DP2013-15

Heterogeneous C om binationsof Knowledge Elements:
How the Knowled ge Ba se Structure
Im pacts Knowledge-related
Outc om es of a Firm*
Yoichi MATSUMOTO
April 8, 2013

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Heterogeneous Combinations of Knowledge Elements:
How the Knowledge Base Structure Impacts Knowledge-related Outcomes of a Firm 1
Yoichi Matsumoto
Abstract
Knowledge is the preeminent resource of a firm. Although many scholars have focusedon the firm’s knowledge base, few studies have examined the effects of the knowledge
base structure—how knowledge elements are linked or separated from each other inclusters—on firm’s knowledge-related outcomes. This study examines the knowledge
base structure, and tests hypotheses about the effects of heterogeneous combinations ofknowledge elements on the outcomes. Through an analysis of the patents related to LCD
technology, (1) the usefulness of an organization’s inventions correlates positively withthe density of the knowledge links between technologically different knowledge
components, (2) the average usefulness of a firm’s inventions correlates positively withthe density of the knowledge links between technologically disparate knowledge
components, (3) the number of inventions correlates negatively with the density of theknowledge links between excessively disparate knowledge components.

1 This work was supported by JSPS KAKENHI Grant Number 22330123.

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1.

Introduction

For a company to gain competitive advantage, it must differentiate itself from its
competitors. To this end, considerable research has been conducted, including examining
difference from a resource-based view of the firm and, in more recent studies, employing
a knowledge-based view of the firm (Grant 1996; Conner and Prahalad 1996). To date,various researchers have investigated how the characteristics of accumulated knowledge
within organizations modulate the performance of knowledge creation activities oforganizations, considering the effects of related dimensions and outcomes (Yayavaramand Ahuja 2008; Ahuja and Katila 2001).
Many scholars have focused on the quantitative aspect of the knowledge base.
For example, the size of the knowledge base of the organization has positive effects on
its outcomes (Henderson and Cockburn 1996; Pakes and Griliches 1984). Furthermore,
the size of the knowledge base is also related to the success of mergers and acquisitions.
When a company is acquired, there is a positive relationship between the size of theacquirer’s knowledge base and the acquired company’s subsequent output of innovation(Ahuja and Katila 2001). If an organization has a broad knowledge base, it also has
greater ability to explore external knowledge (Brusoni et al. 2001; March 1991).

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The knowledge base’s qualitative aspect as well as its quantitative aspect has
drawn the attention of researchers. For instance, recombination or refinement offamiliar combinations of knowledge elements increases an invention’s usefulness(Fleming 2001). The extent of a firm’s absorption of technological capabilities from its
alliance partners is positively related to its pre-alliance level of technological overlap
with partner firms (Mowery et al. 1996). Lane and Lubatkin (1998) proposed thatelements of a knowledge base can be classified into two categories: basic knowledge andspecialized knowledge. The former refers to a general understanding of a discipline’s
underlying traditions and techniques. The latter includes all the remaining elements.
According to these authors, in the case of a biotechnology company, the basic knowledge
comprises things relevant to biochemistry, whereas the specialized knowledge is anyother knowledge. The relevance of a student’s basic knowledge of the firm to a teacher’s
knowledge base of the firm is positively associated with inter-organizational learning(Lane and Lubatkin 1998).
Most studies regarded the knowledge base of an organization as anaggregation of “knowledge components” (Grant 1996). Ahuja and Katila (2001)
operationalized a company’s knowledge base as the aggregation of patents earned and3

patents cited by a company. Patents are new intrinsic knowledge components, and theybring together past learning represented by patents cited to form an organization’s
knowledge base. Therefore, they are recognized as an aggregation of separate knowledge
components (in the form of previous patents) that comprise the knowledge base. Fleming
and Sorenson (2001) also argued that a knowledge base is an aggregation of knowledge
components. Diversification of knowledge components increases the uncertainty
surrounding the optimal combination of various components. Taylor and Greve (2006),
who cited this assertion, considered the impact of diversity of an individual’s and group’s
knowledge base on the creativity of the individual and group writing comic books.
In contrast to the research described above, a groundbreaking study by
Yayavaram and Ahuja (2008) addressed the structural aspect of the knowledge base.
The knowledge base structure links different knowledge elements or separates them inclusters. The point Yayavaram and Ahuja (2008) make is that even for organizations
with knowledge bases composed of identical knowledge components, if the combinationof the components is different, the results differ for knowledge bases. A study on theworldwide semiconductor industry revealed inter-organizational and inter-temporal
differences in the organization knowledge structure, and proved that the level of the4knowledge structure decomposability affects knowledge-related outcomes of theorganization. More specifically, there exists an appropriate level of density of the total
connections between the components of a knowledge base, and too high or too low a total
density of the connections can negatively affect knowledge-related outcomes (Yayavaramand Ahuja 2008).
Various studies have focused on the quantitative and qualitative aspects of theknowledge base regarded as an aggregation of knowledge components. In contrast,
Yayavaram and Ahuja (2008) analyzed the knowledge base structure. Because this wasan epochal study, it warrants further research. In contrast to research on knowledge
components, the qualitative aspects of knowledge structure is promising, and thus
address the latter issue.
This paper is organized as follows. Section 2 poses hypotheses. In general,
invention is a process of recombination of new or existing elements. Knowledge
components of a firm’s knowledge base are heterogeneous. Integration of knowledge
components for invention can be more difficult when the components to be integrated
are strongly heterogeneous. This study takes into account the effect of the heterogeneityof knowledge elements on the firm’s knowledge-related outcomes. Section 3 describes the5technological fields analyzed and the data retrieval method. Section 4 states theempirical results of the data analysis. Section 5 discusses the results, and concludes it.

2.

Hypotheses

Innovation, an important determining factor in the continued maintenance of acorporation’s competiveness, is the recombination of existing concepts, physicalmaterials, and power, not simply a new combination of existing components oftechnology to invent new technology. Other forms of innovation include a new
combination of technology and markets and the innovation of organizations (Nelson andWinter 1982; Schumpeter 1934). To simplify the discussion, we focus on the invention ofa new technology.
Decomposability of Knowledge Structure and Outcomes
One representative view in the history of technology conceptualizes invention as aprocess of recombination (Fleming and Sorenson 2004; Fleming 2001). According to thisperspective, the inventor is searching for a new combination of technology components(Weitzman 1996; Basalla 1988) and reconfiguring existing combinations (Henderson and
Clark 1990). Here we refer to knowledge and basic bits of matter as technology

6components that the inventor uses during the invention process (Fleming and Sorenson2004). The primary function of research and development (R&D) is to combine differing
technology components to create new knowledge (Fleming 2001; Kogut and Zander
1992).
In order to invent, an organization must create and recombine technology
components. When the variety of an organization’s knowledge elements in its knowledge
base increases, the number of potential recombinations also increases. The essence oforganizational capability is the integration of specialized knowledge components (Grant
1996). An organization with a knowledge base comprising diverse knowledge
components can invent things that yield greater value because of the greater number ofpossible combinations. Many researchers have analyzed the relationship between an
organization’s knowledge-related outcomes and the number of knowledge components(Ahuja and Katila 2001; Fleming 2001; Lane and Lubatkin 1998; Mowery et al. 1996).
Although most researchers have focused on the components that constituteknowledge bases, Yayavaram and Ahuja (2008) examined the impact of the knowledge
base structure on knowledge-related outcomes. Their arguments are as follows.
The knowledge base structure links different knowledge elements or separates

7them in clusters. For example, consider the case of semiconductors. Viewing
semiconductors as a combination of a material (silicon) and a given architecture (CMOS)
is obvious, and the material and architecture for use can be decided separately. With two
knowledge components, a person deciding on the solution to a problem either considers
the two knowledge components as a combined single unit of knowledge or eachknowledge element independently. Determining whether it is most appropriate toconsider two knowledge components simultaneously involves an organization’s
reasoning process. The choice to consider two components simultaneously (link them inthe knowledge base) does not depend on whether there is always a strong
interdependence of technologies; rather, choosing whether to link both components
together as a

chunk depends on the organization’s human decision-making

characteristics (Simon 1996).
Organizational variations in coupling patterns between knowledge elements isreflected in a spectrum of the knowledge base structures, from non-decomposable
through nearly decomposable to fully decomposable. The non-decomposable (integrated)structure is one with no emerging knowledge clusters, as the couplings are pervasively
distributed. The nearly decomposable structure means that the knowledge clusters are8discernible but connected through cross-cluster couplings. The fully decomposable
structure refers to the knowledge base composed of distinct clusters of coupledknowledge elements with no significant link between clusters.
An organization can achieve significant outcomes by successfully combining
deep knowledge in a particular field with broad knowledge (Macher and Boerner 2006;
Katila and Ahuja 2002). Nearly decomposable knowledge bases permit both breadth anddepth of search, and provide the benefits of exploration both within a cluster and acrossclusters. Therefore, the nearly decomposable knowledge structure provides the highest
outcome of these three levels (Yayavaram and Ahuja 2008).
Heterogeneous Combinations of Knowledge Components and Outcomes
Past research about firms’ knowledge bases can be classified into two categories. First,
there are the studies on the variation of the knowledge components comprising theknowledge base. Second, many researchers have addressed component quality. The
latter line of research has concentrated on the relatedness or similarity of knowledge
elements with those of other organizations. Although the study by Yayavaram and Ahuja(2008) was groundbreaking in that they focused on the knowledge base structure, they
had certain commonalities with the former line in that they did not consider the9heterogeneity of the knowledge base elements. They operationalized the knowledge basestructure by the density of links, which depends on the number of connections between
knowledge components but regarded each component as faceless.
However, heterogeneity of the organization elements can affect its outcomes. For
example, suppose that several organizations exist, one composed of Americans andCanadians and another composed of Taiwanese and Chinese, each obviously with lessdiversity than an organization composed of American, Canadian, Taiwanese, andChinese. The first two organizations are more homogeneous than the third because
members of the first two organizations have similar cultural backgrounds. It is safe toassume that harmonizing the members of the first two is much easier than the third,
but the third organization’s cultural diversity may make it more innovative than thefirst two.
Similarly, a knowledge base contains disparate knowledge components to achieve
higher innovation potential.
An individual’s learning is greatest when the new knowledge to be assimilated isrelated to the individual’s existing knowledge structure (Lane and Lubatkin 1998;
Bower and Hilgard 1981). Individuals who have the same basic knowledge can

10communicate and understand each other relatively easily. To understand new
knowledge, individuals or firms must possess some amount of basic prior knowledge(Lane and Lubatkin 1998). Mastering the basic knowledge for an area facilitates
understanding of the relevant more advanced knowledge components. Generally
speaking, the wider the scope of knowledge being integrated (and thus the greater thediversity of individuals involved), the lower is the level of common knowledge, and themore inefficient the communication and integration of knowledge (Grant 1996). Whenconsidering the possibility for recombining knowledge components, if they are closely
related technologies, it can be easier to evaluate the anticipated value of combining
them, and there will be fewer obstacles to integrate them.
A firm’s primary role, and the essence of organizational capability, is the integrationof knowledge (Grant 1996); thus, the knowledge base’s wealth of diversity leads toinvention. However, diversity can also inhibit component integration for invention. In
this complicated context, an invention borne out of the integration of highly diversified
knowledge components enables firms to differentiate themselves from their competitors.
On the one hand, in order to put the pieces together, if technologically different
components are considered as a chunk, firms can make useful inventions. However, if

11the difference among components in a chunk is excessive, although the value of an
invention increases, the number of inventions decreases. The foregoing discussion
implies the following hypotheses.

Hypothesis 1a: The number of inventions is positively related to the density ofknowledge links between different knowledge components.
Hypothesis 1b: The number of inventions is negatively related to the density ofknowledge links between excessively different components.
Hypothesis 2a: The average usefulness of inventions is positively related to the densityof knowledge links between different knowledge components.
Hypothesis 2b: The average usefulness of inventions is more strongly positively related
to the density of knowledge links between excessively different knowledge components
than to the density between moderately different components.

Given that the knowledge-related outcome of a firm is dependent on the quality andquantity of its inventions, the outcome will be higher when the knowledge basestructure exhibits dense links among different components. However, when the degree12

of the difference between components to be linked is excessive, although the quality of
the inventions will increase, the number of inventions will decrease. In that situation,
the relationship between the density of the links between disparate components and thefirm’s knowledge-related outcome is unclear.

Hypothesis 3: The knowledge-related outcome of an organization is positively related tothe density of links between different components, but when the difference between
components to be linked is excessive, one cannot predict the explicit relationshipbetween the density of links and the knowledge-related outcome.

3.

MethodEmpirical analysis focuses on the global liquid crystal display (LCD) industry for two
reasons. The first reason is that LCDs have had various applications since the 1970s,
such as their use in calculators, watches, laptop monitors, personal computer monitors,
and televisions. The variety of applications continues to increase; thus, technology
development in LCDs plays an important role in determining a company’scompetitiveness. The second reason is that manufacturing the LCD components requires13

a high level of technology, such as liquid crystal materials, thin film transistors (TFTs),
drivers for LCDs, polarizers, color filters, glass, and backlights. Although LCD is asingle product, it requires various types of technological elements, making it appropriate
for testing the hypotheses of this study.
The data used in the empirical analysis are U. S. patents. While this study
uses patent data filed since 1976, as described below, the structural indicator forknowledge bases for year t are composed of the sum of the prior three years (t-3 to t-1);
thus, the outcomes analyzed are from 1979 onwards. In addition, considering thenecessary time lag between patent filing and registration, patent filings up to the year
2002 were analyzed. There are various limitations in the information on patent citations(Jaffe and Trajtenberg 2002; Griliches 1990). For example, depending on the industry
and products involved, the appropriability of innovations based on patents gained varies(Levin et al. 1987), and differences also exist in the patentability of technology and theratio of citations (Hall et al. 2001). Because the following analysis is limited to specifictechnologies, these factors should not present a serious issue.
We extracted all patents registered with the USPTO for technology related to
LCDs (search date July 13, 2011) using the Thomson Innovation database (Thomson

14Reuters). A sample of how this technology category was extracted is the category use
identified as “LCD technology-related” patents by the Derwent World Patent Index. The
extraction retrieved 8,770 patents and from these, 2,419 patents were used for the
analysis. The information provided in the database for each patent includes the name of
the company (and the inventor’s name), filing date, registration date, the literature citedin the patent (patents, scientific literature), technical classification of the patent (U.S.
classification, international classification), and other items. We used the international
patent classification (IPC) to construct our key variables. In the rapidly evolving field of
LCD technology, the acquisition of patents is extremely important for competitiveness(Spencer 2003); therefore, companies that have not acquired patents are not consideredcentral players in the industry and are excluded from the analysis. In addition, thisstudy limits its focus to manufacturers of LCDs, companies that have manufactured
LCDs in the past, or companies that have established a subsidiary for the purpose of
manufacturing LCDs; other companies, such as those specializing in technology
development and acquiring patents for purposes thought to be different are excluded.
We based this distinction on newspapers, magazines, and company annual reports.
Variables

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Dependent variables. Three dependent variables are used in this study. The number of
inventions in the year t by the firm j is measured by the number of patents. Patentcitations are the key indicators used to measure the quality of the patents (Yayavaramand Ahuja 2008; Hall et al. 2001). If a patent has been cited by other patents manytimes, it indicates that the patent resulted in a change in technology and has had asignificant impact on the society (Jaffe and Trajtenberg 2002). Here, as in many
previous studies, the number of patent citations was used as a variable to represent theusefulness of an invention. The knowledge-related outcome of the firm j in the year t ismeasured by the number of times the patents has been cited by other patents insubsequent years. The average usefulness of the inventions in the year t by the firm j ismeasured by the citations’ median.

Independent variables. In this study, a firm’s knowledge base or patentportfolio at t is assumed to comprise all patents that the firm has accumulated from t-3
to t-1 years (Yayavaram and Ahuja 2008). For example, if the sample contains theknowledge creation outcomes of the firm j in the year t, this means that the firm j filed
for one or more of the patents under consideration each year for four consecutive years(t-3 to t). There are repeated entries of new firms, exits of existing firms, and M&A in

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the LCD business. This study analyzed only companies performing stable R&D.
As an indication mark for later retrieval, each patent is given a unified
internationalclassificationcode

indicatingtherelatedtechnology

field,

notclassifications peculiar to individual countries. For international classifications, thehierarchical order is section, class, subclass, main group, and subgroup. This study uses
the international classifications published in the January 2006 edition. Subclass, whichis in the middle of the hierarchy, is used as the unit of analysis—knowledge components.
A patent is not limited to one classification alone; if a patent covers multiple
fields of technology, it is allotted classification codes for each field. If two knowledge
components are extracted for an organization, i.e., if an organization has beenrecognized to have combined two components, the organization must have produced aninvention by combining the two components. Thus, it is possible to observe from theirpatent portfolio whether the company’s knowledge base contains two knowledge
components that have been combined (Yayavaram and Ahuja 2008). The technology
classification of a patent can be determined from the past accumulated technology andby comparing the citations of past related patents by the inventor; the bias can beconsidered small (Yayavaram and Ahuja 2008). When a patent is given multiple

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subclasses, there exists a connection between each knowledge component, and thus theknowledge base technology classifications that are connected filings are considered to beknowledge links.
To derive the knowledge links, we used the association analysis data mining
method. An application of association analysis is the retail point-of-sale data, which isused to extract information about products that tend to be bought at the same time. An
association rule refers to the situation in which event B occurs whenever event A occurs.
The greater the frequency of both event A and event B occurring together, the stronger
the association rule. Three representative indicators are used to evaluate the association
rule. The first is confidence, indicating the probability that B will occur when A occurs in
the entire data set. The second is support, the probability of A occurring in the entire
data set and the product of confidence. The third is lift, obtained by dividing theconfidence by the probability of event B occurring, and representing the improvementthat can be made in the predictability of event B occurring when it is examined in
combination with the occurrence of event A compared with the independent occurrenceof event B. In this study, the algorithm used to perform the analysis “apriori” (Agrawal
et al. 1993) employs the R statistical environment data analysis package “arules”

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(Hahsler et al. 2005), and derives the two knowledge components’ association rules in
the knowledge base of the firm j in the year t. Knowledge links for which the connection
is extremely weak are considered exceptional cases; in order to exclude them, we employ
commonly used statistical analysis metrics for determining significant levels: confidenceof ≥0.01, level of support of ≥0.01, and rate of improvement of ≥1.
On the basis of the foregoing procedure, Figure 1 and Figure 2 display theorganizational knowledge base structure in the year 2000 for Sharp and Samsung
Electronics, respectively. The filings of the two companies between 1997 and 1999
appear on the vertical axis and horizontal axis, respectively. Among the subclassclassification included in the technology categorization of the registered patents, thenumber of incidences was found to be at least 1% of the total number of the entire
knowledge base for each company. The vertical and horizontal axes are the same
subclass, and the diagonal line falling from the top left to the bottom right represents
the symmetry of the matrix.
Our measure of revealed knowledge structure contains several characteristics.
First, we measured knowledge structure directly at the firm level, not as an aggregationof individual knowledge structures. Second, precisely defined measure enables us to19

observe knowledge structures both cross-sectionally and intertemporally. These
characteristics are the same as the Yayavaram and Ahuja measure (2008). Third, we canclassify the extent of technological difference between knowledge components into threelevels: intra-class, inter-class, and inter-section. From the technological standpoint of
the patent classification, subclasses categorized in same class are closer than thosecategorized in different classes, and are much closer than those categorized in different
sections. In Figures 1 and 2, subclasses in the same class appear in dashed boxes, andthose in the same section appear in solid boxes.
Insert Figure 1 and Figure 2 here
The difference between Sharp and Samsung Electronics is that Sharp has
more knowledge components than Samsung Electronics. Among these components is thetechnology related to section B “performing operations; transporting,” B23K, B26F,
B32B, B65D. These technologies are related to transporting large panels. The two
figures suggest that Sharp was more enthusiastic than Samsung Electronics about
inventions related to the production of large LCD panels. Further, the intersection of
G02F with H01J has no knowledge link for Sharp, whereas Samsung Electronics has
knowledge links at this intersection. Although these organizations conduct technology

20

development at the same time in the same field, they exhibit differences in theirknowledge links.
From the sum of squares in the figures, a diagonal line from upper left tobottom right is drawn through the number of squares (the number of the subclass types)
dividing them into two, and we get the number of potential combinations of subclasses.
On the basis of the metrics (evaluations criteria) of the association rules, pairs that
occur beyond the defined frequency of occurrence (the shaded ones) are knowledge links.
Independent variables are the ratio of the number of inter-class links to that of all links(interclass links ratio), and the ratio of the number of inter-section links to that of alllinks (intersection links ratio).

Control Variables. On the basis of the network theory, Yayavaram and Ahuja(2008) analyzed the effects of the knowledge base structure on the organization’s
knowledge-related outcome. They applied the concept of density to operationalize theknowledge base variable level of decomposability. Density refers to the proportion oflinks actually present in a network (Prell 2012). It is necessary to control this input
measure. Decomposability of the knowledge base is measured as the rate of knowledge
links within the number of potential combinations of two subclasses (density of links).

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The number of citations may be different for each technology field. There may
be more citations for technology in fields in which it is easy to patent technology or infields on which attention is focused during the period under consideration. Therefore, itis necessary to consider the ease of citation for each technology field (Yayavaram andAhuja 2008; Fleming and Sorenson 2001). Specifically, first, all the patents related to asingle field, LCDs, are considered. Second, the averages for the year and class of thetotal number of citations are calculated. Third, all patent filing classes for the firm j in
the year t are extracted, and the sum of the product of the average number of citations
and occurrences for each class is calculated. Differences in trends in relation to thenumber of citations for each technology field are controlled (technology control). Because
subclasses belonging to different classes are viewed as disparate elements in this study,
technology control is calculated on a class basis.
In relation to an organization’s technology development, the size of theknowledge base will vary—the greater the development, the larger the base. When there
are greater technology development activities, more knowledge resources are invested intechnology development, and there is a positive relationship between the magnitude ofthis investment and the outcomes, indicating that the size of the knowledge base (Ahuja

22and Katila 2001) based on a three-year patent portfolio.
If a significant amount of funds is invested in technology development, moreresources can be devoted to the process of search and discovery, and better outcomes canbe anticipated (Henderson and Cockburn 1996). Thus, controlling the differences inhuman resources and funds invested in the development of LCDs is necessary. However,many LCD manufacturers are diversified corporations, clouding the determination of
the percentage of overall R&D spending devoted to LCDs. In addition, variation among
the definitions of the LCD-related business units among these corporations makes
comparing revenues across corporations difficult. Therefore, this study controls fordifferences in resources invested in R&D, using the number of inventors involved inrelated inventions. Specifically, patents for the firm j in the year t list the inventors, andusing the full names of the inventors allows any duplication to be eliminated.
Control variables are also added to evaluate the number of classes in aknowledge base because the diversity of knowledge components in the knowledge base
can have a positive effect on the knowledge-related outcome of the organization.
Statistical Analysis
The dependent variable is count data. Because the variance is approximately equal to23the mean, Poisson regression analysis was performed. The result of the random effectmodel has been displayed in Table 2; the results were not significantly different,
estimated by the fixed effect model.
Insert Table 1 here

4.

Results

Table 2 presents the results of hypothesis testing for three types of dependent variables.
Model 1, Model 4, and Model 7 display the results for the control variables. In Model 2,
the interclass link ratio was inserted into Model 1. Thus, hypothesis 1a is not supported.
In Model 3, the intersection link ratio is inserted into Model 1. The increase in the ratio
of intersection links negatively influences the number of patents; thus, supporting
hypothesis 1b. Next, the interclass link ratio in Model 4 was added to Model 5. Theinterclass link ratio has a positive influence on the median of citations; thus, supporting
hypothesis 2a. The intersection link ratio in Model 4 was added to Model 6. Theintersection link ratio positively influences the median of citations; thus, supporting
hypothesis 2b. Further, the interclass link ratio in Model 7 was added to Model 8 and to
Model 9. On one hand, the ratio of interclass links positively affects the number of24citations, and the other, the ratio of intersection links positively affects the number ofcitations too. Consequently, hypothesis 3 is not supported.
Insert Table 2 here

5.

Summary and Conclusion

New combination is the source of novelty. An invention can be defined as a new
combination of components or a new relationship between previously combined
technology components. Knowledge is a firm’s critical resource, and organizational
capability involves the integration of multiple knowledge components (Grant 1996).
Therefore, an organization with a highly-diversified knowledge base can enhance its
knowledge-related outcomes. Many researchers have focused on firms’ knowledge base,particularly its elements. Yayavaram and Ahuja (2008) were the first to examine thestructure by which different knowledge elements are linked or separated from eachother in clusters. The present study examines the knowledge base structure considering
the elements’ heterogeneity.
Through an analysis of the patents related to LCD technology, we establishedthree major findings. First, the usefulness of an organization’s inventions correlates25

positively to the density of the knowledge links between technologically different
knowledge components. Second, the average usefulness of a firm’s inventions correlates
positively to the density of the knowledge links between technologically disparate
knowledge components. Third, however, the number of inventions correlates negatively
to the density of the knowledge links between excessively disparate knowledge
components.
These findings suggest a need for firms to choose a knowledge base creation
strategy on the basis of industry or business domain. In industries such as
pharmaceutical or bio-chemical, firms need little focus on the quantity of inventions
because pharmaceutical firms can earn large profits from a single blockbuster patentand so should concentrate on improving the invention’s quality. Therefore, incorporating
many disparate elements into the knowledge base and connecting them is beneficial.
However, in other industries, such as electronics and automobile, a single patent does
not always strengthen the firm’s competitiveness. In these industries, firms need a highquantity of inventions as well as high quality inventions. If improving an invention’squality decreases the quantity of patents, it harms firms in these industries. Thus, eventhough they incorporate disparate elements into the knowledge base, such firms should26carefully examine how many disparate elements are added in the knowledge base andhow densely they are linked.
Although the results of the statistical analysis are clear, we must interpretthem carefully. This study focuses on LCD technology. LCDs comprise a complex
combination of various elements. Deep knowledge about each of the technologies thatcomprise the product and the balance between this knowledge and the knowledge of how
to combine them may have special importance for this technology. In contrast, forexample, focusing on the combination of parts of a personal computer may lead todifferent results, and a comparative study involving such a product is clearly one of thisstudy’s limitations that has not yet been resolved. In addition, a difference may exist
between industries regarding the level of knowledge link density and the level ofknowledge component disparity at which the number of inventions decreases. Theselevels may affect the results of the test about hypothesis 3. Comparisons between
industries and technologies are the challenges left for future research.
In this study, a degree of bias may exist as a result of the characteristics of thedataset. Here the subject of analysis was LCD manufacturers. These companies have atleast the minimal knowledge required for LCD manufacture or related technology

27

development. Companies that have not reached the stage of LCD production may nothave the minimal knowledge required, and an analysis of such companies may produce
different results. However, the presence of such a bias can be regarded as inevitable
when using patent data sources. Patents are information about inventions resulting
from successful technology development. To deal with this type of bias, a study must use
other sources of data to complement the patent data in order to verify the results.
Most previous studies suggest that an organization’s knowledge base is acollection of components. This study focused on the knowledge base structure andaddressed the elements’ heterogeneity and its implications for the firm’s knowledgerelated outcomes. Further research with this perspective may lead to additional
productive results.

28

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Figure 1: The Knowledge Base Structure (Sharp, 2000)

Figure 2: The Knowledge Base Structure (Samsung Electronics, 2000)
33

Table 1: Descriptive Statistics and Correlations
Variable

Obs

Mean

S. D.

Min

Max

1

2

3

4

5

1.

# Citations

200

139.98137.636

0

910

1

2.

M Citations

200

14.12514.134

0

101.5

0.103

1

3.

# Patents

200

9.775

9.681

1

61

0.650

−0.2861

4.

Interclass link ratio

199

0.911

0.086

0.5

1

0.137

0.098

0.082

1

5.

Intersection link ratio

199

0.543

0.173

0

1

0.093

0.036

0.162

0.209

1

6

7

8

9

6.

Density of links

200

0.318

0.168

0

1

−0.2550.120−0.354−0.073−0.1901

7.

Technology control

200

290.147

261.3245

1639.828

0.883−0.061

0.717

0.139

0.066

−0.2391

8.

Knowledge base size

200

167.94156.508

12

852

0.473

−0.272

0.779

0.080

0.210

−0.435

0.476

1

9.

# Inventors

200

22.03

22.301

1

172

0.560

−0.264

0.849

0.046

0.161

−0.325

0.592

0.691

1

10. # Classes

200

7.69

3.577

2

20

0.325

−0.275

0.657

0.063

0.208

−0.575

0.339

0.805

0.575

34

10

1

Table 2: Results of Poisson Regression Analysis
Dependent variable = # Patents
Model 1
Interclass link ratio

Model 2Model 4Model 7

Model 5Model 6

Model 80.584**

0.957***(0.746)(0.034)(0.000)Model 9−0.389**0.647***

0.130**(0.043)(0.000)(0.010)−0.913***−0.864***−0.899***0.361**

0.307*

0.410**−0.558***−0.555***−0.530***(0.000)(0.001)(0.001)(0.034)(0.076)(0.019)(0.000)(0.000)(0.000)0.001***

0.001***

0.001***

0.001***

0.001***

0.001***(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)Technology control
Knowledge base size

Dependent variable = # Citations

0.126

Intersection link ratio
Density of links

Model 3

Dependent variable = M Citations

0.001***

0.001***

0.001***−0.000−0.000−0.001*

0.000

0.000

−6.23e−07(0.002)(0.002)(0.001)(0.235)(0.220)(0.092)(0.485)(0.383)(0.993)0.017***

0.017***

0.017***−0.021***−0.021***−0.022***0.005***

0.004***

0.004***(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)

0.008

0.009

0.011

−0.035***−0.037***−0.027*−0.024***−0.024***−0.022***(0.482)(0.441)(0.377)(0.008)(0.006)(0.050)(0.000)(0.000)(0.000)# Observations

200

199

199

200

199

199

200

199

199

# Groups

23

23

23

23

23

23

23

23

23

Log likelihood−540.763−538.402−536.416−1035.154−1030.181−1019.403−3676.376−3631.381−3669.699# Inventors# Classes

Note: *p<0.1; **p < 0.05; ***p < 0.01. All models include year dummies.

35