education level on the
last two only. Either way, we think an important task for future research is to perform
tests of theory-driven hypotheses on a higher level of specificity than what we have
employed.
27
A third noteworthy aspect of our findings is that they suggest improvements to
the resource base of the firm are paramount to previously measured resource
endowments in the development of dynamic capabilities. Several authors have
remarked that a more valuable approach to resource-based research is to treat firms as
consisting of dynamic flows of resources, not static stocks, and thus that the firm
changes its resource base over time (e.g. Amit and Schoemaker, 1993; Dierickx and
Cool, 1989; Helfat and Peteraf, 2003). In line with this, our results suggest that
resources and capabilities are moving targets where firms engage in a continuous
search for fit with the environment as well as with internal goals rather than
establishing one “best” configuration for the firm. This is a perspective that may be
particularly important to employ when discussing new firms, where the change in
resources between any two points in time may often represent a large share of the total
resource base.
For future research our results suggest the following. First, as regards basic
design it appears that the tricky issues of resource stocks, resource improvements,
dynamic capabilities and their relationships can be meaningfully addressed with a
quantitative, survey-based approach so further developments along that route seem
worthwhile. For such efforts following our example of a longitudinal design is
advisable as it not only secures time separation of cause and effect variables but—
importantly—allows for both stock and flow measures of resources. Assessing
resource stocks and changes more often (e.g., Lichtenstein and Brush, 2001) than we
did may result in more fine-grained knowledge as to the frequency, nature, and
direction of resource-base changes and their individual effects on capability
development. Further, a more theory-driven design leading to development of more
precise hypotheses than ours is recommended for future studies. While our results
28
concerning differential effects for varying types of dynamic capabilities are
interesting they are also speculative and uncertain. More precise theory a priori would
be part of the remedy against this.
As regards sampling the small size of the sample was a limitation of our study,
especially for the longitudinal analyses. A larger sample would reduce the influence
of stochastic variation. However, our focus on a theoretically valid sample rather than
a random sample from any new business population is a feature arguably worth
following. A further improvement would be to focus on a more narrowly defined
empirical population. This would reduce unmeasured heterogeneity that might blur
results.
As most extant research on dynamic capabilities offers no operationalizations
whatsoever, attempting this at all is a contribution of our study. Further, the use of
sub-indices for different aspects of dynamic capabilities (rather than one, global
index) seems worth following. However, we would hold that the specific
operationalizations we use for the key variables are also a major limitation of our
study. In retrospect, one could wish for better validated measures that were also
consistently applied across waves. Arguably, our study has at least determined that
further development in that direction is worthwhile. Future studies can benefit from
this insight as well as from improvements in conceptualizations and
operationalizations that have occurred after our data were collected (e.g. Dutta et al.,
2005). Importantly, a narrower sample (cf. above) would also allow for more precise,
customized operationalizations (cf. Cliff et al., 2006). As regards operationalization of
independent variables a lesson from our study is to develop measures that capture
resource use and not just resource possession. Our results concerning employee
human capital is a positive example of this. A negative example may be our results for
29
financial capital, where the mere presence of financial capital is ascribed no or
negative effects. While these unexpected findings fit within a developing set of
empirical findings that limited financial resources may in fact enhance entrepreneurial
behavior and the development of dynamic capabilities (Katila and Shane, 2005;
Mosakowski, 2002), we suggest that measures of particular types of investments and
usages of financial capital (allowed by access to financial capital), as opposed to
simply financial capital, may well facilitate a better understanding of the development
of dynamic capabilities.
CONCLUSIONS
We still have limited knowledge as to how and why dynamic capabilities
develop in new firms, despite the assumed performance outcomes of these
capabilities. This study is an early attempt at examining how founder- and firm
resource-base conditions, and changes to these resource bases over time, affect the
development of dynamic capabilities in new firms. Our findings support the notion
that resources and changes to these are important in the development of dynamic
capabilities. However, the respective impact of different types of resources varies for
different types of dynamic capabilities. In our view, this study provides valuable
insights as to the heterogeneous resource bases of new firms and how these varying
bases affect firm-level dynamic capabilities. Theory-driven, multi-wave research on
carefully restricted samples which allow for customized and therefore precise
operationalizations of resource possession and use seem to be a promising way
forward towards large-scale, systematic research into the development of dynamic
capabilities.
30
REFERENCES Abernathy, W.J. and J.M. Utterback (1978). ‘Patterns of Industrial Innovation’,
Technological Review, 80(7), pp. 40-47. Aldrich, H E. and E.R. Auster (1986). ‘Even dwarfs start small: Liabilities of age and
size and their strategic implications’. In: B. Staw and L. Cummings (eds.), Research in Organization Behavior, pp. 165-198. Greenwich, CT: JAI Press,.
Aldrich, H.E. and C. Zimmer (1986). ‘Entrepreneurship through social networks’. In: D. Sexton and R. Smilor (eds.), The Art and Science of Entrepreneurship, pp. 3-23. Cambridge, MA: Ballinger.
Alvarez, S.A. and L.W. Busenitz (2001). ‘The entrepreneurship of resource-based theory’, Journal of Management, 27(6), pp. 755-775.
Amit, R. and P.J.H. Schoemaker (1993). ‘Strategic assets and organizational rent’, Strategic Management Journal, 14(1), pp. 33-46.
Autio, E., H. Sapienza, and P. Almeida (2000). ‘Effects of Age at Entry, Knowledge Intensity, and Imitability on International Growth’, Academy of Management Journal, 43(5), pp. 909-1014.
Baker, T. and R.E. Nelson (2005). ‘Creating something from nothing: Resource construction through entrepreneurial bricolage’, Administrative Science Quarterly, 50(3), pp. 329-366.
Bantel, K.A. and S.E. Jackson (1989). ‘Top management and innovation in banking: Does the composition of the top management team make a difference?’ Strategic Management Journal, 10(1), pp. 107-124.
Borsch, O.J., M. Huse, and K. Senneseth (1999). ‘Resource configurations, competitive strategies, and corporate entrepreneurship: An empirical examination of small firms’, Entrepreneurship Theory & Practice, 24(1), pp. 49-70.
Branzei, O. and S. Thornhill (2006). ‘From ordinary resources to extraordinary performance: Environmental moderators of competitive advantage’, Strategic Organization, 4(1), pp. 11-41.
Branzei, O. and I. Vertinsky (2004). ‘Strategic pathways to product innovation capabilities in SMEs’, Journal of Business Venturing, 21(1), pp. 75-105.
Brown, T.E., P. Davidsson, and J. Wiklund (2001). ‘An operationalization of Stevenson's conceptualization of entrepreneurship as opportunity-based firm behavior’, Strategic Management Journal, 22(10), pp. 953-968.
Brush, C.G., P.G. Greene, and M.M. Hart (2001). ‘From initial idea to unique advantage: The entrepreneurial challenge of constructing a resource base’, Academy of Management Executive, 15(1), pp. 64-78.
Census, U.S Bureau of the (1992). Characteristics of Business Owners. U.S. Government Printing Office, Washington DC.
Chandler, G.N. and S.H. Hanks (1994). ‘Market attractiveness, resource-based capabilities, venture strategies and venture performance.’ Journal of Business Venturing, 9(4), pp. 331-349.
Chen, M.-J. and D.C. Hambrick (1995). ‘Speed, stealth, and selective attack: How small firms differ from large firms in competitive behavior’, Academy of Management Journal, 38(2), pp. 453-482.
Cliff, J.E., P.D. Jennings, and R. Greenwood (2006). ‘New to the game and questioning the rules: The experiences and beliefs of founders who start imitative versus innovative firms’, Journal of Business Venturing, 21(5), pp. 633-663.
31
Cohen, J (1994). ‘The earth is round (p<.05)’, American Psychologist, 47(12), pp. 997-1003.
Cooper, G., F.J. Gimeno-Gascon, and C.Y. Woo (1994). ‘Initial human and financial capital as predictors of new venture performance’, Journal of Business Venturing, 9(5), pp. 371-395.
Covin, J.G. and D.P. Slevin (1991). ‘A conceptual model of entrepreneurship as firm behavior’, Entrepreneurship Theory & Practice, 16(1), pp. 7-25.
Davidsson, P. (2004). Researching Entrepreneurship. Springer, New York. Davidsson, P. (2006). ‘Nascent entrepreneurship: Empirical studies and
developments’, Foundations and Trends in Entrepreneurship, 2(1), pp. 1-76. Davidsson, P., L. Achtenhagen, and L. Naldi (2006). ‘What do we know about small
firm growth?’ In: S. Parker (ed.), The Life Cycle of Entrepreneurial Ventures (Vol. 3), pp. 359-396. New York: Springer.
Davidsson, P. and B. Honig (2003). ‘The role of social and human capital among nascent entrepreneurs’. Journal of Business Venturing, 18(3), pp. 301-331.
Dierickx, I. and K. Cool (1989). ‘Asset stock accumulation and sustainability of competitive advantage’, Management Science, 35(12), pp. 1504-1513.
Dollinger, M.J. (2003). Entrepreneurship: Strategies and resources (3rd edition). Prentice Hall, Upper Saddle River, NJ.
Dosi, G., R.R. Nelson, R. R., and S.G. Winter (2000). ‘Introduction’. In: G. Dosi, R. R. Nelson and S. G. Winter (eds.), The Nature and Dynamics of Organizational Capabilities, pp. 1-22. Oxford: Oxford University Press.
Dutta, S., O. Narasimhan, and S. Rajiv (2005). ‘Conceptualizing and measuring capabilities: Methodology and empirical application’, Strategic Management Journal, 26(3), pp. 277-285.
Eisenhardt, K.M. and J.A. Martin (2000). ‘Dynamic capabilities: What are they?’ Strategic Management Journal, 21(10-11), pp. 1105-1121.
Ethiraj, S.K., P. Kale, M.S. Krishnan, and J.V. Singh, (2005). ‘Where Do Capabilities Come From and How Do They Matter? A study in the software services industry’, Strategic Management Journal, 26(1), pp. 25-45.
Geroski, P.A. (1995). ‘What do we know about entry?’ International Journal of Industrial Organization, 13(4), pp. 421-440.
Gimeno, J., T.B. Folta, A.C. Cooper, and C.Y. Woo (1997). ‘Survival of the fittest? Entrepreneurial human capital and the persistence of underperforming firms’, Administrative Science Quarterly, 42(4), pp. 750-783.
Grant, R.M. (1991). ‘The resource-based theory of competitive advantage: Implications for strategy formulation’, California Management Review, 33(3), pp. 114-135.
Hambrick, D.C. and P.A. Mason (1984). ‘Upper echelons: The organization as a reflection of its top managers’, Academy of Management Review, 9(2), pp. 193-207.
Hannan, M. and J. Freeman (1977). ‘The population ecology of organizations’, American Journal of Sociology, 82(5), pp. 929-964.
Hansen, M.T. and J. Birkinshaw (2007). ‘The innovation value chain’, Harvard Business Review, 85(6), pp. 121-130.
Helfat, C.E. (1997). ‘Know-how and asset complementarity and dynamic capability accumulation: The case of R&D’, Strategic Management Journal, 18(5), pp. 339-360.
32
Helfat, C.E. and M.B. Lieberman (2002). ‘The birth of capabilities: market entry and the importance of pre-history’, Industrial and Corporate Change, 11(4), pp. 725-760.
Helfat, C.E. and M.A. Peteraf (2003). ‘The Dynamic Resource-Based View: Capability Lifecycles’, Strategic Management Journal, 24(10), pp. 997-1010.
Henderson, R. and I. Cockburn (1994). ‘Measuring Competence? Exploring firm effects in pharmaceutical research’, Strategic Management Journal, 15, pp. S63-S84.
Hitt, M.A., L. Bierman, K. Shimizu, and R. Kochhar (2001). ‘Direct and moderating effects of human capital on strategy and performance in professional service firms: A resource-based perspective’, Academy of Management Journal, 44(1), pp. 13-28.
Katila, R. and S. Shane (2005). ‘When does lack of resources make new firms innovate?’ Academy of Management Journal, 48(5), pp. 814-829.
King, A.A. and C.L. Tucci (2002). ‘Incumbent entry into new market niches: The role of experience and managerial choice in the creation of dynamic capabilities’, Management Science, 48(2), pp. 171-186.
Kor, Y.Y. and J.T. Mahoney (2005). ‘How dynamics, management, and governance of resource deployments influence firm-level performance’, Strategic Management Journal, 26(5), pp. 489-496.
Laitinen, E.K. (1992). ‘Prediction of failure of a newly founded firm’, Journal of Business Venturing, 7(4), pp. 323-340.
Lane, P.J. and M. Lubatkin (1998). ’Relative absorptive capacity and inter-organizational learning’, Strategic Management Journal, 19(5), pp. 461-477.
Leonard-Barton, D. (1992). ‘Core capabilities and core rigidities: A paradox in managing new product development’, Strategic Management Journal, 13(5), pp. 111-126.
Lichtenstein, B.M.B. and C.G. Brush (2001). ‘How do "resource bundles" change in new ventures? A dynamic model and longitudinal exploration’, Entrepreneurship: Theory & Practice, 25(3), pp. 37-58.
Lord, R.G. and K.J. Maher (1990). ‘Alternative information-processing models and their implications for theory, research, and practice’, Academy of Management Review, 15(1), pp. 9-28.
Makadok, R. (2001). ‘Toward a synthesis of the resource-based and dynamic-capability views of rent creation’, Strategic Management Journal, 22(5), pp. 387-401.
McDougall, P., J.G. Covin, R.B. Robinson, and L. Herron (1994). ‘The Effects of Industry Growth and Strategic Breadth on New Venture Performance and Strategy Content’, Strategic Management Journal, 15(7), pp. 537-554.
Mosakowski, E. (2002). ‘Overcoming resource disadvantages in entrepreneurial firms: When less is more’. In: M. A. Hitt, R. D. Ireland, S. M. Camp and D. L. Sexton (eds.), Strategic Entrepreneurship: Creating a new mindset, pp. 106-126. Oxford: Blackwell Publishers.
Nunally, J.C. (1978). Psychometric theory. McGraw-Hill, New York. Oakes, M. (1986). Statistical Inference: A Commentary for the Social and
Behavioural Sciences. Wiley, Chichester, UK. Penrose, E. (1959). The Theory of the Growth of the Firm. Oxford University Press,
Oxford. Peteraf, M.A. (1993). ‘The cornerstones of competitive advantage: A resource-based
view’, Strategic Management Journal, 14(3), pp. 179-191.
33
Peteraf, M.A. and J.B. Barney (2003). ‘Unravelling the resource-based tangle’, Managerial and Decision Economics, 24(4), pp. 309-323.
Ranft, A.L. and M.D. Lord (2002). ‘Acquiring new technologies and capabilities: A grounded model of acquisition implementation’, Organization Science, 13(4), pp. 420-441.
Rauch, A., M. Frese, and A. Utsch (2005). ‘Effects of human capital and long-term human resources development and utilization on employment growth of small-scale businesses: A causal analysis’, Entrepreneurship Theory & Practice, 29(6), pp. 681-693.
Rosenbloom, R.S. (2000). ‘Leadership, capabilities, and technological change: The transformation of NCR in the electronic era’, Strategic Management Journal, 21(10/11), pp. 1083-1103.
Sanchez, R. (1995). ‘Strategic flexibility in product competition’, Strategic Management Journal, 16, pp. S135-S159.
Schultz, T.W. (1961). ‘Investment in human capital’, American Economic Review, 51(1), pp. 1-17.
Sirmon, D.G., M.A. Hitt, and R.D. Ireland (2007). ‘Managing firm resources in dynamic environments to create value: Looking inside the black box’. Academy of Management Review, 32(1), pp. 273-292.
Smith, K.G, C.J. Collins, and K.D. Clark (2005). ‘Existing knowledge, knowledge creation capability, and the rate of new product introduction in high-technology firms’, Academy of Management Journal, 48(2), pp. 346-357.
Sorensen, J.B. and T.E. Stuart (2000). ‘Aging, Obsolescence, and Organizational Innovation’, Administrative Science Quarterly, 45(1), pp. 81-112.
Stevenson, H.H. and J.C. Jarillo (1990). ‘A paradigm of entrepreneurship: Entrepreneurial management’, Strategic Management Journal, 11, pp. S17-S27.
Stinchcombe, A.L. (1965). ‘Social structure and organizations’. In: J. G. March (ed.), Handbook of Organizations, pp. 142-193. Chicago: Rand McNally.
Subramaniam, M., and M.A. Youndt (2005). ‘The influence of intellectual capabilities on the types of innovative capabilities’, Academy of Management Journal, 48(3), pp. 450-463.
Teece, D. J., G. Pisano, and A. Shuen (1997). ‘Dynamic Capabilities and Strategic Management’, Strategic Management Journal, 18(7), pp. 509-533.
Tsai, K. (2004). ‘The impact of technological capability on firm performance in Taiwan's electronics industry’, Journal of High Technology Management Research, 15(2), pp. 183-195.
Tushman, M.L. and P. Anderson (1986). ‘Technological Discontinuities and Organizational Environments’, Administrative Science Quarterly, 31(3), pp. 439-465.
Utterback, J.M. and W.J. Abernathy (1975). ‘A dynamic model of process and product innovation’, Omega, 3(6), pp. 639-656.
Verona, G. and D. Ravasi (2003). ‘Unbundling dynamic capabilities: An exploratory study of continuous product innovation’, Industrial and Corporate Change, 12(3), pp. 577-606.
Wiklund, J. and D.A. Shepherd (2003). ‘Knowledge-based resources, entrepreneurial orientation, and the performance of small and medium sized firms’, Strategic Management Journal, 24(13), pp. 1307-1314.
34
Yli-Renko, H., A. Autio, and H, Sapienza (2001) ‘Social capital, knowledge acquisition and knowledge exploitation in young technology-based firms’, Strategic Management Journal, 22(6), pp. 587-613
Zahra, S.A. and I. Filatotchev (2004). ‘Governance of the entrepreneurial threshold firm: A knowledge-based perspective’, Journal of Management Studies, 41(5), pp. 885-897.
Zahra, S.A., D.O. Neubaum, and G.M. El-Hagrassey (2002). ‘Competitive Analysis and New Venture Performance: Understanding the Impact of Strategic Uncertainty and Venture Origin’, Entrepreneurship Theory & Practice, 27(1), pp. 1-28.
Zahra, S.A., H. Sapienza, and P. Davidsson (2006). ‘Entrepreneurship and dynamic capabilities: A review, model and research agenda’, Journal of Management Studies, 43(4), pp. 917-955.
Zollo, M. and S.G. Winter (2002). ‘Deliberate learning and the Evolution of Dynamic Capabilities’, Organization Science, 13(3), pp. 339-351.
Table 1. Descriptive statistics and correlations of relevant variables
Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Age1 7.42 1.64 2. Nr employees 52.52 48.48 .07
3. Access to employee human capital 24.57 3.44 .240** -.034
4. Access to technological expertise 9.34 1.94 .241** .052 .296**
5. Access to other specific expertise 12.99 2.53 .074 .086 .365** .258**
6. Access to financial capital 4.67 1.45 .084 .115 .059 -.041 .179**
7. Access to modern plants/equipment 4.51 1.17 .067 .020 .100 .206** .184** .277**
8. Reputational resource improvements 14.77 2.08 .074 -.060 .113 .124 -.055 -.072 -.107
9. Operational resource improvements 13.87 2.18 .073 -.034 .107 .264** .033 -.012 .054 .242**
10. Technological resource improvements 3.50 1.72 -.115 -.110 -.082 -.046 -.179* .082 .037 .004 .220*
11. Idea generation capabilities 14.23 3.35 .108 -.001 .221** .188** .019 -.082 .015 .237** .165 .008
12. Market disruptiveness capabilities 25.77 5.41 .120 -.054 .294** .319** .282** .108 .243** .135 .103 .002 .385**
13. New product development capabilities 10.27 1.38 .060 .088 .165 .239* .158 .013 .065 .299** .313** .099 .175 .380**
14. New process development capabilities 6.90 1.06 -.008 .025 -.090 .190* .145 -.064 .053 .096 .193* .157 .113 .208* .612**
* p < 0.05, ** p < 0.01 Notes: 1 This age is calculated at the time of the second wave of data collection, (i.e. three years following the first wave).
Table 2. Hierarchical regression with idea generation capability and market disruptiveness capability
Idea generation capability Market disruptiveness capability Base Founder Access to Base Founder Access to model resources resources model resources resourcesAge .133* .137* .071 .119+ .126+ .026 Number of employees -.012 -.046 -.015 -.064 -.112 -.104 Manufacturing -.025 -.014 -.002 -.050 -.031 .000 Level of education .114* .083 .073 .022 Business education .101+ .117* .195** .179** Managerial experience -.048 -.050 .079 .006 Industry experience -.007 -.021 -.071 -.050 Access to employee human capital .229** .171**
Access to technological expertise .115+ .183**
Access to other specific expertise -.136* .082
Access to financial capital -.104+ .016 Access to modern plants/equipment .079 .214**
R2 .018 .046+ .126*** .020 .086** .250*** Adjusted R2 .006 .017+ .080*** .007 .057** .208***Change in R2 .018 .029+ .080*** .020 .066** .164***
Notes: Standardized regression coefficients are displayed in the table. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001; n = 238.
Table 3. Hierarchical regression with new product and process development capabilities over time New product development capability New process development capability
Founder Access to Resource Founder Access to Resource resources resources improvements resources resources improvements Age .057 -.007 -.025 -.003 .005 -.010 Number of employees .019 .032 .042 -.029 -.089 -.093 Manufacturing .130 .108 .145 .209* .198* .225* Level of education .144+ .145+ .153+ .233* .269** .280** Business education .136+ .131 .132+ .059 .107 .102 Managerial experience .149 .091 .089 .199* .153+ .147+ Industry experience -.022 -.023 -.036 -.170* -.132+ -.113 Access to employee human capital
.099 .081 -.208+ -.186*
Access to technological expertise .145+ .032 .096 .029
Access to other specific expertise
.028 .084 .155+ .187*
Access to financial capital -.070 -.082 -.181* -.200*
Access to modern plants/equipment
.142+ .094 .205* .137+
Reputational resources improvements
.216* .134+
Operational resources improvements .268** .094
Technological resources improvements .167+ .268**
R2 .080+ .143 .315*** .157** .264* .372** Adjusted R2 .016+ .035 .204*** .098** .172* .271** Change in R2 .067+ .063 .172*** .137** .107* .109**
Notes: Standardized regression coefficients are displayed in the table. Due to space considerations the respective ‘base models’ have been left out. There were no statistically significant coefficients in these models, and the R2 minus Change in R2 difference provides information on their respective explanatory power. + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001; n = 108.
38
Table 4. Summary outcomes of the hypotheses
I. Idea generation
capability (n= 238)
II. Market disruptiveness
capability (n= 238)
III. New product development
capability (n=108)
IV. New process development
capability (n=108)
H1a: Level of education N N N N Y Y Y Y Partly supported (III & IV) H1b: Business education Y Y Y Y Y Y Y N Supported H1c: Managerial experience N N N N N N Y Y Partly supported (IV only) H1d: Industry experience N N N N N N N N Not supported H2: Employee human capital Y Y Y Y N N N (Y) Partly supported (I & II) /
Partly reversed (IV)
H3a: Technological expertise Y Y Y Y N N N N Partly supported (I & II)
H3b: Other specific expertise N (Y) N N N N Y Y Partly reversed (I only) /
Partly supported (IV only)
H4a: Financial capital N (Y) N N N N N (Y) Partly reversed (at least I & IV)
H4b: Plants & equipment N N Y Y Y N Y Y Partly supported (II & IV) H5a: Reputational resource improvements n/a n/a Y Y Y Y Supported
H5b: Operational resource improvements n/a n/a Y Y N N Partly supported (III)
H5c: Technological resource improvements n/a n/a Y Y Y Y Supported
Note: The first letter (yes/no) in each column denotes whether or not the relationship is in the hypothesized direction and larger than 0.10 in magnitude (stand. coeff.). The second letter denotes whether the relationship is statistically significant at < 0.10. This is to avoid—in the absence of a known cost for type I vs. type II errors and/or a sound theoretical argument for non-effect—the questionable practice of interpreting non-negligible effects in the direction predicted by sound theory as evidence against it (Oakes, 1986). A ‘Y’ within parentheses means the result is ‘significant’ in the non-hypothesized direction.
APPENDIX Variable & composite items Cronbach’s
alpha Dependent variables Idea generation capability .73 We have more promising ideas than we have time and the resources to pursue. We never experience a lack of ideas that we can convert into profitable products/service.
Market disruptiveness capability .73 Over the past few years, our firm has released very many new products or services to the market.
Over the past few years, changes to our product lines have been radical. Our firm generally initiates changes that our competitors are forced to thereafter react to.
Our firm is often the first firm to introduce new products, systems, production methods, etc.
We heavily invest in innovation and the development of new products and services. New product development capabilities .60 Relative to your two most important competitors, how would you rate our firm’s performance over the past three years concerning…
The development of new products or services The quality of newly developed products or services The diversity of newly developed products or services New process development capabilities .63 Relative to your two most important competitors, how would you rate our firm’s performance over the past three years concerning…
The development of new product development methods The adaptation of new technologies in existing processes Independent variables Level of education – Which is your highest level of completed education? n/a Business education – Have you received any formal education in business administration?
n/a
Managerial experience – Before you became the manager of this company, did you have any experience from management positions in other companies?
n/a
Industry experience – Before you became the manager of this company, did you have any previous work experience from this industry?
n/a
Access to employee human capital .78 Access to staff with a positive commitment towards the company's development Access to highly productive staff Access to staff educated in giving superior customer service Access to staff who like to contribute ideas for new products/services Access to staff capable of marketing our products/services well Access to other specific expertise .60 Access to expertise in marketing Access to top management time to devote to long term development Access to special expertise regarding management Access to technological expertise .74 Access to technical expertise Access to expertise in development of products/services Access to financial capital n/a Access to modern plants and equipment n/a
40
Reputational resource improvements .82 Please evaluate the extent to which your access to the following resources has changed over the past three years…
Changes to brand name recognition Changes to company name Changes to company reputation Changes to the reputation of company executives Operational resources improvements .63 Please evaluate the extent to which your access to the following resources has changed over the past three years…
Changes to human capital Changes to financial capital Changes to manufacturing resources Changes to marketing resources Technological resources improvements n/a
41
BIOGRAPHIES Alexander McKelvie is Assistant Professor of Entrepreneurship at the Whitman School of Management at Syracuse University, USA. He earned his Ph.D. in 2007 from Jönköping International Business School in Sweden. Professor McKelvie’s research primarily concerns new firms, and in particular looks how and why new firms grow. He has been involved in large-scale, longitudinal data collection efforts examining internal characteristics and behaviours of new firms in facilitating growth. Per Davidsson is Professor in Entrepreneurship and Director of Research at the Faculty of Business at Queensland University of Technology, Australia. He has published over 100 works on various entrepreneurship-related topics, including many on venture creation processes and small firm growth. He serves on the editorial boards for several of the leading journals in the field. Professor Davidsson has led several major research programs including the current Comprehensive Australian Study on Entrepreneurial Emergence (CAUSEE).
