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Reproducing Knowledge: Replication Without Imitation at
Moderate Complexity
Jan W. Rivkin,
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Jan W. Rivkin, (2001) Reproducing Knowledge: Replication Without Imitation at Moderate Complexity. Organization Science
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Reproducing Knowledge: Replication Without
Imitation at Moderate Complexity
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Jan W. Rivkin
Harvard Graduate School of Business Administration, Morgan Hall 239, Boston, Massachusetts 02163
jrivkin@hbs.edu
Abstract
The complexity of a firm’s strategy affects both the ease with
which the firm can replicate the strategy in a new setting and
the ease with which rivals can imitate it. Simple strategies are
as readily imitated as replicated, and highly intricate strategies
resist imitation and replication equally. At moderate levels of
complexity, however, a wedge develops between the ease of
replication and the difficulty of imitation, so long as the replicator has better information than the imitator about the original
success. An agent-based simulation model clarifies the structural reasons that this is so. The model also shows how the
wedge-maximizing level of complexity varies with the replicator’s informational edge over the imitator.
The results help to pinpoint situations in which strategies
requiring replication are likely to defy imitation and generate
sustained competitive advantage. More generally, the analysis
sheds light on the value of superior but imperfect information
about good solutions to hard problems. Finally, the results suggest that a pattern long observed by organization scholars—that
“loosely coupled organizations” are especially effective competitors—may arise for a very different reason than is normally
posited.
(Replication; Imitation; Complexity; Interaction; Loose Coupling; Competitive Advantage)
imitation is elusive. Rather, replication and imitation often go hand-in-hand. A productive system whose component resources are readily assembled and coordinated
is easy to replicate. If such a system is profitable, however, imitators will rapidly construct equivalent systems
and, in the process, drive down output prices or bid up
input costs. Conversely, the factors that permit a system
to defy imitation can forge equally strong barriers to replication. The tacit knowledge and personal relationships
that make a particular factory run smoothly, for instance,
are difficult to reproduce in a second plant, regardless of
who owns that plant. Productive routines which are challenging targets for rivals are often difficult targets for internal agents as well (Nelson and Winter 1982).
What characterizes a productive system which can be
replicated within a firm more easily than it can be imitated
by rivals? In this paper, I propose that the complexity of
a system can drive a wedge between the ease of replication and the ease of imitation. Following Simon (1962),
I define complexity as having two aspects; a productive
system is complex if (a) it consists of numerous elements
and (b) those elements interact with one another richly. I
then use NK simulation techniques (Kauffman 1993) to
establish the main hypothesis of the paper:
HYPOTHESIS. In the settings simulated here, the gap
between replicability and imitability tends to be greatest
at moderate levels of complexity.
1. Introduction
Entrepreneurs crave a superior system of production that
they themselves can replicate but others cannot imitate.
By replicating such a system within the boundaries of the
firm, the entrepreneur can exploit the system’s advantages
in a wide range of markets. Yet because the system cannot
be imitated by rivals, the entrepreneur does not face competitors of equal strength in those markets. In such a situation, replication may be the centerpiece of a firm’s
competitive efforts (Winter and Szulanski 2000).
Unfortunately for entrepreneurs, replication without
ORGANIZATION SCIENCE, 䉷 2001 INFORMS
Vol. 12, No. 3, May–June 2001, pp. 274–293
Simple systems are as easily imitated as replicated, and
highly intricate systems resist imitation and replication
equally. Systems of moderate complexity, however—for
structural reasons described below—are more easily replicated than imitated.
The paper is organized as follows. Section 2 argues that
replication and imitation are connected, but the link can
be severed. The section reviews game-theoretic, resourcebased, and organizational explanations for replicationwithout-imitation, but finds the most promising explanation
in an evolutionary perspective that views replication and
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JAN W. RIVKIN Reproducing Knowledge
imitation as competing search efforts. Accordingly, §3
formulates an agent-based simulation model of competitive search. In the model, imitators and replicators compete to discover, or rediscover, the knowledge embodied
in a successful productive system. The model purposely
suppresses game-theoretic, resource-based, and organizational factors that may impede both imitation and replication but are unlikely to account for a wedge between
the two. As a result, the model focuses exclusively on
how the character of knowledge and search can generate
replication-without-imitation.
The results of the model, presented in §4, show how
moderate complexity can make imitation more difficult
than replication. A final section summarizes the empirical
implications of the paper. It also offers a new interpretation of a longstanding observation in organizational theory, that “loosely coupled organizations” are especially
effective competitors. Under this interpretation, the correlation between loose coupling and superior performance arises spuriously, not because loose coupling is
advantageous per se.
2. The Link Between Replication and
Imitation
Pioneering efforts in evolutionary economics identified
replication and imitation as brethren phenomena. Nelson
and Winter (1982) cast both as efforts to reproduce successful routines. Later research pointed out that this similarity poses a fundamental dilemma to any firm which
hopes to transfer its successes to new sites: Not only do
the “[f]eatures that restrain involuntary transfer tend to
inhibit voluntary transfer” (Winter 1987, p. 174), but any
effort undertaken to ease internal transfer can make imitation more likely. An attempt to codify knowledge for
the purpose of replication, for instance, might make it
easier to communicate that knowledge to outsiders or
make it easier for outsiders to interpret the knowledge
(Kogut and Zander 1992, Zander and Kogut 1995).
2.1. A Breakable Link (Some Examples)
Case studies bear out the proposition that replication and
imitation are closely related. Examples also show, however, that the link between the two is not impregnable. In
the examples which follow and the subsequent discussion, I focus on the replication and imitation of entire
business strategies—that is, the set of customer needs
targeted by a business and the full array of functional
policies deployed to satisfy those needs in a superior manner. This is a departure from most of the earlier literature
on replication and imitation, which has tended to examine
the copying of smaller units of analysis: isolated processes (e.g., a new distillation technique), products (a
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
semiconductor), installations (a factory), or practices (total quality management). I depart in this way in order to
adopt the broadest framing for the analysis. The conceptual arguments apply to productive systems ranging in
scale from entire strategies to individual processes. I return to the topic of smaller-scale systems in the final section.
The early history of White Castle (Hogan 1997, especially Chapter 2) illustrates the hazards of an easily replicated, but quickly imitated, business strategy. White
Castle’s founders, Walt Anderson and Billy Ingram, are
widely credited with inventing both the hamburger and
the fast-food business. In Anderson’s four ramshackle
food stands in Wichita, Kansas, Ingram saw a success
formula that could be reproduced on a national scale.
Partnering with Anderson in 1921, Ingram ruthlessly
standardized and codified everything about a White Castle hamburger stand: the white steel exterior with crenellated walls and turrets; the interior layout with five
stools facing a grill; the staffing with a single cook and
“clean-up man;” the simple menu of hamburgers, CocaCola, coffee, and pie; the location in working-class neighborhoods and near factories; the emphasis on hygiene;
and so forth. He then applied the formula rapidly in other
cities: Omaha, Kansas City, St. Louis, Minneapolis, Louisville, Cincinnati, Columbus, Chicago, and New York,
all by 1930. As the chain expanded, Ingram insisted on
absolute standardization. To guarantee uniformity, the
company founded subsidiaries to grind beef, manufacture
steel buildings, and make paper caps and aprons. A
monthly newsletter, “Hot Hamburger,” publicizing corporate successes and standards, was distributed to employees and customers (and presumably to would-be rivals). Posters entitled “Look Yourself Over” reminded
employees of how they should appear and behave. Experienced employees were assigned to new territories,
and Ingram and Anderson learned to fly what must have
been one of the first corporate airplanes so that they could
inspect far-flung sites.
White Castle’s highly visible success, however, attracted a flood of copycats. Soon, lookalike white buildings with towers, turrets, or lighthouses spotted the corners of working-class neighborhoods throughout the nation.
Most adopted names that were clear takeoffs from “White
Castle”: among the contenders were White Palace, White
Log, White House, White Tavern, White Hut, White Cap,
White Shop, White Grille, White Cabin, White Plaza,
White Wonder, White Knight, White Turret, White
Tower, White Diamond, White Fortress, White Kitchen,
White Crescent, White Spot, White Manna, White Mill,
Blue Bell, Blue Beacon, Blue Tower, Blue Castle, Red
Lantern, Red Barn, Red Castle, Silver Castle, Magic
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JAN W. RIVKIN Reproducing Knowledge
Castle, Prince’s Castle, Klover Kastle, Little Kastle, Little
Crown, Modern Castle, Royal Castle, and Castle Blanca.
Many imitators used White Castle as a direct template.
The founders of White Tower, for instance, visited the
new White Castles in Minneapolis, observed operations
closely, photographed the sites, took interior and exterior
measurements, copied products, and “lured away a White
Castle operator at princely wages” (Hogan 1997, p. 54).
By the early 1930s, White Tower operated nearly as many
outlets as did White Castle. Imitation of White Castle’s
strategy had proven almost as easy as replication.
Just as one can find replicable strategies that were
quickly imitated, so it is easy to find examples of the
converse: hard-to-imitate strategies that also defied replication. Marks and Spencer, for instance, succeeded for
generations as Britain’s leading apparel and food retailer
and one of its most admired companies (Montgomery
1994, Collis 1996). Its products, sold only under its St.
Michael’s label, were manufactured by an intricate network of suppliers. Many suppliers produced almost exclusively for Marks and Spencer and did so for decades.
Through product-development and manufacturing partnerships with these suppliers, the company produced
high-quality items very efficiently. The quality of its
products, its good prices, and its reliable service generated enormous customer loyalty. It also enjoyed loyalty
from employees, to whom it had offered above-average
wages, benefits, and amenities since the 1920s. Imitators
failed to match Marks and Spencer’s faithful following
among suppliers, customers, and employees, or to equal
its prime locations on Britain’s High Streets.
Yet Marks and Spencer was unable to duplicate its British success in Canada and Europe in the 1970s and 1980s.
In Canada, the company tried to reconstruct its intricate
supply network in order to avoid high tariffs. The new
suppliers failed to meet Marks and Spencer’s quality standards, however, and the company resorted to importing
goods from its traditional suppliers. Canadian and European consumers were unfamiliar with the St. Michael’s
brand, and employee enthusiasm was difficult to recreate
abroad. Overall, “the same difficulties that inhibit[ed]
competitors from copying M & S’s position in the United
Kingdom also limit[ed] M & S’s ability to replicate its
U.K. position overseas” (Collis 1996, p. 1).
The connection between imitation and replication is
tight, but it is not ironclad. The pantheon of management
includes many entrepreneurs who reproduced successful
strategies within a firm but resisted imitation. Herb Kelleher
replicated Southwest’s no-frills airline success in markets
from California to New England even as other airlines
such as Continental failed in their no-frills efforts (Hallowell
1997, Porter 1996). Under Keith Iverson, Nucor set up
276
profitable steel mini-mills in numerous independent locations while rivals such as North Star and Northwestern
Steel and Wire struggled in the same business (Ghemawat
and Stander 1996). Ray Kroc installed his system for producing fast food in thousands of McDonald’s locations,
but kept would-be imitators such as Burger King and Hardee’s at bay. Others who have coupled replicability with
inimitability, at least for some time, include John McCoy
of Banc One (Uyterhoeven and Hart 1996, Winter and
Szulanski 2000), Charles Lazarus of Toys “R” Us (Rivkin
1995), Sam Walton of Wal-Mart (Foley et al. 1996), and
Robert Cizik of Cooper Industries (Collis and Stuart
1995).
2.2. Barriers to Replication and Imitation
Under what conditions are the bonds between replication
and imitation broken? To address this question, I consider
four perspectives on barriers to imitation and replication,
and I identify obstacles that apply unequally to the two
types of effort (Table 1). The analysis makes the case that
none of the candidate explanations for replicationwithout-imitation is wholly satisfying, but explanations
concerning managerial search deserve closer examination.
Game-Theoretic Strategic Maneuvers. Industrial economists, strategy theorists, organization theorists, and evolutionary economists have examined imitation and replication from very different angles. In the models of
mainstream industrial economics, firms typically deter
imitation by executing some game-theoretic maneuver:
By incurring a sunk cost, the firm alters its own marginal
incentives in a way that fends off rivals. By installing a
great deal of productive capacity, for instance, a firm
might credibly commit to resist any entrant vigorously,
and this commitment may cause would-be imitators to
avoid the market (Dixit 1980). Alternatively, the first firm
in a market might fill the “space” of possible products
(Schmalensee 1978) or write costly-to-break contracts
with customers (Aghion and Bolton 1987) that make it
difficult for copycats to secure a beachhead. In line with
economic orthodoxy, such models assume (and the assumption is so common that it usually goes unstated) that
replication is straightforward (Nelson and Winter 1982).
Productive knowledge can be duplicated within a firm at
no cost. Thus, for most industrial economists, the problem
of replication is foreign, and imitation and replication are
unconnected.
Perhaps, then, cases of replication-without-imitation
arise when the replicator makes some game-theoretic
move that repels copycats. For two reasons, however, this
explanation appears incomplete at best. First, there is little
empirical evidence that such maneuvers are prevalent. It
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JAN W. RIVKIN Reproducing Knowledge
has proven difficult to detect preemptive, game-theoretic
behavior in large-scale statistical tests (Lieberman 1987a,
1987b; Gilbert and Lieberman 1987; Ghemawat and
Caves 1986). Indeed, the most prominent and direct statistical analyses of imitation do not even examine preemption as a candidate barrier to imitation (Levin et al.
1987, Mansfield 1985, Zander and Kogut 1995).1
The second, more fundamental, reason that the gametheoretic explanation is incomplete is that it discounts the
difficulty of replication. A number of empirical studies
show that replication of success is costly and far from
automatic. Teece (1977), for instance, examines 26 international technology transfer projects. He finds that the
transmission and assimilation of unembedded technical
know-how accounts, on average, for 19% of project costs.
For four of the projects, the figure exceeds 40%.2 In a
study of multiplant commercial food operations, Chew et
al. (1990) find transfer of best practice so difficult that
the best plants within a company remain twice as productive as the worst, even after one controls for differences in technology, location, and plant size. Szulanski
(1996, fn. 1) reports that internal benchmarking efforts
commonly uncover such wide gaps. In sum, then, the
game-theoretic perspective does not provide a definitive
explanation for replication-without-imitation.
Access to Key Resources. Unlike industrial economists,
strategy theorists have typically acknowledged that replication of a successful strategy can be difficult (e.g.,
Collis and Montgomery 1997, pp. 67, 154–159). While
economists have focused on preemptive moves in product
markets, proponents of the resource-based view of strategy turn to factor markets in order to explore imitation
and replication. In this view, an imitator fails if it cannot
muster resources comparable to the incumbent rival
(Dierickx and Cool 1989, Barney 1991). For instance, the
imitator may have to make do with an inferior input (e.g.,
the second-best retail location in town), may find distribution channels already congested, may lack a brand that
customers value or trust, or may fail to have the complementary assets (Teece 1986) necessary to succeed.
Such factors, however, do not automatically make imitation harder than replication. In a new market, a replicator may have just as hard a time garnering inputs,
breaking into distribution channels, establishing a brand,
or assembling complementary assets as would an imitator. Indeed, difficulty in acquiring resources may be the
key constraint on internal firm growth (Penrose 1959).
With respect to some resources, the replicator and the
imitator start on the same footing when they enter a new
setting.
There are at least two resources to which the replicator
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
Table 1
Potential Barriers to Imitation and Replication
Potential Barrier
Barrier to Barrier to
Imitation? Replication?
Game-theoretic strategic maneuvers by incumbents
• Capacity preemption (Dixit 1980)
✓
Implicit
• Spatial preemption (Schmalensee 1978)
✓
assumption:
• Contractual preemption of customers/
✓
no barriers
switching costs (Aghion and Bolton
to replication
1987)
Unfavorable access to key resources
• Poor access to inputs
• Poor access to distribution
• Lack of brand
• Lack of complementary assets (Teece
1986)
• Inexperience with similar technology
• Patent protection
Organizational impediments or weaknesses
• Lack of absorptive or retentive capacity
(Teece 1977, Cohen and Levinthal 1990,
Szulanski 1996)
• Poor motivation of imitator (Porter 1996)
or replicator personnel (Szulanski 1996)
• Not-invented-here syndrome (Katz and
Allen 1982)
• Internal jealousies; arduous relation
between source and recipient (Szulanski
1996)
• Desire to preserve scarcity and tap
monopoly rents (Winter 1995)
Obstacles to effective search
• Causal ambiguity (Lippman and Rumelt
1982)
• Tacit nature of required knowledge
(Polanyi 1962, Winter 1987)
• Poor codifiability (Zander and Kogut
1995)
• Poor teachability (Zander and Kogut
1995)
• Complexity (Winter 1987, Rivkin 2000)
• Lack of template (Nelson and Winter
1982)
• Inability to observe in use (Winter 1987)
⻫
⻫
⻫
⻫
⻫
⻫
⻫
⻫
✓
✓
⻫
⻫
⻫
⻫
⻫
⻫
✓
✓
⻫
⻫
⻫
⻫
⻫
⻫
⻫
⻫
⻫
✓
⻫
✓
Bold check marks indicate barriers that may apply unequally to imitation and replication.
would have preferential access: prior production experience and patented knowledge. The “experience curve” is
widely touted to give established incumbents an advantage which is hard to imitate (Boston Consulting Group
1972, Porter 1980). Such experience, however, is often
277
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JAN W. RIVKIN Reproducing Knowledge
described as localized within a single installation, so it is
unclear just how experience aids replication elsewhere.
Moreover, empirical evidence concerning experience
seems equivocal. In his study of 39 chemical product industries, Lieberman (1989) finds no evidence that incumbent experience deters entry and only weak indications
that it increases entrant mortality.3 He concludes that the
benefits of experience may diffuse rapidly throughout
such industries, a conclusion echoed by others who examine different contexts (Mansfield 1985, Jaffe 1986,
Irwin and Klenow 1994). Likewise, patents guard against
imitation only in limited circumstances (Winter 1987).
Mansfield et al. (1981) find that 60% of patented innovations are imitated within four years and that patenting
raises the cost of imitation by a mere 6–11%.4 Reflecting
this, Levin et al. (1987) report that few R&D executives
consider patents an especially effective means to protect
the fruits of innovation. Lead time, learning curves, sales
or service efforts, and (in the case of process innovations)
secrecy are judged to be more effective. Patents may be
crucial in some industry contexts, but they tend to be
“invented around,” are unavailable for some technologies, and require disclosure of sensitive information. The
more recent survey results of Cohen et al. (2000) confirm
that executives deem patents crucial in only a handful of
industries—and often as bargaining chips in negotiations
or suits, not only as a means to prevent copying.
In sum, then, key resources do not always drive a decisive wedge between replicability and imitability. It may
be hard to gain access to resources in new settings, but
this is true for both the replicator and the imitator. And
two resources that offer the replicator preferential access—experience and patents—do not seem to pose uniformly high barriers to imitation. While resources may
account for some instances of replication-withoutimitation, it seems worthwhile to search for other explanations.
Organizational Impediments or Weaknesses. A number
of researchers have highlighted the organizational characteristics that induce or impede the transfer of good
ideas. An interlocking set of beliefs, values, measurement
systems, incentives, and staff functions must be in place
to enable smooth transfers (Chew et al. 1990; Szulanski
1996). The recipient of the transfer must have the ability
to absorb and retain what is conveyed (Teece 1977,
Cohen and Levinthal 1990, Szulanski 1996). In an internal transfer, the relationship between the source and the
recipient must be intimate and productive, and both
must be sufficiently motivated to complete the handoff
(Szulanski 1996); similarly, in an imitation effort, the external copycat must not have made prior choices or commitments that dampen its motivation to complete the
278
transfer successfully (Porter 1996). A replicator or imitator must overcome the tendency of an organization to
reject ideas that were “not invented here” (Katz and Allen
1982).
Though organizational considerations and motivation
may deter both replication and imitation, there are two
reasons to doubt that they drive a wedge between the ease
of replication and the difficulty of imitation. First, existing empirical work suggests that organizational impediments do not pose the most important barriers to either
replication or imitation. Szulanski (1996), for instance,
finds that knowledge-related factors, not motivational
considerations, form the major barriers to internal replication of best practices. In the quick print industry, Knott
and McKelvey (1999) find that efficiency depends far
more on access to successful routines than on the presence of high-powered incentives (though the role of incentives is sometimes statistically significant). Second,
along organizational and motivational dimensions, a replicator may actually face higher barriers to transfer than
an imitator does. Internal jealousies may impede replication while factions within an imitator may unite to face
a common, external enemy. Moreover, a replicator’s incentives to expand output are dulled: The replicator
knows that expansion will depress prices, including prices
on the volume it was selling before expansion. If imitation were not a threat, it might prefer to restrict output
and maintain high prices rather than expand and “compete against itself” (Winter 1987, 1995). The imitator, on
the other hand, was not selling any units before and therefore does not worry about depressing prices on preexpansion output. Its motives to expand production are
unmixed. Organizational impediments, then, do not offer
a compelling explanation for replication-withoutimitation.
Obstacles to Effective Search. An alternative perspective on replication and imitation views the transfer of productive knowledge—by either replication or imitation—
as a search process (Nelson and Winter 1982). The
imitator must discover, and the replicator must rediscover, the combination of routines, activities, and resources which made the original installation so successful.
Certain types of knowledge are inherently hard to
(re)discover because it is difficult to specify and communicate precisely where the original combination resides in the space of all possible routines, activities, and
resources. On the figurative treasure map, it is hard to
place the “X” that “marks the spot.” This difficulty may
arise because no one in an organization fully understands
the connection between firm actions and outcomes, a con-
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JAN W. RIVKIN Reproducing Knowledge
dition known as causal ambiguity (Lippman and Rumelt
1982, Reed and DeFillippi 1990). It may occur because
production calls on tacit individual actions or connections
among individuals that the involved individuals themselves do not consciously understand (Polanyi 1962) or
that cannot be codified or taught inexpensively (Zander
and Kogut 1995).5 Or, difficulty may arise because the
productive knowledge is complex.
“Complexity” demands further explanation. The definition of the term is the subject of vigorous, sometimes
strident debate among advocates and skeptics of “complexity theory” (Horgan 1995). By one accounting, researchers examining complexity employ 41 different definitions of the word.6 Within the narrow domain of
evolutionary and behavioral economics, however, the
term is used fairly consistently. Simon (1962) defines
complexity as having two aspects: An item is complex if
it consists of many elements and those elements interact
richly. Reflecting Simon’s first aspect, Winter (1987) follows Kolmogorov (1965) and defines the complexity of
an item of knowledge as the amount of information required to characterize it. Reflecting the second aspect,
Zander and Kogut (1995) argue that a piece of knowledge
is complex if it integrates many distinct competencies
within a firm. In this paper, I adopt Simon’s definition,
which encompasses Winter’s and Zander and Kogut’s. I
pay particular attention to Simon’s second aspect, the intensity of interactions among elements.
Of course, the fact that certain types of productive
knowledge require and resist search does not by itself
imply any wedge between replicability and imitability.
Knowledge that is causally ambiguous, tacit, uncodifiable, unteachable, or complex poses a challenge to both
voluntary and involuntary transfer. A number of researchers have argued that valuable pieces of knowledge or winning business strategies that consist of many interwoven
elements are difficult for outsiders to copy (Porter 1996,
Rivkin 2000 and references therein). But why should insiders be any more able to reproduce such successes?
A “basic proposition of evolutionary economics” is that
“replication of an established routine is much easier than
imitation . . .” (Winter 1995, p. 166). The crux of the
evolutionary argument is that, in searching to (re)discover
the successful combination, the replicator enjoys a key
advantage: It has full access to the original success which
serves as a template (Nelson and Winter 1982, Winter
1995). The template may be useful in a number of ways.
It may allow the replicator to start with a better facsimile,
to initiate its search in closer proximity to its ultimate
target. It may give the replicating managers a place to
turn when problems arise with the new installation. Or, it
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
may generate experienced personnel who can staff the
new operation or train their counterparts. As the replicator
makes a number of attempts to reproduce its original success, it gains access to multiple templates—some more
successful than others, in all likelihood. Through multiple
experiments, the replicator gradually comes to understand
what truly matters in the original success, what attributes
deserve to be replicated, and how these attributes can be
attained—an understanding that Winter and Szulanski
(2000) label the “Arrow core.”
A template may be an especially potent source of advantage when the roots of a replicator’s success cannot
be observed in use (Winter 1987), that is, when the external output of a productive system gives few clues about
the system’s internal composition. The concept of observability in use has traditionally been applied to discrete, technological innovations. Product advances, it is
reasoned for instance, are more readily observed and
reverse-engineered than process innovations. Accordingly, new products may be imitated more rapidly than
new processes.7 The idea of observability in use also applies to innovative strategies, however. The strategy of a
retailer such as White Castle is more visible to the public
than, say, that of the steel maker Nucor.
In sum, replication-without-imitation may arise when
(a) the nature of the productive knowledge necessitates
search but makes it challenging, and (b) the replicator’s
access to a template gives it an advantage in the search
process. Efforts to confirm this view empirically, though
rare, have been supportive.8 Alternative perspectives concerning strategic maneuvers, resource impediments, and
organizational considerations—while important—fall
short of a thorough explanation.
The remainder of the paper uses a simple simulation
model of managerial search to make the evolutionary argument more precise. Specifically, it explores a key question concerning the argument: Under what conditions of
search complexity and template validity is the gap between the ease of replication and the difficulty of imitation the widest? Put differently, can complexity drive a
wedge between replicability and imitability? An important feature of the model is that resource impediments and
organizational considerations—which can suppress both
imitation and replication even if they do not make imitation harder than replication—are suppressed. By assumption there is not, for instance, some truly unique resource that makes replication and imitation downright
impossible. Rather, the model creates a world—admittedly a stylized world—in which the success of replicators and imitators depends entirely on the character of the
knowledge they have and seek.
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3. Simulation of Replication and
Imitation
My simulation approach is an extension of the NK simulation model developed by Stuart Kauffman and his colleagues in the context of evolutionary biology (Kauffman
and Levin 1987, Kauffman et al. 1988, Kauffman and
Weinberger 1989, Kauffman 1993). Although devised to
explore how organisms and proteins evolve, the NK technique can be adapted to examine managerial search. A
number of researchers have used NK models to analyze
human organizations.9 Indeed, tools from complexity research are increasingly being applied to organization science (Anderson 1999). None of the research efforts to
date have examined the tension between replication and
imitation.
A significant virtue of the NK framework, for the present paper, is that it ties the hands of the modeler fairly
tightly. The modeler can manipulate the basic setup in
only a handful of ways, most of which have been vetted
by other researchers. Consequently, an NK simulation
can be viewed as an experiment in a “world” that others
have found reasonable—and importantly, not an idiosyncratic world tailormade by the modeler. This reduces the
fear that the model is rigged to produce the desired result.
The fear may linger, however. To address such concerns
below, I emphasize the intuition behind the results and
discuss the robustness of the results to alternative specifications.
The simulation unfolds as follows. First, I fix the complexity of the decision problem which modeled managers
will face; that is, I choose (a) the number of decisions
which together constitute a strategy and (b) the degree to
which those decisions interact with one another in determining firm performance. Using NK techniques described below, a computer then generates—in a stochastic
but well-controlled manner—a decision problem with the
specified level of complexity. The decision problem is a
particular mapping from choices to payoffs. Graphically,
the decision problem can be conceived of as a landscape
in a high-dimensional space. Each discrete decision constitutes a “horizontal” axis, and each possible choice concerning a decision is a point along that axis. The vertical
axis records the payoff from each combination of decisions, and the mapping from combinations to payoffs defines the landscape.10
Once a decision problem is generated, I assume that
one firm, Firm R (for “replicator”), has happened upon
the best solution to the problem, the highest point on the
landscape. The firm faces the challenge of replicating the
solution in a new installation. The managers of the new
installation are “released” on the landscape at a point near
280
the highest peak but, reflecting the previous section, not
precisely atop the summit. They then face the problem of
rediscovering the solution. At the same time, a second
organization, Firm I (for “imitator”), tries to imitate Firm
R’s original success. That is, it tries to reproduce the set
of choices that led Firm R to be so successful initially.
Because the imitator lacks a template, however, its search
begins from a more distant point and proceeds with less
accuracy. Though the landscape was generated by a random process, it is fixed and determined from the perspective of Firms R and I.
The computer records the relative success of the replicator and the imitator in reproducing the original success
and solving the decision problem. It then generates another decision problem which, though it differs in its particulars, has the same degree of complexity. A second pair
of firms—replicator and imitator—tackle the second
problem. This process is repeated hundreds of times.
From the repetition emerges a profile of how replicators
and imitators fare relative to one another for a given degree of complexity. I then adjust the parameters that govern complexity and repeat the process. By doing so, I
gradually build up an understanding of how complexity
affects the gap between replicability and imitability.
To describe the simulation thoroughly, I next explain
how decision problems are generated and how modeled
managers search for better choices. Table 2 summarizes
the simulation parameters and symbols.
3.1. Generation of Decision Problems
Two parameters, N and K, govern the complexity of a
modeled firm’s decision problem. N is the first aspect of
complexity, the number of decisions that a firm must
make. Each decision j, j 僆 {1, 2, . . ., N}, can be resolved
in two ways. Hence a particular strategy s, a configuration
of decisions, is an N-vector {s1, s2, . . ., sN}, with sj 僆 {0,
1}, from the space of all possible configurations S. Note
that |S| ⳱ 2N. K controls the second aspect of complexity,
the degree to which the decisions interact. The efficacy
of each decision is affected not only by the choice (0 or
1) made concerning that decision, but also by the choices
regarding K other decisions. In the model, each decision
j makes a contribution Cj to overall firm value. Cj, a number between 0 and 1, depends not only on the resolution
of decision j itself (sj), but also on how K other randomly
assigned decisions are resolved: Cj ⳱ Cj(sj; sj1, . . ., sjK).
K ranges from 0 to N ⳮ 1.
N reflects the reality that managers must make numerous decisions. They must decide, for instance, whether to
make an input internally or purchase it in the marketplace;
whether to install flexible production equipment or commit to specialized machinery; whether to manufacture a
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JAN W. RIVKIN Reproducing Knowledge
Table 2
Simulation Parameters and Symbols
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Parameters
N
K
r
i
hf
Symbols
s
S
Cj
P(s)
s*
s†
The number of decisions which the modeled firm must make. All decisions are binary (0/1)
The number of decisions, other than the focal decision, whose resolution (0 or 1) affects the contribution of the focal
decision to firm payoff. Ranges from 0 to N ⳮ 1
The number of decisions in common between the replicator’s initial s and the benchmark s*
The number of decisions in common between the imitator’s initial s and the benchmark s*
The probability that, in a long jump, firm f successfully matches s*j , an individual element of s*. f ⳱ R for the
replicator and I for the imitator
An N-digit string of zeroes and ones, representing a full set of choices
The space of all possible choices
The contribution of decision j to firm payoff. For each resolution of decision j and the K randomly-assigneddecisions
affecting decision j, Cj is drawn at random from a uniform distribution between 0 and 1
The payoff of a firm which adopts decisions s. An average over the contributions of the N decisions
The combination of choices which yields the highest possible payoff
The combination of choices which yields the second-highest possible payoff
diverse product line or focus on a handful of products;
whether to organize by function or by product, etc. The
firm’s strategy is embodied in this nexus of choices. It
may seem odd to represent a firm’s strategy as a string of
N zeroes and ones; such a string feels linear while strategies are holistic, with component decisions intertwined.
Adding connections across decisions is precisely the role
of K, the second parameter in the model.
K captures the fact that the choice made concerning
one decision may affect the marginal benefit or cost associated with another decision.11 For instance, investments in machines that permit production of a complex
product line are made more valuable on the margin if a
firm also employs a highly trained salesforce that can promote the product line effectively. The marginal value of
product inspection activities is reduced by manufacturing
activities that eliminate defects in the production process.
The marginal cost of a no-questions-asked merchandise
return policy is lowered for a retailer whose logistics system is configured to handle returns easily. A number of
recent studies document such interactions.12 Choices often complement one another or substitute for each other.
As described below, these interactions constrain search
processes in important ways.
A payoff is assigned to each of the 2N combinations of
decisions as follows. Recall that the contribution of each
decision to overall firm value, Cj, is affected by K other
randomly assigned decisions. For each possible realization of (sj; sj1, . . ., sjK), a contribution Cj is drawn at
random from a uniform distribution between 0 and 1. The
overall payoff associated with a configuration is the average over the N contributions:
N
P(s) ⳱
冤 兺 C (s ; s , . . . , s )冥冫N.
j
j
j1
jK
j⳱1
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
Note that when K ⳱ 0, P(s) is the average over N contributions, each of which depends only on a single choice.
At the other extreme, when K ⳱ N ⳮ 1, P(s) is the
average over N contributions, each of which is affected
by all N choices.
3.2. Managerial Search
Once the modeler fixes N and K and the computer generates a particular decision problem (that is, a payoff P(s)
for each of the 2N possible decision configurations), a
modeled replicator and imitator search for good solutions
to the problem. Firm R, the replicator, is assumed to have
discovered a very effective solution, a high point on the
landscape, in a previous period. This solution serves as a
target for the replicator and the imitator. In particular, I
assume that the replicator managed previously to attain
the very best solution to the decision problem, the global
peak, s*. (The computer finds this optimum by exhaustively searching the 2N possible configurations.) For all
the reasons discussed above, the replicator cannot simply
write down the combination of decisions that constitutes
s*. Rather, the firm has only imperfect knowledge of the
decisions that underpin the original success. Starting with
this knowledge, it must search to rediscover the winning
formula. At the same time, an imitating Firm I begins its
search to match the success.
An important aspect of the decision problems generated by this version of the NK approach is that they are
difficult to solve using any optimization algorithm. Formally, for K ⬎ 2, these NK problems are NP-complete.
That is, there exists no algorithm that can find the global
optimum for such decision problems in polynomial time.
(See Rivkin 2000 for further explanation.) For this reason,
it is sensible to assume that replicating or imitating managers use search heuristics—not some algorithm—to
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JAN W. RIVKIN Reproducing Knowledge
(re)discover successful combinations of choices.
I explore two polar types of heuristic: incremental improvement and follow-the-leader long jumps. When engaging in incremental improvement, a firm considers
changing only one decision at a time. Starting at configuration s, the firm examines all N of the alternatives which
involve changing just one decision. It accepts a considered alternative sⴕ if P(sⴕ) ⬎ P(s). Having adopted sⴕ, it
then considers another incremental change, and so forth
until the firm reaches a set of decisions which offers no
more incremental improvement. Note that such a set may
very well constitute a local peak, not the global optimum.
When taking a follow-the-leader long jump, the firm at
s attempts a major reconfiguration toward the target s*.
That is, it tries to adjust each of its N decisions to match
the original success. No longer does it change only one
decision at a time. Because the firm (either replicator or
imitator) cannot discern the precise configuration of the
success, the probability that each decision is matched correctly is h ⱕ 1. hN, then, is the probability that a firm
leaps to precisely s*. In the simulations below, I sometimes couple incremental improvement and long jumps:
A firm moves incrementally uphill, then leaps toward s*,
creeps uphill again, and so forth.
Because it has access to a template, the replicator has
an advantage over the imitator during the search process.
I consider three specific benefits. First, the replicator may
initiate its search in closer proximity to s*. It resolves r
of its decisions in the same way as s* at the outset of its
search, while the imitator shares only i with s*. N ⱖ r ⬎
i ⱖ 0. This advantage reflects the fact that the replicator’s
initial attempts at transfer will be more accurate than the
imitator’s. Second, h may be higher for the replicator than
it is for the imitator. This captures the ability of the replicator to examine the template closely and turn to experienced personnel as it tries to improve its operations.
Third, when taking a long jump, the replicator may know
which of its decisions are already accurately replicated;
the imitator, in contrast, might not know what it is doing
“right” and what it is doing “wrong.” The template guides
the replicator to focus on the right issues.
In the simulation, then, the replicator is simply endowed with a search advantage over the imitator. Clearly,
this simplifies a much more complicated process in which
both replicator and imitator decide how much to “invest”
in understanding the original success. My presumption is
that, on the margin, information about the template is less
costly to the replicator and, accordingly, the replicating
firm emerges with an information advantage. I return to
this issue—that r, i, and h may be endogenous to some
degree—in the final section.
After the replicator and imitator complete their search
282
efforts, the computer records the success of each—both
the performance level P(s) that each attains and the similarity between the final s and the target s*. The computer
then generates a second, altogether new decision problem. This new problem reflects the same levels of N and
K, but is based on a new set of random draws for the Cjs
and a new set of particular interactions. A second replicator and second imitator tackle the problem, and their
relative success is again recorded. This process continues
until 100 to 200 decision problems, all with identical levels of complexity, have been explored. The modeler then
adjusts N and K to alter the level of complexity; examines
replicator and imitator success on 100–200 decision problems with the new level of complexity; alters N and K
again; and so forth. In this way, one gradually isolates
the effect of complexity on the gap between replication
and imitation.
Note that a firm’s payoff is determined solely by its
own decisions. I permit no interplay at all between the
payoff of the replicator and that of the imitator. For instance, I do not penalize the firms for adopting similar
configurations of decisions. Thus, any benefit that the replicator enjoys comes from its advantages in the search
process, not from some preemptive maneuver. In addition, if the replicator and the imitator happen to make the
same configuration of decisions, they attain identical payoffs. Thus the replicator has no resource advantage that
allows it to make more profit than the imitator can from
a particular set of choices.
It may seem odd that the simulation uses the global
peak as the target configuration of decisions. Given the
complexity of the decision problem, how could the replicator or imitator know for sure that the old installation
constitutes the best possible outcome and therefore warrants duplication? In fact, the global peak is used purely
for convenience. All that is necessary is that the replicator
has superior information about some good (not specifically the best) resolution to the decision problem. For
instance, one can alter the simulation so that the target s*
is not the global peak, but the best peak discovered by
some number of prior random searches. As §4.3 mentions, this alternative arrangement does not change the
results qualitatively.
My tack is to examine how the benefits of search advantages shift as K changes, that is, as the underlying
decisions become more or less intertwined. To grasp the
intuition behind the results, however, one must first understand how K affects the topography of the underlying
landscape.
3.3. Interactions and the Landscape
A key insight is that changes in interactivity (K) fundamentally alter the character of the typical landscape.
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Kauffman (1993) explores the effects of interactions in
depth, and Levinthal (1997) and Rivkin (2000) do the
same in the context of business strategy. Here, I summarize three germane effects.
First, as K increases, the landscape shifts from being
smooth and single-peaked to being rugged with numerous
local peaks. The extreme cases of K ⳱ 0 and K ⳱ N ⳮ
1 illustrate this point. When K ⳱ 0, the N decisions make
independent contributions to firm payoff. In that situation,
alteration of a single decision changes the contribution of
that decision alone. From any initial location on a landscape, then, a replicating or imitating firm can rise to the
global peak via a series of value-improving, singledecision “tweaks” to its strategy. Management simply adjusts each choice, one by one, to the option that allows
that decision to make the greater contribution in isolation.
In contrast, when K ⳱ N ⳮ 1, every decision influences
the contribution of every other choice. Then a small step
on the landscape—a change in a single decision—alters
the contributions of all N elements. Consequently, adjacent decision configurations have elevations that are altogether uncorrelated with one another. A particular configuration is a local peak if all N of its neighbors are lower
than itself, and because elevations are random when K ⳱
N ⳮ 1, this occurs with probability 1/(N Ⳮ 1). Of the 2N
possible strategies, 2N/(N Ⳮ 1) are expected to be local
peaks. This number is large for even modest N, surpassing
100 for N ⳱ 11 and 10,000 for N ⳱ 18. More generally,
as K increases, the expected correlation between P(s) and
P(sⴕ)—where s and sⴕ differ in how one decision is resolved—falls toward zero. As a consequence, the number
of local peaks rises. Each of these local peaks is internally
consistent, in the sense that, starting from such a locale,
no tweak to a single decision can boost performance.
“Internally consistent,” however, is not the same as
profitable, and this highlights the second effect of interactions. As K rises, not only do local peaks become more
numerous, but the height of the average peak declines.
As the web of connections among decisions thickens, it
becomes possible for a firm to run out of opportunities
for incremental improvement even at low levels of performance. When K is high, a firm can easily become
trapped in an internally consistent but unfavorable set of
choices. A manager in charge of, say, one of the choices
has no incentive to break the organizational truce (Nelson
and Winter 1982) even though an altogether different arrangement might lead to better performance. Interactions,
then, constrain the search for incremental improvement.
Finally, as interactions proliferate, the high peaks on
the typical landscape spread apart from one another “horizontally.” On a landscape with a low level of K, high
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
peaks usually arise near each other, in “mountain ranges;”
for instance, if one examines the decision configuration
s† that constitutes the second-highest peak, the vector
{s†1, s†2, . . ., s†N} will closely resemble the global peak s*
⳱ {s*,
On a landscape with high K, in
1 s*,
2 . . ., s*}.
N
contrast, favorable sets of decisions are often far apart; s†
may look little like s*. The intuition behind this is
straightforward. When choices are loosely connected, the
strategy of a successful firm can typically be decomposed
into separable subsystems of well-made choices (Simon
1962). Reconfiguring one of the subsystems has limited
effect on the others and generates a new local peak that
is quite close in decision space to the original one. Starting with a very good set of choices, one might, for example, rearrange just the marketing choices while leaving
production, R&D, and logistics decisions unchanged, and
this could lead to a second, internally consistent, effective
configuration. In contrast, when choices are tightly interlocked, the strategy of a successful firm cannot be broken
into subsystems. High peaks are then likely to be altogether different ways of doing business rather than slight
variations on a theme. In such a setting, a change in the
marketing function, for instance, will necessitate adjustments throughout the entire value chain.
In sum, then, interactions reshape the landscapes on
which replicators and imitators search. Simulations show
how these effects alter the value of the replicator’s template, and hence the advantage which the replicator enjoys.
4. Simulation Results
In this section, I examine the replicator’s advantage when
searches proceed by incremental improvement and by
follow-the-leader long jumps. The pattern that emerges is
this: Under a wide range of assumptions, the wedge between replicability and imitability is widest at moderate
levels of K, i.e., at intermediate degrees of complexity.
4.1. Incremental Improvement
First, suppose that the replicator and imitator can
(re)discover the best solution only by means of incremental search. The replicator is at an advantage because
it begins its search closer to the benchmark configuration
of decisions; r ⬎ i. The size of the benefit that derives
from this advantage, however, depends on K.
The four panels of Table 3 show this dependence on K
for various combinations of r and i. (N ⳱ 12 throughout
this section, and reported results are averages over 200
landscapes. Results focus on the final performance attained by each firm, but results for interim performance
are qualitatively similar; see §4.3 below.) When K ⳱ 0,
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JAN W. RIVKIN Reproducing Knowledge
the replicator has no advantage at all. The landscape is
smooth, and both firms discover the global peak eventually. As K rises a little, a gap emerges between the replicator’s performance and the imitator’s. The imitator gets
stranded on local peaks far from the best strategy while
Firm R comes close to complete replication. As K approaches N, however, the replicator starts to founder as
well. Like the imitator, it gets trapped on local peaks
away from the benchmark. Its local peak is closer to the
benchmark than the imitator’s peak (Column 3 vs. Column 5), but because the high peaks are spread apart, it
does little or no better than the imitator.
As Table 4 shows, the replicator’s advantage is often
largest at levels of K well below the maximum value of
N ⳮ 1. Simple decision problems stymie neither firm,
and highly complex problems resist both imitation and
replication. Decision problems with moderately intertwined choices, however, make the replicator’s template
especially valuable. Only when the replicator can reproduce the original success with very high fidelity (r close
to N) is the replicator best off with a maximally intertwined set of choices. For a fixed level of i, the following
pattern emerges: The greater is the replicator’s advantage
with respect to the template (r ⳮ i), the more complex
the replicator would like the underlying set of decisions
to be.
4.2. Follow-the-Leader Long Jumps
Similar results emerge when the two firms start with the
same initial transfer (r ⳱ i), but leap toward the benchmark with different degrees of accuracy (hR ⬎ hI). The
intuition behind the similarity is straightforward. Consider a model in which the firms start equally far from
the target (r ⳱ i ⳱ 0), leap toward the original success
with differential accuracy (hR ⬎ hI), and then inch uphill
to a local peak. Results for such a model are identical to
one with r ⳱ hR N ⬎ i ⳱ hI N. Table 5 illustrates the
similarity for two combinations of hR and hI. (Results here
and below are averages over 100 landscapes.)
In leaping toward the benchmark, Firm R may enjoy
an advantage beyond greater accuracy. Because it has access to a template, the replicator may be better able to
identify which of its decisions differ from the benchmark
and which are already accurately duplicated. When decisions are tightly connected to one another, distinguishing what one is doing “right” from what one is doing
“wrong” is no trivial matter. The ability to tell right from
wrong may give the replicator a substantial edge.
Table 6 illustrates this effect. In the simulation shown
there, the replicator and imitator start out with equally
poor replicas (r ⳱ i ⳱ 0) and the two are equally inac284
curate in their leaps (hR ⳱ hI ⳱ 0.5). Each firm tweaks
uphill, takes a leap, then moves uphill again to a (possibly
local) peak. In making its leap, however, only the replicator knows which choices are identical to the benchmark. The replicator tries to alter only the decisions that
are misaligned with s*, while the imitator experiments
with all N choices. The results show a familiar pattern.
When K ⳱ 0, both replication and imitation are thoroughly successful. When K ⳱ N ⳮ 1, both efforts are
stymied by the complexity of the task. The replicator’s
advantage is greatest at an intermediate value of K well
below the maximum of N ⳮ 1.
4.3. Robustness of the Result
The main result, that the replicator’s advantage over the
imitator is greatest at an intermediate level of interaction,
holds up over a broad range of assumptions. Results
available from the author show that this pattern remains
when N is higher or lower than the value explored here;
when the benchmark s* is set by taking the best of numerous search results rather than placing it atop the global
peak; when each choice offers more than two options;
when contributions Cj are drawn from distributions other
than the uniform distribution assumed above; and when
the assumption that all decisions are ex ante equally influential is altered.
The findings reported so far focus on the final performance of each firm, after it has completed its search. If
search efforts stretch over a long time, however, it may
be important to consider performance during the entire
process, not just in the final period. Imitators initiate their
search efforts farther from the global peak than replicators
do. Consequently, on smooth landscapes (low K), imitators wander in the “lowlands” longer than replicators do.
This effect will widen the gap between replicator performance and imitator performance at low K, but will have
a less pronounced effect on the gap at high K, when local
peaks are numerous and searches brief. At least conceptually, this could create a situation in which the performance gap is widest at K ⳱ 0—contrary to the paper’s
main hypothesis. To test this possibility, I examined performance over the entire search process and discounted
the payoffs back to Period 0 using a discount rate as high
as 100%. This reduced the degree of complexity at which
the performance differential is greatest, as expected, but
not to K ⳱ 0.
The results of virtually any simulation model can be
overturned if one changes one’s assumptions radically
enough. The main result fails to hold under two alternative sets of assumptions. First, if Firm R is able to replicate its initial success perfectly (r ⳱ N) but can keep its
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Table 3
Results for Incremental Improvement
Description: In Panel A, the replicator initially shares eight decisions with the benchmark set of decisions (r ⳱ 8) and the imitator shares two
(i ⳱ 2). Both firms are released on a landscape, and each is allowed to make incremental improvements until arriving at a (possibly local)
peak. N ⳱ 12 and K varies. For each level of K, the panel reports the portion of the benchmark performance eventually attained by each firm
and the “distance” of the final location from the benchmark (i.e., the number of decisions not resolved in identical fashion). The reported
figures are the average (and the standard deviation) over 200 landscapes. The significance level of a test that the replicator’s average and
the imitator’s average do not differ is also reported. The level of K that leads to the greatest performance differential is shown in bold.
Panels B, C, and D report results for other values of r and i.
Panel A: r ⳱ 8, i ⳱ 2
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
99.3 (1.8)
97.8 (3.5)
96.3 (4.6)
93.8 (5.3)
92.4 (6.3)
90.0 (5.9)
88.0 (5.7)
87.6 (5.8)
86.0 (5.7)
85.3 (5.5)
81.1 (4.6)
0.0 (0.0)
0.7 (1.5)
1.8 (2.2)
2.4 (2.4)
3.6 (2.3)
3.8 (2.3)
4.3 (1.9)
4.6 (1.6)
4.6 (1.6)
4.5 (1.3)
4.5 (1.2)
4.4 (1.2)
100.0 (0.0)
95.2 (4.3)
91.8 (5.6)
90.6 (5.7)
89.8 (6.0)
89.2 (5.5)
88.5 (6.0)
87.4 (5.5)
87.0 (5.7)
85.9 (5.7)
84.9 (5.4)
81.0 (4.6)
0.0 (0.0)
4.0 (2.6)
6.9 (2.2)
7.6 (1.6)
8.1 (1.4)
8.1 (1.3)
8.4 (1.3)
8.6 (1.3)
8.9 (1.3)
8.9 (1.1)
8.8 (1.1)
9.1 (1.0)
0.0
4.1 ***
6.1 ***
5.7 ***
3.9 ***
3.2 ***
1.4 ***
0.6
0.6
0.1
0.5
0.2
Panel B: r ⳱ 10, i ⳱ 6
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
99.9 (0.7)
99.8 (1.0)
99.3 (2.4)
99.0 (2.7)
98.4 (4.0)
96.8 (5.2)
94.9 (6.6)
93.7 (7.5)
90.5 (8.0)
89.2 (8.3)
84.9 (7.5)
0.0 (0.0)
0.1 (0.5)
0.1 (0.6)
0.3 (1.0)
0.6 (1.4)
0.6 (1.4)
1.2 (1.8)
1.5 (1.8)
1.9 (1.9)
2.3 (1.8)
2.4 (1.7)
2.8 (1.4)
100.0 (0.0)
98.1 (2.9)
95.1 (4.9)
92.6 (5.4)
90.4 (6.5)
89.5 (5.8)
88.8 (5.7)
87.0 (5.7)
86.4 (5.7)
85.7 (6.0)
84.9 (5.8)
81.6 (4.4)
0.0 (0.0)
1.5 (1.7)
3.5 (2.5)
4.8 (2.5)
5.6 (1.9)
5.9 (1.8)
5.9 (1.6)
6.2 (1.4)
6.0 (1.4)
5.9 (1.3)
5.9 (1.2)
6.0 (1.2)
0.0
1.8 ***
4.7 ***
6.8 ***
8.6 ***
8.9 ***
8.0 ***
8.0 ***
7.3 ***
4.8 ***
4.3 ***
3.3 ***
*** Performance differential significant at the 1% level.
perfect knowledge to itself (r ⬎ i), then its advantage
simply grows larger and larger as the complexity of the
underlying problem increases. Greater complexity then
undermines the efforts of the imitator, but does no harm
to the replicator. Second, if the replicator and imitator
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
have equal access to and knowledge of the template (i.e.,
r ⳱ i, hR ⳱ hI, and the firms have equal knowledge of
what they are doing right), then the replicator enjoys no
advantage regardless of the degree of complexity. Neither
of these alternatives seems likely to arise often in reality.
285
JAN W. RIVKIN Reproducing Knowledge
Table 3 (cont.)
Results for Incremental Improvement
Panel C: r ⳱ 6, i ⳱ 0
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Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
98.1 (2.9)
95.1 (4.9)
92.6 (5.4)
90.4 (6.5)
89.5 (5.8)
88.8 (5.7)
87.0 (5.7)
86.4 (5.7)
85.7 (6.0)
84.9 (5.8)
81.6 (4.4)
0.0 (0.0)
1.5 (1.7)
3.5 (2.5)
4.8 (2.5)
5.6 (1.9)
5.9 (1.8)
5.9 (1.6)
6.2 (1.4)
6.0 (1.4)
5.9 (1.3)
5.9 (1.2)
6.0 (1.2)
100.0 (0.0)
93.4 (5.8)
91.3 (5.6)
90.3 (5.6)
90.4 (5.3)
89.1 (6.0)
88.5 (5.9)
87.4 (6.2)
86.4 (5.6)
85.9 (5.7)
85.2 (5.9)
81.5 (4.1)
0.0 (0.0)
4.6 (2.8)
7.6 (2.0)
8.5 (1.4)
8.9 (1.3)
9.3 (1.2)
9.6 (1.1)
9.7 (1.1)
10.1 (1.1)
10.2 (1.0)
10.3 (1.0)
10.5 (0.9)
0.0
4.7 ***
3.8 ***
2.2 ***
ⳮ0.1
0.4
0.3
ⳮ0.4
0.0
ⳮ0.2
ⳮ0.3
0.0
Panel D: r ⳱ 10, i ⳱ 2
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
99.9 (0.7)
99.8 (1.0)
99.3 (2.4)
99.0 (2.7)
98.4 (4.0)
96.8 (5.2)
94.9 (6.6)
93.7 (7.5)
90.5 (8.0)
89.2 (8.3)
84.9 (7.5)
0.0 (0.0)
0.1 (0.5)
0.1 (0.6)
0.3 (1.0)
0.6 (1.4)
0.6 (1.4)
1.2 (1.8)
1.5 (1.8)
1.9 (1.9)
2.3 (1.8)
2.4 (1.7)
2.8 (1.4)
100.0 (0.0)
95.2 (4.3)
91.8 (5.6)
90.6 (5.7)
89.8 (6.0)
89.2 (5.5)
88.5 (6.0)
87.4 (5.5)
87.0 (5.7)
85.9 (5.7)
84.9 (5.4)
81.0 (4.6)
0.0 (0.0)
4.0 (2.6)
6.9 (2.2)
7.6 (1.6)
8.1 (1.4)
8.1 (1.3)
8.4 (1.3)
8.6 (1.3)
8.9 (1.3)
8.9 (1.1)
8.8 (1.1)
9.1 (1.0)
Performance
differential
0.0
4.7 ***
8.1 ***
8.7 ***
9.1 ***
9.2 ***
8.3 ***
7.6 ***
6.7 ***
4.6 ***
4.3 ***
3.9 ***
***Performance differential significant at the 1% level
5. Conclusions
This paper takes up a deceptively simple question: What
distinguishes situations in which replication and imitation
are equally easy, those in which the two are equally hard,
and those in which the replicator fares better than the
imitator? Why do the situations of, say, White Castle,
Marks & Spencer, and Southwest Airlines differ? I argue
that strategic, resource-based, and organizational perspectives—as important as they are—do not shed bright
light on this particular question,13 and I focus instead on
managerial search for knowledge that is difficult to trans-
286
fer. The particular impediment to search which I examine
in depth is the complexity of the decision problem facing
both replicator and imitator.
One might initially suspect “the more complex the better” from the point of view of the firm which has the
original success. And indeed, decision problems which
are more complex do resist imitation more staunchly
(Rivkin 2000). Unfortunately, they also pose a stiffer
challenge to replication. The replicator’s relative advantage is greatest with respect to decision problems of intermediate complexity. It is here that the replicator’s
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
JAN W. RIVKIN Reproducing Knowledge
Interaction Level that Maximizes Replicator’s
Relative Advantage
Description: For each level of r and i, the table reports the level of K
that generates the widest gap between the replicator’s performance
and the imitator’s performance. Performance is indicated by the
portion of P(s*) attained in simulations similar to the ones described
in Table 3.
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Table 4
i⳱
i⳱
i⳱
i⳱
i⳱
i⳱
0
2
4
6
8
10
r⳱2
r⳱4
r⳱6
r⳱8
r ⳱ 10
r ⳱ 12
1
1
1
1
2
2
2
2
2
3
5
5
4
5
7
11
11
11
11
11
11
informational edge, which comes from its preferential access to the original success, confers the greatest benefit.
In such a regime, landscapes are rugged enough that imitating firms with poor information get trapped on low
and distant local peaks. Landscapes are smooth enough
that perfect information about the benchmark is not necessary for acceptable replication. And high peaks cluster
together, implying that even imperfect information about
the location of the benchmark gives a valuable clue as to
where other high peaks may lie.
In essence, this paper examines the value of superior
yet imperfect information about good solutions to complex problems. Facing simple problems, both replicator
and imitator generate good information through their incremental search efforts. The replicator’s initial informational advantage does not last for long or do it much
good. At high levels of complexity, when decisions depend on one another in an extremely delicate way, small
errors in information spoil replication attempts altogether.
The replicator’s slightly imperfect knowledge then has
little more value than the imitator’s highly imperfect
knowledge. At moderate levels of complexity, the replicator’s initial information guides it to a good set of decisions, and the imitator does not recreate the information
through its ordinary search efforts.
Viewed in this way, the results of the paper have general implications for scholars who focus on the value and
management of knowledge. A growing number of management scholars contend that knowledge has become a
decisive competitive resource (e.g., Grant and Spender
1996, Grant 1996, Nonaka and Takeuchi 1995). Indeed,
knowledge is sometimes held up as the ultimate leveragable resource: Knowledge is often difficult for outsiders to observe and copy, and it is enhanced, not con-
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
sumed, in use. The results developed here suggest that
some kinds of knowledge are more likely than others to
serve as a source of sustained advantage. Specifically,
knowledge that is moderately complex lends itself to being replicated without being imitated. There are doubtlessly other dimensions and characteristics that delineate
competitively useful knowledge from less valuable
knowledge. This paper focuses on just one aspect of what
makes knowledge a potential source of advantage.
Though the discussion has focused on the replication
and imitation of entire business strategies, the core argument concerning the value of superior yet imperfect
information also applies to productive systems of smaller
scale. Consider, for instance, a firm with a single, highly
productive factory. If economies of scale are modest,
transportation costs high, demand geographically dispersed, and raw inputs available at multiple locales, then
the firm may wish to build replicas of the factory in numerous locations. In such a setting, the arguments of this
paper should apply: successful replication without imitation would be most likely when choices concerning the
design and operation of the template factory are modestly
intertwined. Since the inner workings of a factory are
often difficult for an outsider to deduce, the advantage
that the replicator gets from superior access to the template might be especially great in such a context.
The analysis in the paper suggests three hypotheses that
could be tested in real settings. First, replicators will have
the greatest advantage over imitators when underlying decisions are neither tightly intertwined nor unrelated. A
business strategy that requires replication (or, for that
matter, any productive item of knowledge that requires
replication) is most likely to generate sustained competitive advantage if it is of moderate complexity. The second hypothesis is that replication and imitation will both
ensue quickly when decisions are largely unrelated and
that neither will succeed (or perhaps even be attempted)
when decisions are very intensely connected. Under this
interpretation, one of White Castle’s problems was that
the elements of its strategy were only loosely connected
to one another; the turrets had little to do with the limited
menu, and both were unconnected to the emphasis on
hygiene. (The fact that its strategy was easily observed,
i.e., i ⬇ r ⬇ N, also played a role in its difficulties, of
course.) Similarly, the intricacy of Marks and Spencer’s
original British strategy made it difficult to reproduce
abroad. The third and final hypothesis comes from the
analysis of Table 4. For a given level of i, the degree of
complexity that maximizes the replicator’s advantage
rises in line with the replicator’s informational edge (r vs.
i). Put differently, the greater is a firm’s informational
superiority, the more complex it would like its strategy
287
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JAN W. RIVKIN Reproducing Knowledge
Table 5
Results for Follow-the-Leader Long Jumps with Different Degrees of Accuracy
Description: A replicator and an imitator are released on a landscape with equally poor initial positions (r ⳱ i ⳱ 0). Each firm leaps toward
the benchmark set of decisions and then makes incremental improvements until arriving at a (possibly local) peak. The replicator leaps
toward the benchmark with greater accuracy than does the imitator; hR ⳱ 0.8 and hI ⳱ 0.2 in Panel A, and hR ⳱ 0.6 and hI ⳱ 0.4 in Panel
B. N ⳱ 12 and K varies. For each level of K, the panel reports the portion of the benchmark performance eventually attained by each firm
and the “distance” of the final location from the benchmark (i.e., the number of decisions not resolved in identical fashion). The reported
figures are the average (and the standard deviation) over 100 landscapes. The significance level of a test that the replicator’s average and
the imitator’s average do not differ is also reported. The level of K that leads to the greatest performance differential is shown in bold.
Panel A: hR ⳱ 0.8, hI ⳱ 0.2
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
99.6 (1.5)
99.4 (2.1)
98.5 (3.5)
97.7 (4.0)
97.1 (5.1)
94.3 (7.3)
93.3 (7.4)
92.8 (7.6)
91.6 (8.4)
90.4 (8.6)
86.6 (8.7)
0.0 (0.0)
0.3 (1.0)
0.5 (1.6)
0.9 (1.8)
1.3 (2.0)
1.6 (2.5)
2.0 (2.4)
2.6 (2.6)
2.5 (2.3)
2.5 (2.4)
2.7 (2.4)
3.0 (2.0)
100.0 (0.0)
95.3 (5.0)
92.7 (5.7)
91.3 (5.4)
90.2 (6.8)
89.0 (5.5)
88.0 (5.2)
87.0 (5.6)
87.0 (5.9)
86.1 (5.9)
84.4 (4.7)
80.8 (4.1)
0.0 (0.0)
3.6 (2.5)
6.6 (2.6)
7.5 (1.8)
7.6 (1.8)
8.1 (1.5)
8.3 (1.5)
8.3 (1.5)
8.8 (1.5)
8.6 (1.6)
8.4 (1.2)
8.5 (1.5)
0.0
4.4 ***
7.1 ***
7.2 ***
7.5 ***
8.1 ***
6.3 ***
6.3 ***
5.8 ***
5.5 ***
6.0 ***
5.8 ***
Panel B: hR ⳱ 0.6, hI ⳱ 0.4
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
98.5 (2.4)
97.2 (4.4)
94.7 (5.5)
92.6 (6.9)
91.0 (7.0)
91.3 (6.0)
88.4 (7.0)
88.3 (6.2)
86.6 (6.3)
86.5 (6.4)
83.1 (7.3)
0.0 (0.0)
1.4 (2.0)
2.3 (2.8)
3.6 (2.9)
4.0 (2.7)
4.3 (2.7)
4.7 (2.6)
4.8 (2.3)
4.9 (2.0)
5.1 (2.1)
5.1 (2.0)
4.9 (2.0)
100.0 (0.0)
96.7 (4.6)
93.4 (6.4)
91.7 (6.1)
90.7 (6.0)
89.3 (5.5)
89.1 (5.7)
87.1 (5.4)
87.6 (5.3)
86.0 (5.3)
85.7 (5.2)
81.6 (4.6)
0.0 (0.0)
2.8 (2.7)
5.0 (2.9)
5.4 (2.5)
6.3 (2.3)
6.7 (1.7)
6.7 (1.9)
6.7 (1.7)
7.0 (1.8)
6.8 (1.6)
6.7 (1.7)
7.0 (1.9)
0.0
1.8 ***
3.8 ***
3.1 ***
1.9 **
1.7 **
2.2 ***
1.3 *
0.7
0.6
0.8
1.4 *
***Performance differential significant at the 1% level
**Performance differential significant at the 5% level
*Performance differential significant at the 10% level
to be (assuming that its goal is to perform better than its
competitors). All three hypotheses can be tested in principle, though the practical challenges of performing the
tests should not be discounted.14
The analysis offers an alternative interpretation for a
288
prevalent observation in organization theory. So-called
“loosely coupled” organizations, with only modest connections among decision-making elements, are widely
said to be especially effective competitors (Weick 1976,
Page-Jones 1980, pp. 101–103, Peters and Waterman
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
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JAN W. RIVKIN Reproducing Knowledge
Table 6
Results for Follow-the-Leader Long Jumps with Different Knowledge of What Is “Right”
Description: A replicator and an imitator are released on a landscape with equally poor initial positions (r ⳱ i ⳱ 0). Each firm tweaks uphill to
a peak, leaps toward the benchmark set of decisions, and then makes incremental improvements until arriving at a peak. The two firms are
equally accurate in their leaps (hR ⳱ hI ⳱ 0.5). The replicator knows which of its decisions are different from the benchmark and, in its leap,
tries to modify only those decisions. In contrast, the imitator tries to modify all of its decisions in its long jump. N ⳱ 12 and K varies. For
each level of K, the panel reports the portion of the benchmark performance eventually attained by each firm and the “distance” of the final
location from the benchmark (i.e., the number of decisions not resolved in identical fashion). The reported figures are the average (and the
standard deviation) over 100 landscapes. The significance level of a test that the replicator’s average and the imitator’s average do not differ
is also reported. The level of K that leads to the greatest performance differential is shown in bold.
Replicator performance
Imitator performance
K
Percent of
P(s*) attained
Final distance
from s*
Percent of
P(s*) attained
Final distance
from s*
Performance
differential
0
1
2
3
4
5
6
7
8
9
10
11
100.0 (0.0)
98.4 (2.4)
96.8 (3.7)
94.9 (5.3)
93.0 (5.5)
92.0 (6.2)
89.6 (6.3)
88.4 (5.6)
86.9 (5.9)
85.2 (6.4)
85.6 (5.6)
82.7 (5.9)
0.0 (0.0)
1.7 (1.9)
3.0 (2.8)
3.9 (3.2)
4.4 (2.9)
4.4 (2.9)
5.3 (2.3)
5.3 (2.3)
5.3 (1.8)
5.5 (1.9)
5.3 (1.5)
5.3 (1.6)
100.0 (0.0)
97.9 (3.5)
95.9 (4.5)
93.9 (5.3)
91.0 (6.5)
90.2 (6.4)
89.6 (6.1)
88.7 (5.5)
86.8 (5.9)
84.5 (6.2)
85.4 (5.9)
81.9 (6.0)
0.0 (0.0)
1.9 (2.1)
3.6 (3.0)
4.2 (2.7)
5.1 (2.6)
5.6 (2.3)
5.8 (2.0)
5.7 (2.1)
6.0 (1.9)
6.2 (1.7)
6.2 (1.7)
5.9 (1.7)
0.0
0.6 *
1.0 **
1.0 *
2.0 ***
1.7 **
ⳮ0.1
ⳮ0.3
0.1
0.7
0.2
0.7
***Performance differential significant at the 1% level
**Performance differential significant at the 5% level
*Performance differential significant at the 10% level
1982, pp. 107–109, 306–325, Perrow 1984, pp. 62–100,
Sanchez and Mahoney 1996, Brown and Eisenhardt
1998). The traditional interpretation is that such organizations balance the benefits of centralization and decentralization, adapt to changes in local conditions, and seal
off internal problems before they spread (Orton and
Weick 1990). The normative implication is that firms
should configure themselves to be loosely coupled.15
An alternative rendering, however, begins with the observation that environments differ in the degree to which
they demand coupling. Some settings present highly intertwined decisions while others pose uncoupled choices.
It happens—for the reasons explored in this paper—that
the widest gaps between firms, between replicators and
imitators, arise in settings where decisions are inherently
loosely coupled (intermediate K). For this reason, firms
with particularly large performance advantages over their
industry peers might exhibit loose coupling. This association arises not because loose coupling leads to superior
profitability, but because the settings that encourage loose
ORGANIZATION SCIENCE /Vol. 12, No. 3, May–June 2001
coupling also generate wide gaps between the best and
worst performers. In such a scenario, the best (relative)
performers are not those that choose to be loosely coupled, but those who face moderately interactive decision
problems and happen upon good sets of decisions. If this
is the case, universal prescriptions in favor of loose coupling seem misguided.
This alternative interpretation is simply speculative at
this point, but it does suggest empirical patterns that are
distinct from the traditional interpretation. Under the traditional interpretation, different firms within an industry
would exhibit different degrees of coupling, with the superior performers within each industry displaying loose
coupling. Under the alternative rendering, firms within an
industry would exhibit similar degrees of coupling, industries would differ in the degree of coupling of the typical firm, and the broadest dispersion of profitability
would arise within those industries that display loose coupling. I am aware of no existing empirical work that distinguishes between the interpretations.
289
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JAN W. RIVKIN Reproducing Knowledge
The discussion of loose coupling raises two issues
which are significant gaps in the current paper and major
opportunities for future research: environmental change,
and managerial influence on the landscape and the search
process. I consider each in turn.
For simplicity, my analyses have assumed a fixed environment. Replication and imitation attempts proceed on
the very same landscape as the one on which the original
success arose. In reality, of course, external conditions
vary from one setting to the next; in landscape parlance,
each time a firm attempts replication, it faces terrain that
is at least slightly altered. One of the replicator’s gravest
challenges is to detect the changes and respond to them
appropriately. Marks & Spencer’s woes in Canada and
Europe, for instance, arose in part because it reproduced
its British formula too faithfully in some respects. In Canada, it located in downtown shopping areas that resembled British High Streets rather than in regional malls
which were gaining popularity. It also retained traditional
clothing lines and a dark decor that were popular in Britain but not in Canada or Europe (Montgomery 1994). A
natural extension of the research is to explore the conditions of environmental change under which the replicator’s access to a template remains an advantage and the
circumstances under which it can become a liability. How
does environmental change influence the wedge between
replicability and imitability?
The analyses have also simplified the role of managers
in ways that clarify the present paper, but depart from
reality in important respects. Managers in my simple
models, for instance, cannot affect the degree to which
decisions are connected (K). In real situations, of course,
managers spend a great deal of time, energy, and money
to make or break connections (Anderson 1999). They can,
for instance, make investments that sever links among
decisions, thereby making a productive system more modular
(Baldwin and Clark 2000, Levinthal and Warglien 1999,
Schilling 2000). Similarly, managers here do not influence the information they have about past successes (r or
i). In reality, managers undertake crucial efforts to preserve secrecy or to penetrate the secrets of rivals (Winter
1987). They also make trade-offs among different approaches to information gathering. Replicating managers,
for instance, may opt to reproduce a success with less
initial fidelity than an imitator (r ⬍ i) because they know
they can turn to the template later in order to refine their
efforts. Finally, the paper treats replication and imitation
largely as a one-shot effort to reproduce a success, not as
an ongoing effort to refine one’s knowledge of the deeper
roots of success (cf., Winter and Szulanski 2000). All
three of these lacunae offer promising avenues for further
modeling and future research.
290
Acknowledgments
For insightful comments, the author is grateful to Jeff Dyer, Nicolaj
Siggelkow, Gabriel Szulanski, Sidney Winter, three anonymous referees,
and seminar participants at conferences organized by INFORMS and the
Academy of Management. The author also thanks Howard Brenner for
exceptional computer programming assistance and the Division of Research
of Harvard Business School for generous funding. Errors remain the author’s.
Endnotes
1
Because the predictions of game-theoretic models are highly sensitive
to assumptions that depend on the details of a competitive context,
such models might be better tested through analytical case studies than
through large-scale statistical work. Case studies do validate the predictions of game theory in particular settings (Ghemawat 1997).
In the handful of examples of replication-without-imitation cited
above, a preemptive move seems crucial in only one case: Wal-Mart
apparently deterred competitors from entering its markets by committing to build “good-sized stores in little one-horse towns” (Walton and
Huey 1992, Foley et al. 1996). This helps to explain why rivals did not
enter towns already served by Wal-Mart. It leaves open the question
of why rival discount stores in rural areas, such as Ames, did not build
aggressively in other towns.
2
All of the transfers in Teece’s sample are voluntary, but roughly half
are to joint venture partners or to enterprises not owned by the transferring party.
3
Moreover, note that the chemical industries which Lieberman studies
are the markets in which Mansfield (1985) finds information leakage
to be the slowest.
4
Mansfield et al. (1981) report that patents boost imitation costs by
much more than 11% in the ethical drug market, a finding confirmed
by Levin et al. (1987).
5
Codification and teaching of knowledge may be especially expensive
because, in order to codify or teach, the managers who possess the
knowledge must forgo other valuable activities.
6
Personal communication from Seth Lloyd, d’Arbeloff Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, September 11, 1998.
7
Mansfield (1985) and Levin et al. (1987) find evidence, albeit weak
evidence, that this is true.
8
Zander and Kogut (1995) provide the most direct evidence. Using a
survey of managers familiar with 35 Swedish innovations, they examine the factors which influence the speed of replication and the speed
of imitation. Consistent with the search point of view, technologies that
can be codified and communicated more easily are more rapidly replicated. The presence of rivals working on similar technology also pressures firms to replicate their successes quickly. Contrary to the search
perspective, neither the characteristics of the knowledge being transferred nor the observability of the innovation influences the pace of
imitation. Imitation is faster when the innovator has lost key employees
and slower when the innovator pushes aggressively to improve on its
own success.
9
See, for instance, Kauffman (1995), especially Chapters 9–11, Kauffman
and Macready (1995), Westhoff et al. (1996), Levinthal (1997), Sorenson (1997), McKelvey (1999), Levinthal and Warglien (1999), Rivkin
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JAN W. RIVKIN Reproducing Knowledge
(2000), Gavetti and Levinthal (2000), Kauffman et al. (2000), and
Fleming and Sorenson (2001).
10
The NK framework assumes that there exists some single measure
of fitness which managers maximize. Anderson (1999) points out that
real managers must attend to multiple goals, which makes maximization untenable. An interesting extension to this paper would modify
the present model so that each combination of decisions maps to more
than one vertical “payoff” axis. Managers would then cycle through
different goals and optimize with respect to each in turn. Local search
with respect to one goal would often draw managers away from peaks
with respect to other metrics. In such a model, I speculate that firms
would ordinarily not get stuck on a local peak in a static fashion, but
would reach some limiting cycle of steps.
11
This is an adaptation of Milgrom and Roberts’ (1990) definition of
“complementarity.” Under their definition, complementarity arises
when performing more of Activity A increases the marginal benefit of
doing more of Activity B. I use the term “interaction” rather than “complementarity” to encompass both complementarity and substitution between decisions.
12
See Abernathy et al. (1995) on “lean retailing” of apparel, Athey and
Schmutzler (1995) on process and product flexibility and innovation,
Cockburn et al. (1999) on pharmaceutical research, Hitt and Brynjolfsson
(1997) on information technology investments and organizational architecture, Hwang and Weil (1996) on apparel manufacturing,
Ichniowski et al. (1997) on human resource practices in the steel finishing process, Milgrom and Roberts (1990) on “modern manufacturing,” Milgrom and Roberts (1995) on Lincoln Electric, Porter (1996)
on Ikea, Vanguard, and other firms, Rivkin (1995) on Toys “R” Us and
Circuit City, Siggelkow (1998) on Vanguard and Liz Claiborne, and
Upton (1995) on flexible manufacturing.
13
The claim is not that strategic, resource, and organizational considerations have no effect on the success of imitation and replication.
Rather, the argument is that these considerations do not explain a difference between replication and imitation thoroughly. If an original
success is based on some truly one-of-a-kind resource, for example,
the resource constraint could bar both replication and imitation. But
the constraint would not account for a gap between the ease of replication and the ease of imitation.
14
A test would have to measure the degree of interaction, detect differences in interactivity, and size up differences in the ease of replication and imitation. The work of Athey and Stern (1996) highlights
the difficulty involved in measuring interactions. See Zander and Kogut
(1995) for a rare successful effort to compare the speeds of replication
and imitation.
15
The diverse literature on loosely coupled systems is far from unanimous in its endorsement of loose coupling. Some studies see loose
coupling as a problem to be fixed. Others recommend not loose coupling per se, but a degree of coupling commensurate with external and
internal demands. See Orton and Weick (1990) for an excellent survey.
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