Knowledge Breadth, Path Dependence and New Technology Investment

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


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


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.


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


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.


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


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


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


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＋


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


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.


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


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


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


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.


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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

- - - - - - - - - -


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

Knowledge Breadth, Path Dependence and New Technology Investment

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