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2010 年 8 月第十三卷三期 • Vol. 13, No. 3, August 2010
Knowledge Breadth, Path Dependence and
New Technology Investment
Hsuan Lo
Hsien-Jui Chung
Rhay-Hung Weng
http://cmr.ba.ouhk.edu.hk
Web Journal of Chinese Management Review • Vol. 13 • No 3 1
Knowledge Breadth, Path Dependence and New Technology Investment
Hsuan Lo
Hsien-Jui Chung
Rhay-Hung Weng
Abstract
This study aims to explore how firms’ knowledge breadth and past investment
experience influence the unrelatedness of new technology investments in the
Taiwan hospital industry. We adopt secondary data to empirically examine
hypotheses. Our research sample consists of general hospitals in Taiwan in the
year 2001. The results find that firms with broad knowledge are more likely to
invest in unrelated new technology, but firm with similar investment experience
are less likely to invest in unrelated new technology. The results also find that
similar investment experience negatively moderate the relationship between
knowledge breadth and unrelatedness of new technology investments. The
study contributes to the further understanding of the effect of experience and
knowledge on how firms make new technology investment.
Key words: knowledge breadth, path dependence, new technology investment.
Hsuan Lo, Department of Hospital and Health Care Administration, Chia Nan University of
Pharmacy & Science
Hsien-Jui Chung, Department of Business Administration, National Chung Cheng University
Rhay-Hung Weng, Institute of Health Information and Management, Chia Nan University of
Pharmacy and Science
Web Journal of Chinese Management Review • Vol. 13 • No 3 2
Introduction
A number of studies assert that organizations with heterogeneous knowledge
scopes have more opportunities to explore unrelated fields (Chang, 1996; Miller,
2004). Chang (1996) and Miller (2004) emphasize that knowledge breadth
strongly influences an organization’s strategic actions (Chang, 1996; Miller,
2004). An organization with a broad range of knowledge has more opportunities
to enter new fields.
New technology investment is an essential strategy for an organization to sustain
competitive advantage and assure survival (Oliver, 1997; Neumann et al., 1999).
When investing in new technology, some organizations search in areas closely
related to their current knowledge, while others invest in technology highly
dissimilar to their current knowledge (Stuart & Podolny, 1996). Researchers
have found that it is broad knowledge range that helps organizations adopt new
technology (Ethiraj et al., 2005; Knott, 2003).
Though organizations with broad knowledge ranges have a strong potential to
invest in unrelated technology, such potential can be limited by their experience.
The term path dependent is used to describe how organizations’ past experiences
limit their future actions; often organizations prefer a local search and follow past
experiences or routines in deciding on courses of action (Levitt & March, 1988;
Levinthal & March, 1993); these organizations are then said to be path dependent.
We might expect then that organizations which are highly path dependent are less
likely to undertake new investments in technologies which are unrelated to the
primary technologies their main operations use.
This study argues that path dependence and knowledge breadth both directly
influence the relatedness of new technology investments. Their influence is not
simple, however; it is interrelated. Organizations with broad knowledge do tend
to explore new technology unrelated to the technology they currently use.
However, if these organizations are highly path dependent, that is, if they are
strongly influenced by their past experiences, their tendency to explore new and
unrelated technology is weakened. A better understanding of the interaction
between these two effects can contribute to a deeper understanding of
organizations' action choices. This study explores how path dependence and
knowledge breadth together influence organizations’ new technology investment
Web Journal of Chinese Management Review • Vol. 13 • No 3 3
decisions by examining Taiwanese hospitals’ decisions to purchase high
technology equipment.
Theory and Hypotheses
Knowledge Breadth and New Technology Investments
Staw (1977) suggests that an organization with little action-related knowledge
would be forced to experiment on strategic actions. Since experiments are costly
and risky, and thus raise uncertainty and risk, a lack of action-related knowledge
reduces the likelihood that an organization will enter a new field. Cohen &
Levinthal (1990) propose the absorptive capacity concept, arguing that the
innovative capability of an organization can be restrained if it puts little effort in
accumulating knowledge resources. Kogut & Zander (1992) also emphasize the
importance of knowledge resources, stating that an organization learns new skills
by recombining its existing knowledge. Hence, the broader the knowledge an
organization possesses, the more likely it is to expand into new markets.
The breadth of knowledge accumulated by an organization affects its likelihood
to enter new fields (Chang, 1996; Knott, 2003; Miller, 2004; Rodan & Galunic,
2004). As Kanter (1988) indicates, many good ideas come from cross-discipline
fields, which means that recombining extensive knowledge can generate new
ideas. In other words, through reintegrating, re-exchanging and recombining
existing knowledge, an organization with broader knowledge can stimulate more
new ideas and increase its chances of entering new fields (Ethiraj et al., 2005;
Knott, 2003; Rodan & Galunic, 2004). The organization can quickly recognize
new opportunities and adjust its strategy to enter new fields (Chang, 1996; Miller,
2004). Moreover, an organization with broader knowledge is more flexible and
capable of adopting innovation (Bowman & Hurry, 1993; Nelson & Winter, 1982;
Penrose, 1959). Thus the broader an organization's knowledge, the more likely it
is to expand into new fields by applying existing knowledge.
Based on the above discussion, this study predicts that an organization with
broader knowledge is more likely to invest in new technology in an unrelated
field. We thus propose the following hypothesis:
Hypothesis 1: the broader the knowledge an organization has, the greater the
unrelatedness of its new technology investments.
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Path dependence and New Technology Investments
A number of scholars (Cohen & Levinthal, 1990; Gavetti & Levinthal, 2000;
Levinthal & March, 1993) have found that the strategic actions of an organization
are path dependent. They observe that past experience tends to limit an
organization’s actions to those similar to its past practices. Path dependence
influences an organization’s likelihood of entering new unrelated fields (Huff et
al., 1992) through two primary mechanisms: self-specialization and constrained
cognition. First, self-specialization causes an organization follow its past
experiences to solve problems. Organizations prefer following past experience
that has been successfully applied instead of searching for new alternatives
(Levitt & March, 1988; Levinthal & March, 1993; March, 1991). Therefore,
self-specialization reduces the opportunities of entering new fields. Second,
senior managers with constrained cognition usually cannot recognize the urgency
of changes in strategy. Consequently they search for alternatives based on past
experience, which lowers their organizations’ likelihood of entering new fields
(Garud & Rappa, 1994; Simon, 1955; Singh, 1986).
Empirical studies have confirmed these insights. For example, Stuart & Podolny
(1996) find that the evolution of technology niches displays path dependence
phenomena in the semi-conductor industry in Japan; Baum et al. (2000) also
found path dependence phenomena in M&A (merger and acquisition) strategy;
Chuang & Baum (2003) observed it in nursing home chain naming strategies .
Based on the above discussion, this study proposes that the more path dependent
an organization is, the less marked the unrelatedness of its new technology
investment will be. We thus propose Hypothesis 2:
Hypothesis 2: the stronger path dependence is, the lower the degree of
unrelatedness of its new technology investments.
The Moderating Role of Path Dependence
Hypothesis 1 assumes that an organization with broader knowledge is more likely
to invest in new technology in unrelated fields. However, the effect of knowledge
breadth on the relatedness of new technology investments may be moderated by
the extent to which an organization is path dependent.
The basic idea supporting the moderating role of path dependence is that an
Web Journal of Chinese Management Review • Vol. 13 • No 3 5
organization’s deployment and utilization of current resources are moderated by
its past experience. Broad knowledge does allow an organization opportunities to
reintegrate, re-exchange and recombine existing knowledge in novel ways, but if
the organization is highly path dependent, it follows past experience in taking
action (Levitt & March, 1988; Levinthal & March, 1993; March, 1991).
Moreover, because of constrained cognition, senior managers in path dependent
organizations frequently rely on local searches and simplify the processing of
information (Garud and Rappa, 1994; Simon, 1955; Singh, 1986). As a result, an
organization’s exploitation of its knowledge is limited. Novel ways of combining
and integrating existing knowledge are likely to be ignored. As a result, the
relationship between broad knowledge and unrelated new technology investment
is weakened.
In contrast, for an organization less dependent on past experience, broader
knowledge can have a stronger impact on new technology investment. If an
organization with broad knowledge is less constrained by past experience and if
its senior managers are more willing to search extensively for information and
seek new alternatives, the organization is more likely to enter unrelated new
technology fields. In other words, as path dependence increases, the effect of
knowledge breadth on unrelatedness of new technology investment is weaker.
Thus we propose Hypothesis 3:
Hypothesis 3: the stronger path dependence is, the weaker the positive effect of
knowledge breadth will be on the unrelatedness of new technology investments.
Research methods
Research Sample and Data
To examine the effect of knowledge breadth and path dependence on the
relatedness of new technology investments, this study focuses on Taiwan’s
hospital industry, examining hospitals’ investments in high-tech medical
equipment as a way of measuring the basis for new technology investments.
The categories of high-tech medical equipment in this study are listed in
Appendix 1. These categories are defined and registered by Taiwan’s Department
of Health. All hospitals must report their medical equipment by category and
quantity to the Department of Health.
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There are three reasons to take investment in high-tech medical equipment in the
hospital industry as our research target. First, high-tech medical services are the
core activities of a hospital (Kimberly & Evanisko, 1981), and can affect the
survival rate of a hospital (Succi et al., 1997). Hospitals rely on high technology
equipment to provide high-tech medical services. Owning a specific type of high
technology equipment has two implications for hospitals: it not only requires
hospitals to have a training program and operation routines, but also gives them
access a specific client market. Thus investment in new technology equipment is
extremely important for hospitals.
Second, investment in high-tech medical equipment changes the functions of
specialist and technician roles, and has a tremendous effect on a hospital’s
performance (Barley, 1986). Since purchasing high technology equipment
requires hospitals to develop training program and operation routines, hospitals
with only a limited range of knowledge and experience, when adopting
technology completely unrelated to current equipment spend much more effort to
get acquainted with the new technology.
Third, Taiwan hospitals competed in high-tech medical equipment investments
from 1992 to 2003. Hence, the characteristics of the hospital industry in Taiwan
during this period provide an appropriate context for studying the relationship
between knowledge breadth, path dependence and new technology investment.
Our research sample consists of general hospitals in Taiwan in the year 2001.
This study uses secondary data to calculate research variables. The sources of this
secondary data include the Hospital Service Database compiled by the
Department of Health, and the Population Statistics Database compiled by the
Ministry of the Interior. Geographical areas that have fewer than four hospitals,
such as Kinmen County and Lienchiang County, are excluded to prevent skewing
due to competitive intensity. In addition, to test relatedness, hospitals investing in
ten technologies for the first time, or which did not invest in any one category by
the year 2002 are dropped out. The final sample includes 248 general hospitals.
Hypotheses are tested using data at two time points to avoid obscuring cause and
effect. Independent variables and control variables are measured with data from
2001 and dependent variables with data from 2002.
This study used an expert opinion survey to measure the relatedness, in terms of
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scientific and technical principles, between ten categories of high-tech medical
equipment, in order to measure the relatedness of new technology investments.
To compile the survey, 17 attending physicians were selected from medical
centers and regional and district hospitals to participate in the survey (see
Appendix 2 for the survey instrument). Each physician was asked to give a
correlation score, in terms of scientific and technical principles, for each pair of
the Department of Health's ten categories of high-tech medical equipment. The
average correlation score of each pair of the ten categories from the 17 physicians
was then used to compute dependent variables (the relatedness of new technology
investments). The reliability coefficient on correlation scores of each pair of ten
technology categories among the 17 respondents was very high (α=0.960). The
average age of the respondents was 38.9 years old (standard deviation of 4.48),
while the average job seniority was 11.92 years (standard deviation of 4.09).
Dependent variables
Degree of unrelatedness:
The degree of unrelatedness of new technology investments was chosen as the
dependent variable in this study. Unrelatedness degree is measured by the average
of the correlation scores, in terms of scientific and technical principles, between
new high-tech medical equipment purchased during 2002 and equipment owned
in the previous year. A low average value represents high relatedness in new
technology investments. The correlation score is measured by a six-point Likert
Scale (1-6 scores); 1 means highly related, whereas 6 means low related. For
example, a firm which owns high-tech equipment, CT and NMR, might invest in
new technology, HPR and RB. The correlation score between CT and HPR, CT
and RB, NMR and HPR, NMR and RB are 3.94, 5.47, 4.53, and 5.59 respectively.
The unrelatedness degree for the firm’s investment in new technology is
(3.94+5.47+4.53+5.59)/4=4.88.
Independent variables
Knowledge breadth
Organizational knowledge is embedded in the human resources of an organization
(Farjoun, 1994). Human resources is the source of generating and accumulating
organizational knowledge. Therefore, the profile of an organization’s human
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resources can reflect the characteristics of organizational knowledge (Chang,
1996). Following Chang’s (1996) study, this paper defines knowledge breadth as
the diversification degree of specialty physician categories of a hospital measured
by the entropy index of diversification (Hall & John, 1994; Hoskission et. al.,
1993). We define
27
1
)/1( j
ijiji pLnpbreadthknowledge ; j refers to the 27
categories of specialty physicians that hospital i owns (see Appendix 3). Pij
presents the ratio of the number of specialty physicians j to that of total specialty
physicians in the hospital i. The greater the entropy value, the greater the
knowledge breadth.
Path dependence
Path dependence is the tendency to follow past routines in strategic actions (Cohen &
Levinthal, 1990; Levinthal & March, 1993). March (1991) classifies actions into two
types: exploitative actions and explorative actions. Exploitative actions are those
following past routines; explorative actions are those never before employed by an
organization. Organizations employing more exploitative actions are much more path
dependent (March, 1991). In this paper, path dependence is measured by the ratio of the
total number of exploitative actions to that of explorative and exploitative actions taken
by an organization between 1999 and 2001. In this paper, exploitative actions represent
technology investments in medical equipment categories already invested in by the focal
hospital; while explorative actions represent technology investments in medical
equipment categories never before invested in by the hospital.
Control variables
Environmental variables controlled here include market demand, market
competitive intensity, and environmental uncertainty. Some studies show that
market demand affects the opportunity for hospitals’ new technology investments
(Baker & Wheeler, 1998; Baker & Phibbs, 2002). This variable is measured by
the number of the total population in a geographic area (Enpop_n) divided by
1,000,000 (the unit is 106). Market competitive intensity also influences the
likelihood of an organization investing in new technology (Gowrisankaran &
Stavins, 2004). Based on the study by Dobrev et al. (2002), market competitive
intensity is measured by the market concentration index (CR4). This is defined as
the sum of the market shares of the top four hospitals in one area having the most
outpatient volume. Environmental uncertainty also affects the likelihood of new
technology investments by an organization. Here it is defined as the standard
deviation of total high-tech medical equipment utilized by all hospitals in one
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county (or city) for five years before 2001. The greater the value of the standard
deviation, the higher the environmental uncertainty.
Organizational variables controlled for here include organizational age and size,
structure complexity, the organization’s demand for new technology, past
performance and external organizational relationship. Organizations that are older
(Zahra et al., 2000) and larger (Meyer & Goes, 1988; Pennings & Harianto, 1992)
have more resources and better learning capability, and thus have a higher
likelihood of investing in new technology. An organization’s age (Age) is the
period from its founding year to 2001; the number of its outpatients divided by
1,000,000 (the unit is 106) measures an organization’s size (Size). The more
structure complexity an organization has, the more resources it has and the higher
its likelihood of investing in new technology (Meyer & Goes, 1988). Based on
Meyer & Goes’ (1988) study, the number of high-tech medical equipment
categories (TechCategory) measures structure complexity. In addition, a
hospital’s demand for investments in new technology also raises the possibility
for new technology investments (Provan, 1987). Two variables measure a
hospital’s demand for new technology: the number of specialists (PhysicianNum)
and its accreditation status (Rank). Number of specialists refers to the number of
specialty physicians. Accreditation status (Rank) is classified in five grades: 0 is
failing or unaccredited, 1 is a district hospital, 2 is a regional hospital, 3 is a
quasi-medical center, and 4 is a medical center. This study also controls for past
performance, which may affect the likelihood an organization will adopt new
technology (O’Neill et al., 1998; Sitkin, 1992). Past performance is measured by
the utilization of high-tech medical equipment (Usage). This is the utilization sum
of the ten high-tech medical equipment categories in 2001 (the unit is 105).
Finally, the ownership of a hospital also affects investments in new technology
(Goes & Park 1997); 0 refers to public hospitals while 1 refers to private ones.
Statistical Method
The hypotheses were tested by a general linear regression model with Stata 8.1
statistical software package. An estimation model is shown below:
Y = α + β1 Knowledge Breadth + β2 Path dependence + β3 Knowledge
Breadth×Path dependence+(β4 Rank +β5 Ownership+β6 TechCategory+β7
Age+β8 Size+β9 Usage +β10 PhysicianNum +β11 CR4+β12 Enpop_n+
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β13 Uncertainty)+ε
In this model, Y presents the unrelatedness degree of new technology investments.
With the exception of knowledge breadth, path dependence and interaction term,
all other variables are control variables.
Research results
Table 1 shows the results of descriptive statistics and correlations among the
variables. The second part of this section presents the results of regression
analysis.
Table 1: The results of descriptive analysis and correlation matrix analysis
Obs Mean SD 1 3 4 5 6 7 8 9 10 11 12
1 exploration
degree 494 0.41 1.25 1.00
2 Rank 443 1.19 0.81 0.54 1.00
3 TechCategory 498 1.05 1.74 0.58 0.75 1.00
4 Age 498 24.50 21.04 0.18 0.20 0.25 1.00
5 Size (x 106) 496 0.19 0.34 0.60 0.78 0.79 0.22 1.00
6 Usage (x 105) 498 0.00 0.15 0.52 0.64 0.71 0.11 0.80 1.00
7 PhysicianNum 498 29.37 74.52 0.53 0.69 0.69 0.18 0.87 0.79 1.00
8 CR4 498 0.58 0.17 0.00 -0.02 0.01 0.04 -0.06 0.00 -0.05 1.00
9 Enpop_n (x 106) 498 1.44 0.96 0.02 0.01 0.01 -0.03 0.08 -0.01 0.07 -0.78 1.00
10 Uncertainty 494 0.00 1.00 0.07 0.09 0.00 -0.06 0.03 0.01 0.04 0.34 -0.35 1.00
11 inertia 498 0.84 0.25 -0.30 -0.31 -0.41 -0.09 -0.27 -0.14 -0.21 -0.08 0.04 0.06 1.00
12 knowledge
breadth 457 1.50 0.84 0.46 0.56 0.63 0.28 0.57 0.33 0.46 0.05 0.03 -0.04 -0.45
The Analytical Results of the Regression Analysis Model
According to our research hypotheses, we predict that knowledge breadth has a
positive effect on the degree of unrelatedness of new technology investments.
Path dependence, on the other hand, has a negative effect. Furthermore, path
dependence weakens the positive effect of the knowledge breadth on the degree
of unrelatedness of new technology investments.
Table 2 presents the influence of knowledge breadth, path dependence and the
interaction effect on the unrelatedness degree of new technology investments. In
Table 2, model 0 is the baseline model, model 1 includes only knowledge breadth,
model 2 includes only path dependence, model 3 includes knowledge breadth and
Web Journal of Chinese Management Review • Vol. 13 • No 3 11
path dependence, and model 4 includes knowledge breadth, path dependence, and
the interaction term.
Hypothesis 1 predicts that the broader the knowledge breadth is, the greater the
degree of unrelatedness of investments will be in new technologies. Model 3 in
Table 2 shows that the coefficient of knowledge breadth on degree of
unrelatedness is positive and significant (β = 0.42; p value = 0.005), strongly
supporting Hypothesis 1.
Hypothesis 2 predicts that the stronger path dependence is, the lower the degree
of unrelatedness of new technology investments. Model 3 in Table 2 shows that
the coefficient of path dependence on degree of unrelatedness is negative and
significant (β = -0.17; p value = 0.04). This result supports Hypothesis 2.
Hypothesis 3 predicts that path dependence weakens the positive effect of
knowledge breath on the degree of unrelatedness of new technology investments.
Model 4 in Table 2 indicates that the coefficient of interaction term on the degree
of relatedness is negative and significant (β = -0.21; p value = 0.04), supporting
Hypothesis 3.
In regard to control variables, the results show that accreditation status (Rank),
organizational size, utilization of high-tech medical equipment (Usage), and
environmental uncertainty all have a positive and significant effect on the degree
of unrelatedness of new technology investments.
Web Journal of Chinese Management Review • Vol. 13 • No 3 12
Table 2: General linear regression results: dependent variable = the exploration degree of new technology investments
model0 model1 model2 model3 model4
Coef. SE t Coef. SE t Coef. SE t Coef. SE t Coef. SE t
constant -0.22 0.83 -0.26 -0.48 0.91 -0.52 -0.23 0.82 -0.29 -0.46 0.90 -0.51 -0.33 0.90 -0.36
Rank 0.27 0.19 1.43 † 0.21 0.20 1.05 0.24 0.19 1.28 † 0.19 0.20 0.94 0.22 0.20 1.11
Ownership -0.15 0.25 -0.59 0.16 0.28 0.56 -0.16 0.25 -0.62 0.13 0.28 0.45 0.12 0.28 0.42
TechCategory 0.11 0.08 1.32 † 0.05 0.09 0.51 0.08 0.08 0.92 0.02 0.09 0.24 -0.01 0.09 -0.14
Age 0.00 0.00 0.22 0.00 0.00 0.75 0.00 0.00 0.48 0.00 0.00 0.89 0.00 0.00 0.67
Size (x 106) 1.04 0.53 1.95 * 0.58 0.57 1.02 1.02 0.53 1.93 * 0.61 0.57 1.09 0.60 0.56 1.06
Usage(x 105) 0.64 0.89 0.72 1.62 0.96 1.68 * 0.97 0.89 1.08 1.82 0.96 1.89 * 2.01 0.96 2.09 *
PhysicianNum 0.00 0.00 -0.20 0.00 0.00 -0.26 0.00 0.00 -0.24 0.00 0.00 -0.30 0.00 0.00 -0.32
CR4 0.18 0.92 0.20 0.19 0.99 0.19 0.15 0.91 0.17 0.14 0.98 0.15 0.04 0.98 0.04
Enpop_n (x 106) 0.05 0.17 0.28 0.05 0.19 0.25 0.07 0.17 0.41 0.07 0.18 0.36 0.06 0.18 0.34
Uncertainty 0.16 0.10 1.60 † 0.20 0.11 1.84 * 0.19 0.10 1.88 * 0.23 0.11 2.05 * 0.22 0.11 1.97 *
H1:knowledge breadth 0.46 0.16 2.91 ** 0.42 0.16 2.65 ** 0.37 0.16 2.28 **
H2:path dependence -0.20 0.09 -2.25 ** -0.17 0.09 -1.82 * -0.01 0.13 -0.05
H3:knowledge breadth
× path dependence -0.21 0.12 -1.82 *
Number of
observations 233.00 212.00 233.00 212.00 212.00
F value 10.93 ** 9.87 ** 10.57 ** 9.43 ** 9.06 **
R-squared 0.33 0.35 0.34 0.36 0.37
Adj R-squared 0.30 0.32 0.31 0.32 0.33
†<0.1,*<0.05,**<0.01, one tail test
Web Journal of Chinese Management Review • Vol. 13 • No 3 13
Discussion and Conclusions
According to Penrose (1959), knowledge characteristics are key determinants
of an organization’s decisions. This study illustrates empirically that an
organization's breadth of knowledge influences new technology investments.
Path dependence also strongly influences new technology investment decisions
(Levinthal & March, 1993), but its influence is much more complicated. Path
dependence not only has a direct effect on new technology investments, but
also plays a moderating role between organizational knowledge and new
technology investments.
This study focuses on the Taiwan hospital industry to examine the affects of
knowledge breadth, path dependence, and the interaction term between them
on new technology investments. Results of this study strongly support our
hypotheses. Below we discuss the effects of knowledge breadth, path
dependence and the effect of their interactions on new technology investments.
The last paragraph presents our contributions and limitations.
First, the results indicate that the greater the knowledge breadth, the greater the
degree of unrelatedness of investments in new technology, supporting
Hypothesis 1. This result is similar to the findings of Chang (1996), Miller
(2004), Knott (2003), and Ethiraj et al. (2005), who found that the extent of
knowledge significantly affects an organization’s strategic actions.
Organizations with greater knowledge breadth have more opportunities to
expand into new fields than do organizations with less knowledge breadth.
Organizations with broad knowledge are sensitive to new opportunities and can
quickly adjust their plans and enter new fields. The diversity of professionals
reflects the breadth of an organization’s knowledge (Chang, 1996). This study
thus uses the diversity of professionals to measure knowledge breadth and
confirms that knowledge breadth significantly raises the likelihood of unrelated
new technology investments. Results show that the knowledge heterogeneity is
a key determinant of the degree of unrelatedness of new technology.
Second, the results of this study also support Hypothesis 2; high path
dependency lowers the degree of unrelatedness of new technology investments.
Stuart & Podolny (1996), Baum et al. (2000), and Chuang & Baum (2003) all
Web Journal of Chinese Management Review • Vol. 13 • No 3 14
found that repeated routines often limit the future actions of an organization.
This study confirms this finding and shows that highly path dependent
organizations tend to follow past experience and repeatedly invest in existing
technologies.
Third, Hypothesis 3 claims that the tendency to use existing knowledge to
explore unrelated new technologies is influenced by path dependence. Our
examination into the moderating effect of path dependence supports
Hypothesis 3. Highly path dependent organizations often refer to past
experiences to make decisions and take actions (Levitt & March, 1988). These
organizations tend to exploit existing knowledge related to prior experiences
and neglect unused knowledge. Therefore organizations of high path
dependence, even with broad knowledge breadth, still exhibit a weaker
tendency to invest in unrelated technology fields.
This study makes two major contributions. First, unlike most prior studies that
merely focus on the influence of knowledge heterogeneity on technology
investment, this study combines knowledge heterogeneity with path dependent
theory to address the contingent relationship between knowledge breadth and
new technology investment. This study draws attention to path dependence as a
moderator in the relationship between knowledge breadth and new technology
investments. In general, our research suggests that the level of path dependence
determines whether knowledge breadth can effectively prompt an
organization’s expansion into unrelated new technologies.
The other contribution of this study is the measurement of new technology
investments. Most studies about technology investments examine whether
organizations invest in new technology or not in a dichotomous manner
(Mitchell, 1989; Quirmbach, 1986; Robertson et al., 1996; Robinson et al.,
2003; Rodan & Galunic, 2004). This study focuses on the relatedness of new
technology investment on a continuous scale. Our examination gives a more
complete understanding of the trajectory of technology investment strategy.
Wide knowledge breadth and low path dependence allow organizations to
invest in unrelated new technology fields. However, if the newly entered fields
are repeatedly invested in, the degree of path dependence would increase, thus
weakening the effects of knowledge breadth. Finally, such organizations would
Web Journal of Chinese Management Review • Vol. 13 • No 3 15
seek to invest in related new technology fields. The trends toward investment
in related technology fields can be interrupted by broadening knowledge
breadth and breaking out from path dependence.
This study has some limitations. First, due to a lack of longitudinal data
concerning measurement of knowledge breadth, this study adopts a
cross-sectional study that collects data at a specific point in time. Future
research might address this issue by conducting a longitudinal study to observe
long-term behavior of an organization in relation to its new technology
investments. Second, this study only examines the hospital sector. Therefore,
results might not be generalized to other industries. Future studies may choose
other industries to confirm the robustness of the hypotheses.
Web Journal of Chinese Management Review • Vol. 13 • No 3 16
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Appendix 1: List of Ten High-Tech Medical Equipment Categories
No Name of the High-tech Medical Equipment Code
1 Computerized Tomography Scanner CT
2 Nuclear Magnetic Resonance Tomography NMR
3 Radio-Isotope Agnostic Equipment RD
4 Radio-Isotope Therapeutic Equipment RT
5 Linear Accelerators HPR
6 Shock Wave Lithotripsy Equipment HS
7 Excimer Laser Angioplasty System RB
8 Implantable Cardioverter-Defibrillator HH
9 Rotational Coronary Angioplasty of Rotablator BB
10 Excimer Laser Photorefractive Keratectomy Equipment RE
Appendix 2: The partial contents of the expert survey questionnaire and average correlation score of each pair of the ten high-tech medical equipment categories based on scientific and technical principles.
1. A sample question corresponding to the correlation between ten high-tech medical
equipment categories shown in the questionnaire (example):
Please evaluate the extent of correlation between each pair of ten high-tech
medical equipment categories subject to scientific and technical principles. Please
fill in the blanks with a suitable score (1-6): 6 refers to no correlation; 5 refers to
extremely low; 4 refers to low; 3 refers to medium; 2 refers to high, and 1 refers to
extremely high:
Code
Name of the High-tech
Medical Equipment
CT RD RT HPR NMR HS RB RE HH BB
CT
Computerized Tomography
Scanner
- □ □ □ □ □ □ □ □ □
RD
Radio-Isotope Agnostic
Equipment
- - □ □ □ □ □ □ □ □
- - - - - - - - - - - -
HH
Implantable
Cardioverter-Defibrillator
- - - - - - - - - □
BB
Rotational Coronary
Angioplasty of Rotablator
- - - - - - - - - -
Web Journal of Chinese Management Review • Vol. 13 • No 3 22
2. The average correlation score for each pair within the ten categories of the
high-tech medical equipment marked by 17 specialty physicians and based on
scientific and technical principles:
CT RD RT HPR NMR HS RB RE HH BB
CT 1.00
RD 3.00 1.00
RT 3.71 1.76 1.00
HPR 3.94 3.29 2.71 1.00
NMR 3.00 3.82 4.71 4.53 1.00
HS 4.71 5.29 5.29 5.41 5.41 1.00
RB 5.47 5.47 5.47 5.29 5.59 5.41 1.00
RE 5.88 5.82 5.71 5.53 5.94 5.76 2.71 1.00
HH 5.88 5.94 5.94 5.94 5.88 5.94 5.82 5.94 1.00
BB 5.82 5.88 5.88 5.94 5.88 5.71 5.53 5.76 5.65 1.00
Appendix 3: List of the 27 Specialty Physician Categories
Family Medicine Internal Medicine Surgery
Pediatrics Obstetrics and Gynecology Orthopedics
Neurology Neurosurgery Urology
Otolaryngology Ophthalmology Dermatology
Psychiatry Physical Medicine and
Rehabilitation
Plastic Surgery
Anesthesiology Diagnostic Radiology Radiology and Oncology
Anatomical Pathology Clinical Pathology Nuclear Medicine
Oral Maxillofacial
Surgery
Oral Pathology Emergency Medicine
General Medicine Chinese Medicine Dentistry