Recombinant Uncertainty inTechnological Search
Lee Fleming
Harvard University, Graduate School of Business, Morgan Hall T97,
Boston, Massachusetts 02163
lﬂeming@hbs.edu

hile the course of technological change is widely accepted to be highly uncertain
and unpredictable, little work has identiﬁed or studied the ultimate sources andcauses of that uncertainty. This paper proposes that purely technological uncertainty derives
from inventors’ search processes with unfamiliar components and component combinations.
Experimentation with new components and new combinations leads to less useful inventions
on average, but it also implies an increase in the variability that can result in both failure
and breakthrough. Negative binomial count and dispersion models with patent citation data
demonstrate that new combinations are indeed more variable. In contrast to predictions,however, the reuse of components has a nonmonotonic and eventually positive effect onvariability.
(Invention; Search; Recombination; Negative Binomial Dispersion Model)

W

Introduction
While many have acknowledged the pervasive
uncertainty of technological change (Rosenberg 1996),
the ultimate sources of that uncertainty remain poorly
understood. Many scholars in the product life-cycle
tradition have observed that uncertainty peaks earlyand decreases following convergence on a dominant
design (Anderson and Tushman 1990, Klepper 1997).
Radical and destabilizing change and the sources ofthe life cycle are particularly difﬁcult to predict andoften attributed to luck or individual genius (Tushman
and Anderson 1986, Ayres 1988, Mokyr 1990). Clark(1985) proposes that technologies evolve and uncertainty decreases as inventors iterate between the needsof customers and the technological logic of their current trajectory. Much research remains agnostic about
the causal sources of uncertainty and simply models
it as stochastic draws (Nelson and Winter 1982, Klepper 1996). Proponents of bounded rationality often
characterize technological change as an intrinsicallyuncertain and, to varying degrees, blind search process (Nelson and Winter 1982, Vincente 1990, March0025-1909/01/4701/0117$5.00
1526-5501 electronic ISSN

1991). While the assumptions of bounded rationality may be more accurate than those of classical economics, the metaphor of search remains informal andwas only recently developed empirically (Stuart andPodolny 1996). Despite this widespread acknowledgment of the importance of uncertainty, most research
gives the topic brief consideration en route to otherissues, and little work has attempted to identify andempirically validate the causal sources of uncertainty.
Part of the difﬁculty in explaining the ultimate
source of uncertainty is the conceptual and empirical conﬂation of its many sources, and, in particular,
technological invention and commercial innovation.
There exist many causes of uncertainty in technological change besides purely technological sources,
including the adoption and diffusion of new technologies, market and customer acceptance, and competitors’ strategic actions (Rosenberg 1996). Arguments forintentional conﬂation of these sources notwithstanding (Ruttan 1959), our confusion could be reduced
by returning to the classic differentiation between
technological invention and commercial innovation.
Schumpeter (1939) deﬁned innovation as the comManagement Science © 2001 INFORMS
Vol. 47, No. 1, January 2001 pp. 117–132

FLEMING
Recombinant Uncertainty in Technological Searchmercial application or adoption of an invention.
He argued that, “the making of the invention andthe carrying out of the corresponding innovation are,economically and sociologically, two entirely different things” (p. 85). While these sources remain interdependent, their conﬂation confuses our explanationsof their respective processes and contributions to
uncertainty. To avoid such confusion, this paper will
focus on invention and purely technological sourcesof uncertainty.
Attempts to understand the source of technologicaluncertainty have also been frustrated by a lack ofunderstanding of the process by which inventors
create new technologies. A theory of the processof invention would facilitate our understanding of
the sources of technological uncertainty. To address
this issue, this paper synthesizes two classic perspectives on the sources of technological novelty: ﬁrst,
invention is a process of recombination, and, second,
invention is indeed an inherently uncertain and, therefore, typically local search process.

Invention As a Process ofRecombinant Search
Many scholars have proposed that recombination provides the ultimate source of novelty (Gilﬁllan 1935,
Usher 1954). Schumpeter (1939, p. 88) observed that
“innovation combines components in a new way,or that it consists in carrying out New Combinations.” Nelson and Winter (1982, p. 130) state that“the creation of any sort of novelty in art, science,or practical life—consists to a substantial extent ofa recombination of conceptual and physical materials that were previously in existence.” Henderson and
Clark (1990) argue that the mere rearrangement ofpreviously used components can itself cause destabilizing industrial change. Hargadon and Sutton (1997)
describe how a design consulting ﬁrm creates novelty by brokering ideas and technologies between theirclients’ industries.1 Basalla (1988) develops an analogy
1
While the concept of recombinant search as the source of novelty
may well apply outside the strictly focused realm of invention—indeed, to cuisine, innovation (Abernathy and Utterback 1978,
Kogut and Zander 1992, Levinthal 1998), and process improvement(Romer 1993)—I can only comment empirically on invention.

118

from Kroeber’s (1948) image of a tree of cultural
artifacts. Unlike genetic trees, however, the branchesof the tree of technology can fuse together. “Separate
types or branches fuse together to produce new types,
which merge once again with still other branches”(Kroeber 1948, p. 138). Similar to the theory of natural
evolution before the discovery of DNA, the analogy
breaks down at the genetic level. “We who postulate
theories of techological evolution likewise have ourDarwins but not our Mendels” (Kroeber 1948, p. 210).
Although the idea of recombination as the source ofnovelty has been widely discussed, the implications of
the idea remain undeveloped. Previous work suggests,
however (Schumpeter 1939, Henderson and Clark
1990), that an invention can be deﬁned as either a
new combination of components or a new relationshipbetween previously combined components. Whileknowledge, science, algorithms, culture, applications,and manufacturing processes also inﬂuence invention,
they are not part of an actual artifact and are notactually instantiated in an invention. They strongly
inﬂuence the process of inventive search, however,by inspiring, aiding, explaining, or constraining theuse of particular components or combinations. Colloquial usage of the word “components” implies known,
available, and commercially available hardware. In
this paper, however, “components” will denote theconstituents of invention, along the lines of what
Schumpeter calls “factors” (1939, p. 88).
The Scope of Potential Recombination
Even though the number of potential components
overwhelms the imagination, there are no restrictions
on the scope of their recombination. Components are
not like genes and “similar” technologies are notlike species (Basalla 1988). In contrast to variation
processes within genetically isolated populations,inventors can recombine any components within theirpurview. Perceptions that certain technologies or components “belong together” develop through social
construction and previous association. For example,
if an electrical engineer of the 1940s had been asked
about his profession’s use of sand and aluminum,
he probably would have replied with a blank stare.
Today, he or she probably would reply that they are
the most common basic materials of semiconductors

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Recombinant Uncertainty in Technological Searchand the focus of much research investment. Clearly, no
technology evolves independently of the entire worldof made things. At any point in technological evolution, any component is at risk of being recombinedwith any other component. The made world evolves
as a holistic, continuous, and interdependent web, andnot as a disjoint assortment of separate trajectories or
product life cycles.
Investors constantly import previously untriedcomponents from outside the extant made world,
for example, the use of medicinal substances fromtropical jungles or the exploitation of petroleum andnatural gas, substances that had little application totechnology prior to the 19th century. Investors also
create new components through encapsulation andhierarchical modularization of component sets (Simon
1996, Baldwin and Clark 1999). Such “black box” engineering efforts make the underlying components lesssalient but do not destroy the recombinant potentialof the components or the encapsulating module. As
these processes make additional components available
for further recombination, the diversity of the “made”
world increases. Because all previously used components and inventions provide potential constituentsfor future inventions, the potential risk set for recombination is the entire extant made world. Because
inventors also scan outside the made world, anything
not yet derived from the natural world can also be
considered as a potential constituent of invention, as
part of the theoretical risk set. Recombination usuallyoccurs, however, between components that are salient,proximal, and available for the inventor.
These ideas prompt us to look backward in time
to consider the components and untried combinations
that were available at the time of invention; they
prompt us to look forward to predict which inventions are more likely to motivate further recombination. Prediction of further recombination depends
on the fundamental tension between exploration of
untried possibilities and exploitation of previous successes (March 1991).
Cognitive, Social, and TechnologicalInﬂuences on Recombination
Because the agents of recombination are people,
the process of invention remains strongly inﬂuenced

Management Science/Vol. 47, No. 1, January 2001

by cognitive and social phenomena. The mostfundamental inﬂuence is a limitation on the number of potential components and combinations that aninventor can simultaneously consider. Because every
invention can be incorporated in further recombinations, inventors’ combinatoric potential has grown
explosively (Weitzman 1996). The set of potential combinations and, a fortiori, the possible ways that each
set of potential combinations can be combined has
become essentially inﬁnite. It has become impossiblefor individual inventors, groups, even entire communities of inventors to have more than an inﬁnitesimalunderstanding of all these potential combinations
and relationships. As a result of this combinatoric
explosion, inventors and their organizations andcommunities must focus and recombine locally froma limited set of components and combinations.
These arguments follow the assumption of bounded
rationality and local search (March and Simon 1958,
Nelson and Winter 1982, Cohen and Levinthal 1990,
March 1991, Kauffman 1993, Stuart and Podolny1996). Localness corresponds to inventors’ familiarity with their recombinant search space. Local searchor exploitation (March 1991) occurs when an inventor recombines from a familiar set of technology
components or reﬁnes a previously used combination. A pastry chef searches extremely locally when
he mixes previously used dyes in a new proportion to create a novel frosting color. Distant searchor exploration (March 1991) occurs in the opposite situation, when inventors try completely new components or combinations. The early auto industry provides many examples of successful and unsuccessful
distant search including various power sources, pneumatic tires, brakes for each passenger, four-wheeled
diamond conﬁgurations, and, in the 1930s, a combined
car and plane conﬁguration (Basalla 1988).
One would expect that local recombination is morecertain and, on average, more successful. To theextent that inventors draw from familiar component sets and reﬁne previous combinations, they areless likely to develop a completely useless invention.
They also decrease their upside potential, however,of developing a radically different invention that isof much greater impact. Local recombination is morecertain because inventors learn from past failures.

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Inventors learn which components failed in previous inventions and stop using them. They learn to
avoid the combinations and architectures that failed
in the past. They winnow and bound the less successful regions of recombinant space (Vincenti 1990).
This winnowing and bounding improves the average
usefulness of inventive efforts but also decreases thepossibility of wildly successful inventions.
As inventors reuse components they begin tounderstand and characterize those components. They
begin to understand which components are moreor less useful in different contexts. Such knowledge
enables selection and exploitation of more appropriatecomponents in future inventions. Understanding andknowledge increase with use; the greater the use of acomponent, the greater the knowledge of and familiarity with it. Knowledge can also be forgotten, however, and is therefore more potent the more recently it
has been gained (Argote et al. 1990). More recent andfrequent usage therefore implies greater knowledge
and familiarity. Inventions that incorporate familiar
components should be more useful because inventors
can select more appropriate components. Because they
can better predict the performance of the included
components, incorporation of familiar components
should also decrease inventive uncertainty and, hence,the variability of outcomes.
Inventors can draw on others’ knowledge andexperience in addition to their own. While social proximity certainly increases the ease and likelihood ofsharing, knowledge about component use will still diffuse between organizations (Allen 1977) and technological communities (Bijker 1987). Many mechanisms
facilitate knowledge diffusion, including personnel movement, personal friendships, organizational
merger, education, reverse engineering, technical literature, and strategic alliances (Ahuja 2000). Althoughmuch knowledge will be lost or changed in its diffusion, sharing will take place at all social levels,
between individuals, organizations, and communities.
Taken together, these arguments imply the following.
Hypothesis 1. Recombination of familiar components
will increase an invention’s usefulness.
Hypothesis 2. Recombination of familiar components
will decrease inventive uncertainty.

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Parallel arguments hold for the reﬁnement andimprovement of combinations. Engineers gain experience with particular combinations by using them.
As inventors learn which combination relationships
or architectures are less useful, they avoid those
approaches. Such learning and knowledge helps
them improve their inventive efforts on average anddecrease inventive uncertainty. As with knowledge
about individual components, knowledge about combination relationships increases with frequency of use
and decreases since last use. As with individual components, knowledge of combinations and their optimal relationships will diffuse throughout the madeworld. These arguments imply combination hypotheses that parallel the individual component hypotheses.
Hypothesis 3. Reﬁnement of familiar combinations
will increase an invention’s usefulness.
Hypothesis 4. Reﬁnement of familiar combinations
will decrease inventive uncertainty.
Practitioners have long recognized the value ofreuse and reﬁnement (Mead and Carver 1980) and the
difﬁculty of exploring new regions of the essentiallyinﬁnite design space. For example, Altschuler (1998)
explicitly recommends searching previous inventions
for universal analogies and possible applications to
new contexts, and Goldenberg et al. (1999) propose
and empirically validate a method to identify andlink proﬁtable dependencies between previously usedcomponents. Unfortunately for inventors, however,these beneﬁts of familiarity do not last forever. This
results from the technological and social-psychologicalexhaustion of potential reﬁnements, given a particular
combination. Both inﬂuences run counter to the positive effects of local search.
Technological exhaustion occurs because most of
the possible relationships between a set of components have already been tried. As Kim and Kogut(1996) argue, “The repeated application of a particular
set of technologies or organizing principles eventually
exhausts the set of potential combinations.” The argument generalizes Sahal’s (1985) demonstration of thedecreasing returns to physical scaling. For example, if
semiconductor inventors restricted their usage to their
original materials of aluminum and bipolar transistors, progress in the ﬁeld would have halted long ago.

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Recombinant Uncertainty in Technological Search

However, because inventors began using new materials such as copper interconnect and new combinations
such as metal oxide semiconductors, semiconductor
chips have continued to shrink and the trajectory hasrepeatedly avoided exhaustion.
Exhaustion in technological potential also stems
from social and psychological sources and can ultimately be traced to inventors’ frames and “imaginary life cycles” (Henderson 1995). If a community of
inventors accepts or reiﬁes a particular combination as
mature, then they are less likely try new combinationsor broaden their component set. Henderson describes
three such attitudes in her study of the premature
obituaries for optical lithography in 1977: Inventors
failed to consider the possibility of new architectures, improvement in their components, or changes
in users’ application of the technology. These technological and social-psychological effects run counterto the improvement that results from familiarity andlearning.
Hypothesis 5. Cumulative use of a combination will
decrease an invention’s usefulness.

Methods
Thinking of invention as a recombinant search process implies that an invention’s usefulness and uncertainty can be predicted from previous usage of its
components and particular combination of components. I test these ideas with patent data and estimation of a statistical model. The models predict futureprior art citations to a given patent based on previous
usage of its assigned subclasses and particular combination of subclasses. The hypotheses place additional
constraints on the empirical work, however. If engineers can recombine any technology with any other,
the data should include recombination across technological communities. If invention is a process of search,learning, and exhaustion, the data should include thehistory of component and combination use. Finally,
if the uncertainty of invention decreases with learning and the use of familiar components and combinations, the statistical model should estimate the impact
of such learning and familiarity on the variability of
inventive outcomes.

Management Science/Vol. 47, No. 1, January 2001

Data
The unit of analysis is an U.S. patent granted during May or June of 1990 n = 17264.2 The data camefrom the MicroPatent (1996) product. While patent
data are admittedly imperfect records of technology(Levin et al. 1987) and conditional on a successful
patent application, these data still represent a large
portion of failed and successful invention. Independent measures (recombinant history) came from data
before July of 1990 and dependent measures (citations)came from data after July 1, 1990. Citations are used
as controls and a dependent variable only—they are
not used to trace the path of combination reuse, learning, or the diffusion of knowledge. The design is crosssectional and does not consider multiple time periods.
While the sample is large enough to provide very signiﬁcant results for all the hypothesis tests, future work
should consider other time periods to minimize thechances of idiosyncratic sampling.
I observe recombination across all technologicalcommunities by looking simultaneously at all patents
granted during the two-month time period, thus satisfying the ﬁrst modeling constraint. If the data did
not consider all communities simultaneously, I would
need to deﬁne the boundaries between particulartechnological communities. Because such boundaries
are transient, permeable, overlapping, and nested, itwould be empirically intractable to determine themfor even a fraction of the made world. Finally, twohundred years of patent data satisfy the need toobserve recombinant history of component and combination use.
Measures
Table 1 lists and describes all variables. For the dependent variables of mean and variance, I measure an
invention’s usefulness as the number of prior art citations that it receives from subsequent patents. To be
granted a patent, the applicant must establish the novelty of her invention relative to all previous inventions.
She establishes her claim to novelty by identifying(almost always after the fact of invention) and citing
similar “prior art.” The patent examiner then reviews
and usually supplements these citations (Carr 1995).
2

I chose the months of May and June at random.

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Table 1

Description of Variables

VariableType

Description

MeasureCitationsdependent

invention’s usefulness or importanceprior art citations by future patents to focal patent

Mean technology

control

expected citations to technically similar patents

weighted ﬁxed effects by focal patent’s sub-classmembership in class

Variance technology

control

expected variance in citations to technically similar
patents

weighted ﬁxed effects by focal patent’s sub-classmembership in class

Number of prior art citations

control

patents that cite more heavily should be moreheavily citednumber of prior art citations by focal patent

Single-class dummy

control

process of recombination is ﬁner grained than canmeasureequals 1 if focal patent assigned to only one subclass

Number of subclasses

control

number of invention’s components

number of focal patent’s subclasses

Newest subclasscontrol

artifact of patent classiﬁcation system

equals the minimum number of previous uses
amongst the focal patent’s sub classes

Number of classes

controlbreadth of patent classiﬁcations

number of focal patent’s classes

Component familiarity

independent

inventor’s familiarity with components of theinvention

recent and frequent usage of focal patent’s subclasses
across all U.S. patents

Combination familiarity

independent

inventor’s familiarity with particular combinationof components

recent and frequent usage of particular subclass combination across all U.S. patents

Cumulative combination usage

independent

cumulative number of inventive trials with exactly
same combination

cumulative number of U.S. patents with exactly same
subclass combination

Bibliometric studies have repeatedly demonstrated
that future prior citations to a patent correlate with
its technological importance and value (Albert et al.
1991, Hall et al. 2000). To make maximal use of thedata, I measure prior art citations to a focal patent for
6 years and 5 months after its granting. This period
should capture the bulk of citations to a patent as thesecitations typically reach a plateau after about three
years from the grant date (Jaffe et al. 1993). While there
appears to be strong correlation between the rates of
early and later citations to a patent, researchers areactively pursuing the topic (Hall et al. 2000).
To measure the independent variables, I proxy components with patent subclasses. The patent ofﬁce categorizes all patentable technologies into some 400“class references”. Each class is also subdivided intovery ﬁne divisions of technology or approximately
100,000 “subclasses” (Trajtenberg et al. 1997). The
patent ofﬁce typically assigns each patent into multiple subclasses within and across major classes. The
patent ofﬁce also establishes and updates new classes
and subclasses each year, as technology changes (Carr
1995, p. 128). This retrospective updating enables

122

historical consistency in the measurement of components across time.
I do not propose that inventors recombine patentsubclasses directly, only that subclasses can be used
to observe indirectly the process of recombinantsearch and learning. I will illustrate the correspondence between components and patent subclasses
with patent 5,136,185, coauthored with John Walther.
It was (as of Dec. 1996) classiﬁed in four subclasses:
326/16 (with test facilitating feature), 326/31 (signal
sensitivity or transmission integrity), 326/56 (tristate(i.e., high impedance as third state)), and 326/82(current driving (e.g., fan in/out, off chip driving,etc.)). Each of these subclasses corresponded to awell-understood digital hardware component at thattime. Each of them can be found in (and, for thispatent, were drawn speciﬁcally from) contemporary
textbooks of digital design such as McCluskey (1986).
A test facilitate feature (p. 426) has become a necessity with large computer chips due to their millionsof gates. To test the innards of a chip, the chip must
be operated in a “test mode.” A transmission gate(p. 118) simply passes on a signal when enabled.

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A tristate driver (back cover diagram of digital components and pp. 105, 119, 142) is a gate that can drive abus or be turned off to present high impedance to thebus. Finally, “fan in” and “fan out” (p. 104) refers to
the number of components that can drive or be driven
by a particular gate (high fan out basically means a
big gate). While we were not aware of the subclassdeﬁnitions at the time of the invention, we were very
familiar with these basic building blocks of our technological community, namely digital hardware engineers. I propose that subclasses serve as proxies forthese building blocks.
With the subclassiﬁcations and date of applicationof each patent (interpolated prior to 1975), I develop
the three independent variables for each focal patent:
component familiarity, combination familiarity, andcumulative combination usage. Component familiarity
proxies inventors’ familiarity with components, basedon the average degree to which the components havebeen recently and frequently used. The assumption
is that inventors will be more familiar with components that have been recently and frequently used.
I ﬁrst calculate an individual measure for each separate subclass of the focal patent. To the extent that a
particular subclass has been recently and frequently
used, its individual measure will be higher. For eachfocal patent, I look backward to 1790 and consider
each of its individual subclasses in turn. Whenevera particular subclass has been used in any previous
invention, I multiply the indicator of occurrence byan exponentially decaying component (1). This exponential component represents the loss and forgettingof knowledge. For example, it is more likely that aninventor will have learned from previous use of a
subclass, if that sub-class was used three years prior,instead of thirty. These occurrences are then summed
and averaged (2).
Individual component familiarity of patent i’s
subclass j ≡


Iij =

1 patent k uses subclass j

(1)

all patents k granted
before patent i×e application date of patent i− application date of patent k −time constant of knowledge loss

Management Science/Vol. 47, No. 1, January 2001



Average component familiarity of patentall subclasses Iij
j of patent ii s subclasses ≡ Fi = 
all subclasses 1(2)

j of patent i

I set the time constant of knowledge loss at ﬁve
years. This constant in the denominator of the exponential implies that approximately one-third of theknowledge remains after ﬁve years, or a yearly loss
rate of 18%. Argote, Epple, and Darr (Argote et al.
1990, Epple et al. 1991, Darr et al. 1995) have estimateda much higher geometric loss parameter for manufacturing and service organizations, between 40% to
97% per year. It is unlikely that design technology
would experience such a high rate of loss, however.
Design knowledge is far less contextual and more easily articulated than manufacturing or service experience. Design knowledge is more likely to have beenrecorded in trade journals, ﬁrm documentation and,of course, patents. It may have been actually realized in prototypes and products and is far more
likely to have required substantial personal effort andinvestment on the part of its designers. All of theseinﬂuences would argue for a slower loss rate thanmanufacturing and service organizations, but the precise estimation certainly constitutes a valid researchquestion.
I calculate combination familiarity and cumulative
combination usage similarly. The cumulative usage
measures how many times since 1790 a particular
combination of subclasses has been used. Combination familiarity proxies inventors’ familiarity with the
combination, based on the degree to which the combination has been recently and frequently used. For
each focal patent, I consider its particular combination of subclasses. I then look back in time at allother patents that used an identical combination of
subclasses. For each previous patent that used an
identical combination of subclasses, I increment the
cumulative combination usage (3). For the combination familiarity variable, I multiply each indicator
count by the exponential component, to reﬂect theloss and forgetting of experiential knowledge regarding the combination (4). All the measures remain indirect because they assume that learning occurs with use
and that knowledge diffuses throughout technological

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FLEMING
Recombinant Uncertainty in Technological Searchcommunities and the made world. Given that all the
independent variables are highly skewed, I took thesquare root of their original value. This minimized
the effects of outliers and enabled more parsimonious
modeling.
Cumulative combination usage of patent i ≡ Ci =
1patent k uses identical
all patents k granted
before patent i




(3)

1patent j cites a patent within class i∗proportion of cited patent within class i
1patent k classiﬁed within class i

all patents k granted
from 1985−1990∗proportion of patent k within class i

(5)combination of subclasses

Variance in citations to patents classiﬁed within classi ≡ i2 =Combination familiarity for patent i ≡ Ri =1patent k uses identical combination
all patents k granted
before patent i
all patents k granted
from 1985−19905

1patent k classiﬁed within class i ∗ proportionof patent k within class i ∗ citesk − i 2

all patents k granted
from 1985−19905of subclasses as patent i  application date of patent i − application date of patent k 1patent k classiﬁed within class i∗proportion of patent k within class i(6)time constant of knowledge loss

(4)
Given the theoretical interest in recombinant search,the models should ideally control for other differences
between inventions, for example, the differences incitation patterns across different technology classes.
I calculate technology mean and variance by measuringcitations to a technology class prior to the dependentmeasures period. I ﬁrst calculate the expected number of citations to each class in the ﬁve and a halfyears prior to July of 1990 (5). Equation (6) illustrates
similar calculations for the variance of each patentclass. Using these numbers for each class, I control
for the expected mean and variance of citations toeach focal patent based on a weighted average of its
assigned technology class references. I calculate thisby multiplying the proportion of a patent’s subclassassignments within a particular class by the average
cites/class of that class. As an additional control ofdifferences in citation activity, I include the number ofprior art citations that a focal patent makes to previous
patents. Although none of these measures differences
across industry directly, in aggregate they provide
some control of nonrecombinant heterogeneity, to the124

Average citations to a patent classiﬁed within classi ≡ i =all patents i granted
from 1985−19905

as patent i

×eextent that industries have similar technologies andcitation patterns.

Given the theoretical assumptions of bounded rationality and component search, the models should control for the number of the invention’s components.
These can be proxied by the focal patent’s number ofsubclasses. Because it becomes increasingly difﬁcult foran exact match to occur as the number of components increases, I include a second-order term for thisas well.3 Because the recombinant history of patents
with only one subclass remains unobservable, I also
include a dummy variable for single subclass patents(they comprise 8.1% of the data).
Finally, the models should control for artifacts ofthe patenting system. The patent ofﬁce periodically
updates technology classiﬁcations and creates new
subclasses (these analyses use the classiﬁcations ineffect as of November 1996). Because such reclassiﬁcation essentially recognizes newly successful technologies, the ﬁrst few patents of a retrospectively identiﬁed
technology stream are likely to be highly seminal.
Hence, patents that include a newly designated subclass will probably be highly cited. I include a newest
3
I would like to thank an anonymous reviewer for this suggestion.
In addition to adding the squared term, I checked the sensitivityof the results by estimating the model separately on subsets of thedata, for patents classiﬁed in two and three subclasses. The results
agreed with those presented below.

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Table 2

Descriptive Statistics for All U.S. Patents, May/June 1990
n = 17264

Variablemean

stnd.dev.

minimum

citations

380

488

000

mean tech control

119

041

033

303

variance tech control

465

265

036

2055

number of prior art citations

763

699

000

11000single subclass dummy

008

027

000

100

number of subclasses

421

331

100

13000newest subclass10707

10397000

187400

number of classes

178

095

100

1200

component familiarity

060

027

000

228

combination familiarity

028

075

000

1125

cumulative comb usage

062

160

000

2676

maximum
8200

subclass control variable that equals the minimum of
the least used subclass (for example, if a patent had
three subclasses and each subclass had been previously used in 56, 2, and 43 patents, the variable would
equal 2). This variable is very similar to the dependent
variable and hence probably decreases the effects of
the independent variables. I also include the number ofclasses to which a patent belongs to reduce the effect ofadditional citations to a patent, similar to that which
occurs when a scientiﬁc paper straddles ﬁelds.
Tables 1, 2, and 3 list the variables, descriptive statistics, and a correlation matrix. Combination familiarity
and cumulative usage demonstrate high correlation in
Table 3. This is not a problem except for inﬂated standard errors, given the desirable large sample properTable 3

ties of maximum likelihood estimators (Greene 1993,
p. 133).
Negative Binomial Count Models
The dependent variable of citation counts takes on
only whole number values (that is, 0, 1, 2, etc.). The
use of a linear regression model on such data canyield inefﬁcient, inconsistent, and biased coefﬁcientestimates (Long 1997). Explicit count models can avoid
these problems. Researchers often use Poisson models
to analyze count data, but Poisson models assume thatthe mean and variance of the observed distribution are
equal. These data, like most count data, exhibit overdispersion—the variance is greater than the mean.
Negative binomial regressions explicitly accommodate this over-dispersion, however, by enabling thevariance to be greater than the mean. Recent developments (King 1989, Jorgensen 1997) also support independent estimation of effects on the mean and thevariance of the predicted mean, as functions of potentially differing sets of variables.
To begin with, consider a model that estimates themean number of citations that a patent should receive,given its independent variables. The observed citations to the patent will not correspond exactly tothe prediction, however, and will be distributed withsome variance around the expected mean. I operationalize the uncertainty of invention by estimating
the effects of substantive variables on this variance.
For example, to the degree that some independent

Correlation Matrix for All U.S. Patents, May/June 1990 n = 17 264
cites

mean tech control

meanvarianceprior

numsubnewest

class

comp

combin031variance tech control

030

094

number prior art cites

012

002

−006

000

000

−007

number subclasses

011

001

007

008

single subclasssingle000−029

newest subclass

−008

−008

−007

−003

024

number of classes

008

−002

001

006

−024

−023
051

007

component familiarity

016

030

035

−001

−009

027

001

014

combination familiarity

001

006

005

−004

047

−027

−005

−023

017

cumulative comb usage

−005

−003

−003

−005

065

−030

−005

−026

006

Management Science/Vol. 47, No. 1, January 2001

0.85

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FLEMING
Recombinant Uncertainty in Technological Searchvariable decreases inventive uncertainty, its effecton the variance of this error distribution should be
negative.
Most derivations of the negative binomial start froma basic Poisson model (7). The basic Poisson model
estimates the probability of an observed count, conditional on an expected mean i . To avoid negative (i.e.,
undeﬁned) expected values for the mean i , Poissonmodels typically parameterize explanatory variables
as an exponential function (8). The method of maximum likelihood is then applied to the joint frequency
formed from the product of the marginal frequenciesof (7), to determine the coefﬁcient values that are most
likely to result in the observed counts.
yPryi xi  =e−i i iyi !

Eyi xi  = i = exi  

(7)
(8)

The negative binomial model replaces the Poissonmean i with the random variable ˜ i (9). This replacement enables the inclusion of an error term i = e i —and allows the predicted mean to vary according to
the distribution of the error term. Substitution of ˜ ifor i in (7) results in (10).
˜ i = exi + i  = i i Pryi xi  i  =e−i i i i yi

yi !

(9)
(10)

When ˜ i replaces i in (10), the probability of theobserved count becomes conditional on the error distribution. This conditioning can be removed, however,by specifying the error distribution and integratingwith its probability density function to obtain themarginal density. Most formulations specify a gamma
distribution for i with parameter i and probability density function g i  as in (11) (Hausman et al.
1984, Cameron and Trivedi 1986, King 1989, Long
1997). Although the error term can take other distributions, this parameterization is ﬂexible, computationally tractable, and can be derived from a variety ofassumptions. While other versions of the gamma distribution take two parameters, this derivation (fromLong 1997, p. 232) sets both to i , which forces themean of i equal to one and the variance of i equal to126

1/ i . Integrating (10) with the density function of (11)gives the probability of the negative binomial of (12).
g i  =iie− i i  for i > 0and   =t −1 e−t dt
(11)
0
 i
yi

i
yi + i i
 (12)
Pryi xi  =yi !  i 
i + ii + ii−1

 i i

While the ﬁrst term of (9) fully speciﬁes the meanof the negative binomial, various parameterizationsof i remain possible. Cameron and Trivedi (1986)
propose the negative binomial II parameterization (orNegbin II model) when the variance/mean ratio of
the observed data is linear in the mean. By contrast,
the Negbin I holds the variance/mean ratio constant.
The Negbin II speciﬁcation is also much more robust
to distributional misspeciﬁcation than other parameterizations (Cameron and Trivedi 1986). I veriﬁed
the applicability of the Negbin II by regressing predicted counts on the quantity (residualsˆ2/predicted).
The coefﬁcient and intercept were positive thus supporting the Negbin II parameterization (Cameron andTrivedi 1986). Also consistent with Cameron andTrivedi’s argument (1986), Negbin II models demonstrated much more signiﬁcant log likelihoods than
Negbin I models.
Equation (13) speciﬁes the Negbin II parameterization of the conditional variance. i is the inverse ofi and is parameterized as an exponential function,
similar to the mean speciﬁcation but with potentiallydifferent variables. Since var i  = 1/ i = i , greater
variance of the error term for a given estimated mean
will result in an increase of i . I operationalize uncertainty by estimating the effects of the causal variables on the dispersion parameter . Variables that
decrease  will decrease the variability and hence theuncertainty of inventive outcomes.
i= i + 2i (13)Varyi x =  1 +i

STATA estimates (12) by the method of maximum
likelihood. This technique estimates both the mean iand variance 1/ i from (12), and hence produces onlya single log likelihood.

Management Science/Vol. 47, No. 1, January 2001

FLEMING
Recombinant Uncertainty in Technological Search

Results
Table 4 presents estimates for the negative binomial
dispersion models of citation counts. Model 1 estimates a baseline model of controls only and Model
2 adds substantive variables to the baseline model.
Piecewise models indicated a nonmonotonic effect ofcomponent familiarity on the dispersion. To accommodate this, Models 3 and 4 include both ﬁrst- andsecond-order terms for component familiarity. The
substantive variables greatly increase the explanatory
power of Models 2 and 3, as measured by twice thedifference in log likelihoods and compared to a chisquared distribution with degrees of freedom equal tothe added number of variables.4 To test robustness,
Model 4 estimates substantive variables only. Coefﬁcient magnitudes and signiﬁcances vary but the substantive results remain unchanged and signiﬁcant. To
further check for robustness, I split the data set in half,
randomly, and by month. Signs remained unchanged
although not always signiﬁcant. The parameter estimates are not standardized and should be interpreted
as the predicted multiplier effect on the mean citation
count and dispersion parameter (they should be exponentiated as in (8) and (13)).
Hypothesis one proposes that usage of familiar
components will enable inventors to apply learningfrom previous efforts. Such learning improves inventors’ abilities to select the best components and recombine them more successfully. Component familiarity
has a positive and highly signiﬁcant coefﬁcient estimate. A patent receives 147% more citations at its maximum value of 2.28 as opposed to its lowest valueof 0.5 To interpret less extreme changes in the variable, a one standard deviation increase in component
4
R-squared measures are inappropriate for maximum likelihood estimates and suffer from various problems (Cameron andWindmeijer 1996). Cameron and Windmeijer (1996) propose animperfect (by their argument) measure for a negative binomial
model, but it only considers the explanatory power of variableson the mean. Cameron (in personal communication) indicated thatan acceptable R-squared measure for measuring the explanatory
power of variables on the dispersion parameter has yet to be
proposed.
5

At its maximum value of 2.28, and from the coefﬁcient estimate of 0.3958 in Model 3, the effect of component familiarity ise03958 ∗ 228 = 247.

Management Science/Vol. 47, No. 1, January 2001

familiarity results in an 11.3% increase in expectedcitations.6
While the results support Hypothesis 1, they do
not support Hypothesis 2, that component learning
decreases the uncertainty of invention. Given that theﬁrst-order term by itself became insigniﬁcant without controls (model not shown), I estimated piecewisemodels to check for a nonlinear effect. These models indicated a nonmonotonic effect, with the maximum negative effect at the 60th percentile of thedata. Compared to a baseline model of no effect ofcomponent familiarity on the variance, component
familiarity decreases the variance by 9.6% at the 60th
percentile.7 Past that point, the effect becomes lessnegative and eventually becomes positive at the 96th
percentile. It appears that increasing familiarity withcomponents has an initially negative effect on the variability of invention. Eventually this changes, however,such that the use of very familiar components has apositive effect on variability.
In parallel to Hypothesis 1, Hypothesis 3 argues thatreuse of familiar combinations lets inventors apply
learning from previous efforts. Such learning enables
reﬁnement and improvement of previous inventions,such that future inventions are more useful. Combination familiarity demonstrates a positive, strong,and highly signiﬁcant effect on the mean. A patentreceives 332% more citations at combination familiarity’s highest value of 11.25 as opposed to its lowest value of 0. A one standard deviation increase of
combination familiarity results in a 10.3% increase in
expected citations.
In contrast to the contrary results of component
familiarity on the dispersion, combination familiarity demonstrates predicted and highly signiﬁcant6e03958 ∗ 027 − 1 = 01128.

7

The maximum negative effect of component familiarity on thedispersion (from Model 3) occurs at 0.5992. At the mean citation
count of 3.80 the variance is thereforeVaryi x = i + 2i = 380e03958 ∗ 059922+e−04139 ∗ 05992 + 03454 ∗ 05992  380e03958 ∗ 05992 2 = 2532
At the mean citation count of 3.80 and no effect of combination
familiarity on the dispersion at 0.5992, the variance is Varyi x =i + 2i = 380e03958 ∗ 05992 + e0 380e03958 ∗ 05992 2 = 2802. Hence themaximum negative effect is 2802 − 2532/2802 = 96% less.

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Recombinant Uncertainty in Technological Search

Table 4

Negative Binomial Models of Citation Counts (All U.S. Patents, May/June of 1990)

Variable/Models

Model 1Model 2Model 3Model 4

Effects on the mean
Mean technology control

08830∗∗∗07790∗∗∗07800∗∗∗001980021500216

Number of prior art citations

00181∗∗∗00184∗∗∗00185∗∗∗000120001200012Single-class dummy control−01742∗∗∗−01568∗∗∗−01605∗∗∗

003490043700438

Number of subclasses control

00271∗∗∗00183∗∗∗00185∗∗∗000440004900049

Number of subclasses squared−00002+−00001−00001000010000100001

Newest subclass control−00003∗∗∗−00009∗∗∗−00009∗∗∗000010000100001

00461∗∗∗00425∗∗∗00413∗∗∗000970009800098

03807∗∗∗03958∗∗∗07195∗∗∗004300044100353

01329∗∗∗01301∗∗∗02301∗∗∗002230022500218

−00384∗∗−00363∗∗−01523∗∗∗

001310013200107−00899∗−01090∗∗−01134∗∗09115∗∗∗00363003690037200230−00154∗∗−00187∗∗−00177∗∗000540006000060

−00011−00007−00007000200002000020

0100102426∗∗∗02111∗∗∗006560072500731−00170∗−00164∗−00178∗000750008100081

00003+00003+00003+000020000200002

−00003+−00003−00002

000020000200002

−00390∗−00505∗∗−00420∗001890019100193

01683∗−04139∗−05331∗∗007490204801798

03454∗∗03121∗∗

Number of classes control
Component familiarity
Combination familiarity
Cumulative combination usage
Constant
Effects on dispersion parameter
Variance technology control
Activity control
Single-class dummy control
Number of subclasses control
Number of subclasses squared
Newest subclass control
Number of classes control
Component familiarity
Component familiarityˆ2
Combination familiarity
Constant
Log-likelihood

0113501059−01033∗∗∗−01011∗∗∗−00506∗
002000024900248

00545−0017701609∗01644∗00560005780081900683−4109526−4100192−4099726−4202200(n = 17264 all models, standard errors in parentheses, +p < 01, ∗ p < 005, ∗∗ p < 001, ∗∗∗ p < 0001)

128

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Recombinant Uncertainty in Technological Search

results on the dispersion. The negative effect supports
Hypothesis 4, that inventive uncertainty decreases
with the reﬁnement of previously used combinations.
At the mean citation count of 3.80 and the mean valueof combination familiarity at 0.28, the variance is 2.2%
less than it would have been without the effect of combination familiarity on the dispersion. At the maximum value of combination familiarity, the difference
grows to 64%.
Finally, Hypothesis 5 predicts that cumulativeuse of a particular combination will eventually
exhaust recombinant potential. The results support
this hypothesis, as indicated by the negative and signiﬁcant estimate on cumulative combination usage.
When cumulative combination count reaches its maximum value of 26.76, a patent receives only 37.9% of
the citations it would have received at the variable’s
lowest value of 0. A one standard deviation increase of
cumulative combination use results in a 5.6% decreasein expected citations.

Discussion
These results should be viewed cautiously for avariety of reasons. The typical reservations regarding the use of patent data certainly apply, mostnotably that patenting practices and effectiveness varyacross industries (Levin et al. 1987). Furthermore, eventhough these data cover all patented technologies
across a two-month time period, much inventive activity remains unpatented and, hence, unobserved. Theseissues cause at least three problems. First, the dataset
misses unimportant inventions that failed to merit apatent. Use of citation data mitigates this problem,
however, because it includes the bulk of relatively useless inventions that receive no or few citations. Second, the dataset may miss breakthroughs that ﬁrms
chose not to patent, presumably for strategic reasons. However, unless there is systematic bias in thoseﬁrms’ use of particular components or combinations,these results should remain valid. Third, technological communities vary in their propensity to patent.
Again, while these models controlled for much variance across technologies, they did not introduce
explicit industrial controls. But given the focus onrecombinant search across technological communities,

Management Science/Vol. 47, No. 1, January 2001

it would be difﬁcult to compile similarly vast observations across so many communities. The dataset therefore trades detail and depth in exchange for breadthand possibility of observation.
In addition to differences in the dependent variableof citations across communities, the accuracy of the
independent variables also varies across communities.
Subclasses and combinations of subclasses represent
only a proxy for inventors’ components and architectures. While the subclasses of digital hardware patents
correspond very closely to my engineering experience, other subclasses may not. For example, ﬁnancial
patents tend to be classiﬁed in fewer subclasses and,hence, may not reﬂect a process of recombinant search.
They may also be better characterized as knowledge oralgorithms and not technology. Such patents, however,
became popular only after the observation period.
In addition to concerns about data and variables,
these results also remain open to alternate interpretations. Most importantly, these models cannot deﬁnitively separate learning and familiarity, technologicalexhaustion, and life-cycle effects. For example: anexogenous variable (such as the inventive myopia
described by Henderson (1995)) could drive the useof particular components and combinations, such that
increased citations might simply reﬂect the popular
usage of those components and combinations. As aresult, the positive signs in the mean for component and combination familiarity and negative signfor cumulative use might reﬂect only the normal progression of technological life cycles, instead of learning and exhaustion. Even with the multiple controls
for differences in citation patterns across technologies,
types of combinations, and retrospective identiﬁcationof seminal subclasses, the models cannot convincingly
reject the alternative argument that the results merely
reﬂect the normal life-cycle progression.
These reservations do not apply, however, to theuncertainty hypotheses. Indeed, all of the explanatory variables in the mean could simply be interpreted
as control variables for the dispersion estimates. The
combination result supports the intuitive argumentthat uncertainty decreases with reﬁnement and highlights the importance of early architectural reﬁnements as a source of destabilizing technologies fororganizations and industries (Henderson and Clark

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FLEMING
Recombinant Uncertainty in Technological Search1990). In contrast, the nonmonotonic results for components does not support the simple linear relationship predicted in Hypothesis 2. It remains consistent,
however, with the argument that technological breakthroughs derive from new combinations of well-usedcomponents (Usher 1954, Nelson and Winter 1982,
Sahal 1985). For example, Utterback (1996, p. xxvii)
argues that “radical innovations often are seen to
be based on the synthesis of well-known technicalinformation or components.” These results also indicate that inventors take more inventive risk withextremely familiar components. These results support
Utterback’s argument and motivate further research.
For example, are breakthroughs most likely wheninventors combine very familiar components in new
combinations? If this were the case, then breakthroughs would be most likely to emerge from social
contexts that brought together inventors with deep
experience in previously disparate ﬁelds. Such contexts would also be more likely to be the technological source of potential future product life cycles andtrajectories.

Conclusion
This paper developed and tested an explanation forthe sources of purely technological uncertainty. It
argued that the source of technological novelty anduncertainty lies within the combination of new components and new conﬁgurations of previously combined components. Inventors’ experimentation withnew components and combinations leads to less success on average, but it also increases the variability
that can lead to breakthroughs. Empirical results supported the arguments with the exception that the useof more familiar components has a nonmonotonic andeventually positive effect on the uncertainty of invention. In contrast to the nonmonotonic effect of component familiarity, the reﬁnement of previously usedcombinations has a negative and monotonic effect on
uncertainty.
Coupled with recent and complementary research
regarding market inﬂuences (Adner and Levinthal
2000) and formal economic models (Klepper 1996),
this work helps us understand the causal forces that
underlie the widely observed regularities of the product life cycle. The data and methods presented here130

also provide a strong basis for further investigations
into the sources of invention and technological uncertainty. Most importantly, negative binomial count anddispersion models enable researchers to analyze theﬁrst and second moments of patent citation data. Suchmodels will enable us to move beyond basic counts
in analysis of patent data and quantitatively analyze the outliers of the highly skewed distributionsof inventive trials. Such tools can enable more formal
analysis of breakthrough inventions, heretofore, “thedomain of economic historians” (Scherer and Harhoff
2000). For example, the classic controversy about thesources of technological breakthroughs, whether they
emerge from smaller, entrepreneurial, and “outside”
ﬁrms (Schumpeter 1939, Marquis 1969, Klein 1977), or
large, industrial incumbents (Schumpeter 1942), canbe reconsidered with these data and methods. The
approach presented here also has application beyond
the study of technology and patents, mainly that weshould think of varying variance as an opportunity
instead of a nuisance.
Future work should also investigate the relationships between invention as a recombinant search process and other literatures such as integrality, coupling,
and modularity (Ulrich 1995), complex systems(Kauffman 1993), modular operators (Baldwin and
Clark 1999, Goldenberg et al. 1999), the evolution ofmodular design choices (Simon 1996), and the market
implications of such issues (Christensen and Verlinden
2000). For example, interdependence between components should increase inventive uncertainty and modularity should decrease it. Kauffman’s (1993) models
of search over interdependent landscapes also imply
a positive but nonmonotonic effect on the mean. Conceptualizing technological invention as search overinterdependent landscapes implies a complexity catastrophe in technological evolution, as inventors face
greater numbers of components and greater interdependence between them (Fleming and Sorenson 2001).
Inventors are not blind search agents, however, andtheir search strategies will differ greatly and presumably be more effective than genetic recombination(Gavetti and Levinthal 2000, Rivkin 2000).
The work has strategic implications as well. Organizations that seek technological breakthroughs should
experiment with new combinations, possibly withManagement Science/Vol. 47, No. 1, January 2001

FLEMING
Recombinant Uncertainty in Technological Searchold components. They do so, however, at the riskof an increased number of failures. If invention isindeed a partially blind search process, such failures are to some extent unavoidable. Simple portfolio strategies are unfortunately not the complete
answer, because of the extreme skew of inventive
distributions. Although the overall variance of such
distributions decreases with an increasing numberof trials, ﬁrms—and indeed, as Scherer and Harhoff(2000) demonstrate—entire economies cannot anticipate completely stable returns. There might exist organizational mechanisms that encourage recombinant
exploration, while limiting the downside of increased
failures. For example, ﬁrms that can screen or test
their nascent inventions more effectively will beneﬁt more and should increase the variability of theirinventive trials. The recent progress in drug design asa result of automated screening processes provides asalient example (Amato 1999). Finally, science should
enlighten and shorten technological search over difﬁcult landscapes. Science can either motivate recombinant search across particular technological landscapes,
or aid in search across landscapes that inventors havediscovered empirically.
Acknowledgments
The author would like to acknowledge the helpful comments of
Gautam Ahuja, Steve Barley, Bill Barnett, Phil Bromiley, Laurie
Calhoun, Mike Hannan and his seminar students, James March,
Candace Fleming, Olav Sorenson, Toby Stuart, and three very helpful reviewers. He would also like to acknowledge the support ofWilliam Simpson and the Faculty Research Computing Center atthe Harvard Business School, the Department of Industrial Engineering at Stanford University, and Corey Billington, Phil Johnson,
and Ellen King and the library staff of Hewlett Packard Company.
Errors and omissions remain my responsibility.

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Accepted by Karl Ulrich; received October 27, 1998. This paper was with the author 8 months for 3 revisions.

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