Strat. Mgmt. J.,36: 377–396 (2015) Published online EarlyView 2 January 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2218 Received 23 January 2012;Final revision received 28 October 2013
CHANGES IN FIRM KNOWLEDGE COUPLINGS AND
FIRM INNOVATION PERFORMANCE: THE
MODERATING ROLE OF TECHNOLOGICAL
COMPLEXITY
SAI YAYAVARAM1
* and WEI-RU CHEN2
1Corporate Strategy & Policy, Indian Institute of Management Bangalore,
Bangalore, India
2Strategy and Entrepreneurship, China Europe International Business School,
Shanghai, China
We investigate the effect of changes in a firm’s knowledge couplings on its innovation performance. We develop arguments to explain how changes in couplings among existing knowledge domains and those between new and existing knowledge domains affect the generation of valuable inventions. We also examine how observed domain complexity, an indicator of the inherent interdependencies among knowledge domains, moderates the effects of changes in a firm’s knowledge couplings on innovation performance. Our results suggest that a change in couplings among existing knowledge domains hurts innovation outcomes, but not when the degree of domain complexity is high, whereas coupling new and existing knowledge domains leads to improved outcomes, but not when the degree of domain complexity is high. Copyright2013
John Wiley & Sons, Ltd.
INTRODUCTION
In this paper, we investigate how changes in a firm’s knowledge base affect its innovation perfor-mance. The literature on innovation management posits that firms recombine elements of knowl-edge from familiar domains to generate inventions that did not previously exist (Fleming, 2001; Schumpeter, 1934). Firms typically exploit and refine their knowledge by searching in the vicinity of their existing knowledge domains (Levinthal and March, 1993; Miller, 2002). At the same time, they are under pressure to change their knowledge bases to keep pace with the external technological environment and to compensate for the exhaustion
Keywords: change; coupling; interdependence; innova-tion; complexity
*Correspondence to: Sai Yayavaram, E-107, Indian Institute of Management Bangalore, Bannerghatta Road, Bangalore 560076, India. E-mail: sai.yayavaram@iimb.ernet.in
Copyright2013 John Wiley & Sons, Ltd.
the knowledge domains to which these elements belong (Grant, 1996; Henderson, 1992; Kogut and Zander, 1992). The type of relationship between knowledge domains that we explore in this paper is “coupling,” which is defined as the extent to which a firm is likely to combine the elements in two domains, X and Y, when searching for new inventions (Yayavaram and Ahuja, 2008). A high level of coupling between X and Y implies that the firm is likely to search with combinations of X and Y rather than with X or Y alone, whereas a low level implies that this is less likely. The couplings between all pairs of knowledge domains over which a firm conducts its technological search can be specified, and this set of couplings can be used to characterize the firm’s knowledge base.1
Because a firm can change both the domains over which it conducts a search and the couplings between those domains, it is important to examine both. A domain change occurs when a firm exits from domain Y and/or enters a new domain Z. Even when no new knowledge domains are involved, a domain change occurs when a firm shifts its emphasis from an existing domain X to another existing domain Y. A change in couplings occurs when a firm introduces coupling between an existing domain X and a new domain Z, or between two existing domains A and B that were previously uncoupled, or when it shifts its emphasis from the coupling of an existing domain pair X-Y to another existing domain pair C-D.
Previous research has examined the effects of searching in knowledge domains that are either familiar or new to the firm (Ahuja and Lampert, 2001; Katila and Ahuja, 2002; Rosenkopf and Nerkar, 2001). For instance, Rosenkopf and Nerkar (2001) show that an exploratory search that spans existing technological or organizational boundaries tends to have a larger effect than one that does not. Similarly, Katila and Ahuja (2002) examine
1Although the concepts of architectural knowledge and knowl-edge coupling are closely related, an important distinction between them should be noted. Henderson (1992) defines archi-tectural knowledge as knowledge of the ways in which a prod-uct’s components are integrated and linked into a coherent whole. A firm’s knowledge couplings, in contrast, specify the combinations that a firm is likely to consider during a techno-logical search. The focus here is on the entire knowledge base that leads to inventions, rather than on knowledge pertaining to a single product or group of products.
how the repeated use of existing knowledge and exploration of new knowledge influence new product introductions. These studies, however, do not explicitly consider how existing and new knowledge are integrated or how the knowledge base itself changes due to changes in couplings. In this paper, we focus on the changes that a firm makes in its couplings (and the implied changes in combinations) while controlling for changes in the knowledge domains over which it conducts its search. By looking at changes in couplings rather than each of a firm’s inventions by itself, we can examine how the knowledge base has changed and how such changes affect the invention process in the long run. In addition, we disaggregate the overall change in couplings into (1) changes in the couplings among a firm’s existing knowledge domains, and (2) those that arise from the addition of couplings between a firm’s existing and new knowledge domains. It is important to consider these differences because the effects on innovation outcomes may vary across the two types of coupling changes.
negatively moderated when domain complexity is high.
THEORY AND HYPOTHESES
Firms and recombinant inventions
Technological inventions are generated by recom-bining knowledge in novel ways (Fleming, 2001; Kogut and Zander, 1992; Schumpeter, 1934). Each such combination represents an invention. Firms explore the “search space” of possible combina-tions (i.e., the technological landscape) to iden-tify valuable inventions. A technological search almost never starts ab initio. Rather, it typically involves changing the configurations of an exist-ing combination to create new combinations, and then examining the many recombinations possible to determine whether any will lead to an enhanced performance outcome.
The eventual outcomes are determined by the knowledge domains considered by the entity con-ducting the search (in our case, a firm), the interde-pendencies among those knowledge domains, and the combinations considered. First, in the search process, a firm considers only a small subset of the universe of potential knowledge domains. This set of domains is, by definition, the firm’s knowledge base. The number of domains that can potentially be included in the consideration set (the knowl-edge universe) is the same across firms, but the domains that areactually included differ by firm. Second, search effectiveness is also strongly influ-enced by the interdependencies between knowl-edge domains (Kauffman, 1993; Levinthal, 1997). In the context of technological innovation, two knowledge domains, X and Y, are interdependent when inventions that combine X and Y are more likely to be successful than those that use X or Y alone. Thus, inventors need to search both domains to identify valuable XY configurations. When the two knowledge domains are independent, in con-trast, a search in one can proceed independently of that in the other.
Third, the outcomes are affected by which combinations the search entity considers (Flem-ing, 2001; Stuart and Podolny, 1996). Although interdependencies obviously influence which combinations are considered, they do not fully determine these decisions. Since it is not possible to know all of the underlying interdependencies
ex ante, the performance implications of any combination of knowledge domains are initially unknown and can be discovered only through experimentation.2 Further, given the large number of potential interdependencies, firms have to make important choices about which to focus on. The combinations that a firm decides to consider are governed primarily by the existing couplings between knowledge domains (Yayavaram and Ahuja, 2008). The coupling of two domains indicates the extent to which the firm is likely to combine knowledge elements from each in search-ing for new inventions. Couplsearch-ings can vary from strong (domains X and Y are always considered together) to weak (X and Y are considered together occasionally) to nonexistent (X and Y are always considered independently).3 Because couplings
guide the combinations a firm considers, they play an important role in technological search. An example of a firm’s set of knowledge couplings is provided in Figure 1.4 In this figure, the nodes
represent the firm’s knowledge domains, and node size is a measure of domain size (i.e., the number of the firm’s patents that belong to that domain). Viewing a firm’s knowledge base as a network also suggests that couplings can be interpreted as the ties that the firm has between knowledge domains and that the strength of a tie between two domains represents the level of coupling between them.
It may be useful at this point to consider some of the differences between the concepts of interde-pendence and coupling. Interdeinterde-pendence is part of the natural world (Yayavaram and Ahuja, 2008). Since it is not possible for a firm to know all inter-dependenciesa priori, improving understanding of interdependencies is an important part of a techno-logical search. Coupling, in contrast, is a decision that belongs to the made world, that is, a decision that the search entity makes, either explicitly or implicitly, while conducting the search. If a firm
2A firm can also engage in a cognitive search to generate maps (Fleming and Sorenson, 2004; Gavetti and Levinthal, 2000) that can help to locate promising areas. However, the firm must eventually engage in an experiential search to identify valuable outcomes (Gavetti and Levinthal, 2000).
3We define coupling pairwise in this paper. This definition, however, does not preclude a firm from combining more than two knowledge domains. In other words, if a firm has a high level of coupling between domains X and Y and between domains Y and Z, it is likely to combine all three domains in an invention. 4The approach used to construct these knowledge couplings is discussed in the Methods section.
Figure 1. Knowledge couplings for Intel in 1996. The numbered nodes represent technology classes and the ties between nodes represent coupling between those classes, where the strength of a tie represents level of coupling and darker lines indicate stronger ties; the node size is a measure of the number of patents that belong to that technology class. Isolated nodes and very weak ties have been removed to clarify the illustration, which includes only those classes
that are related to the semiconductor industry5
perceives X and Y to be interdependent (i.e., per-ceives that a combined search across X and Y can lead to valuable configurations), it may pro-vide for some level of coupling between these two domains. However, even if a firm perceives the two domains to be interdependent, it may choose not to couple them because it is impossible for it to consider all of the interdependencies in the techno-logical world. It may also decide that other interde-pendencies are more worthy of consideration than that between X and Y. Thus, the actual set of cou-plings that a firm chooses is likely to be a subset of the set of all potential couplings. Further, a firm’s couplings do not usually converge to the underly-ing interdependencies since provision of couplunderly-ing between all interdependent domains makes the search process rigid (Yayavaram and Ahuja, 2008). With respect to the technological search process, it is useful to distinguish between the invention,
5Figure taken from Yayavaram and Ahuja, (2008). Decompos-ability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability.Administrative Science Quarterly 53(2): 333–362. Reprinted with permission from SAGE Publications.
the search entity, and the underlying technological environment. Our focus on knowledge couplings allows us to distinguish between the search space and the characteristics of the entity (in our case, the firm) conducting the search. In our concep-tualization, the characteristics of the search space are determined by interdependencies exogenous to the firm, whereas the choices of where to search (i.e., the consideration set) and which domains to couple or not couple are endogenous. As a point of clarification, our approach differs from that of Fleming and Sorenson (2001), whose focus is on the complexity of an invention. Ours is on the firm-level search process. By shifting our analy-sis to the firm level, we are able to examine the enablers of and hindrances to change that exist within a firm, not all of which exist for each indi-vidual invention. Further, we can examine features of a firm’s knowledge base, such as knowledge couplings, that exist at levels higher than that of an invention.
social cognition (Barr, Stimpert, and Huff, 1992; Kiesler and Sproull, 1982), social organization (Galunic and Rodan, 1998), and internal sociopo-litical processes (Lyles and Schwenk, 1992). Cou-plings are the outcomes of both deliberate and emergent activities within the firm. For example, a coupling may be driven by a top manage-ment directive or emerge from the actions of researchers working independently. Couplings are embodied in routines, in beliefs about the under-lying interdependencies between domains, and in communication networks and the organizational structure (Yayavaram and Ahuja, 2008). Coupling between previously uncoupled domains X and Y is accomplished by changes in a firm’s inter-nal routines, communication patterns (e.g., the R&D staff in Research Unit 1, which special-izes in domain X, will now interact with the R&D staff in Research Unit 2, which special-izes in domain Y), organizational structure (the two research units may merge), or resource alloca-tion, effort, and attention (projects that involve X and Y will get more funding and top management attention).
In the next section, we consider the organiza-tional issues involved when couplings change, and develop hypotheses about what kinds of changes in a firm’s knowledge couplings lead to better innovation outcomes and how domain technolog-ical complexity may moderate the relationship between the two. We consider innovation qual-ity (i.e., the value or impact of a firm’s inven-tions) to be a more appropriate outcome variable than quantity (i.e., the number of inventions) in representing innovation performance. Innovation performance is enhanced when the firm gener-ates inventions that create economic value for the firm by resolving important techno-economic problems.
Changes in a firm’s knowledge couplings
A firm can change its knowledge base by chang-ing either the knowledge domains it covers or the couplings between those domains. Given the importance of recombination to the inno-vation process, we focus on how firms change the couplings between their knowledge domains while controlling for changes in domains per se
that might also determine innovation outcomes. The need for a change in knowledge couplings arises from two factors. First, firms have to
change their technologies over time to keep pace with the changing environment— including the changing needs of customers, suppliers, and alliance partners—and their competitors. Second, the returns from an existing knowledge coupling decline over time because of the technological and social/psychological exhaustion of potential refinements (Fleming, 2001; Fleming and Soren-son, 2001). Technological exhaustion eventually occurs because there is a finite number of ways in which a given set of knowledge domains can be recombined (Ahuja and Lampert, 2001; Kim and Kogut, 1996).
Changes in a firm’s knowledge couplings can be disaggregated into five constituent types: (1) a firm can change the coupling among its existing knowledge domains; (2) when new knowledge is being acquired or developed organically, the firm may couple the new knowledge domains with existing domains; (3) when a firm discontinues the use of two or more knowledge domains (i.e., exits two or more technologies), it dissolves the couplings between those domains; (4) when a firm discontinues the use of particular knowledge domains, it dissolves the couplings between those domains and existing knowledge domains; and (5) the firm may couple new knowledge domains with one another (see Table S1 in the supporting information). For conceptual clarity, we focus on the first and second types of change (in which at least one of the knowledge domains is familiar to the firm) in the hypothesis section. These two types together account for nearly 70 percent of all change in couplings in our sample.
As an example of these two types of change, consider again Intel’s knowledge couplings in 1996 (see Figure 1). The increased coupling between class 257 (Active solid-state devices), class 174 (Electricity: conductors and insulators), and class 361 (Electricity: electrical systems and devices) from 1990 to 1996 is an example of coupling changes between existing knowledge domains. The increase in the level of coupling between these classes can be ascribed to an increase in the number of inventions targeting heat dissipation in microprocessors, which became a major issue for developers from the early 1990s onward as a result of the rapid increase in transistor density. An example of coupling between new and existing knowledge domains is Intel’s entry into technology class 348 (Television), a step it took
to facilitate its entry into the videoconferencing market, and the subsequent couplings between that class, which was new to Intel, and existing classes 370 (Multiplex communications) and 375 (Pulse or digital communications). Using network terminology, a change in the level of coupling between a pair of knowledge domains can be interpreted as a change in the strength of the tie between those domains. It is important to note that in looking at changes in the strength of couplings, rather than only at the addition or deletion of couplings, we are looking at a more comprehensive measure of change. In other words, changes in the strength of ties encompasses cases in which ties have been created or deleted, whereas the addition or deletion of ties excludes all cases in which existing ties have become weaker or stronger.
To better understand how these different types of change affect the innovation process, it is important to look at both the costs and benefits of change and whether the former outweigh the latter. It is not only a question of whether the benefits outweigh the costs, but also of identifying the conditions under which they do so. We first discuss the benefits and costs of change for the base case, and then examine how the technological environment in which the firm operates may alter the balance between the two.
Coupling changes among existing knowledge domains
A change in the coupling among existing knowl-edge domains occurs when a firm introduces new couplings, discontinues old couplings, and weak-ens or strengthweak-ens existing couplings among these domains. We group all such changes into one cat-egory, as their effect on innovation outcomes is likely to be the same. The mechanisms behind the effects of increases and decreases in coupling on innovation outcomes are the same, and hence these increases and decreases should be grouped together rather than considered separately.
Such changes in couplings can have a positive impact on innovation performance for several reasons. By undertaking these changes, a firm is exploring new combinations or shifting its focus away from one set of combinations to another. The firm is already familiar with its existing knowl-edge domains, and has a high level of expertise in using them (Ahuja and Lampert, 2001). Further, in the case of strengthening the coupling between
existing knowledge domains that were weakly coupled before, the combinations that are being considered are not entirely novel to the firm. As it already possesses the knowledge in question, the factors that may hinder innovation, such as a lack of absorptive capacity or the “not-invented-here” syndrome, are absent. At the same time, by using its existing knowledge in novel ways, the firm can attempt novel modes of reasoning and adopt new problem-solving approaches and, thereby, over-come the “familiarity trap” (Ahuja and Lampert, 2001). The firm can thus generate combinations that, while being built on existing knowledge, provide the benefits of novelty and exploration.
knowledge couplings is difficult, and can easily lead to failure (Henderson and Clark, 1990).
While we argue that change has both benefits and costs, it is difficult to make a strong theoretical case for why benefits outweigh costs or vice versa. We therefore posit competing hypotheses.
Hypothesis 1a: Change in coupling among existing knowledge domains has a positive effect on firm innovation performance.
Hypothesis 1b: Change in coupling among existing knowledge domains has a negative effect on firm innovation performance.
Coupling between new and existing knowledge domains
A firm may also alter its knowledge base by intro-ducing new technologies. Entering a new technol-ogy domain does not in itself guarantee success because multiple factors may intervene. The value of the new technology may be highly uncertain, thereby increasing the possibility that the firm has made the wrong choice. More importantly, the firm may lack the capability or necessary experience to conduct a search in the new technological domain, which may confer competitive advantage on other more capable firms. Several benefits will accrue to the firm, however, if the new technology can be coupled with existing knowledge. The num-ber of combinations that the firm can examine rises, as knowledge from the new domains can potentially be combined with its existing knowl-edge. Such combinations involve less uncertainty than entirely new combinations because the firm is familiar with at least part of the technology in question, which can thus serve as a stepping stone to new technology entry, allowing the firm to grad-ually build research capabilities in the new domain. Further, coupling a new knowledge domain with existing domains creates linkages that cannot be easily matched by competitors.
Similar to the prior case of coupling changes among existing knowledge domains, there are costs associated with creating couplings between new and existing knowledge domains. However, these costs are likely to be much lower in the case of coupling with new knowledge domains. To see why, consider how the search process works with existing knowledge domains. Since firms typically
satisfice in the search process and the outcomes of changes are uncertain, they may be content to stay at their existing position on the technological landscape if they perceive it to be successful enough. Thus, they are likely to resist change unless their current performance does not meet the threshold of what they consider successful. Even when a firm sees a rival doing something different, it may not accept that the rival’s approach is better since long-term performance implications of technologies are not immediately clear. Due to existing cognitive filters and expertise in these domains, R&D teams are less likely to accept another approach as better especially when both domains are familiar. In the face of such resistance to change, reconfiguration of existing knowledge domains will be poorly managed and thus unlikely to provide the desired benefits.
When additional domains are considered in the search process, there may be greater awareness of the need for change. A significant problem with existing couplings is that, owing to cognitive fil-ters, research teams may fail to recognize that the domains with which they are familiar can be combined in novel ways. When domains are unfa-miliar, in contrast, the combinations that are con-sidered would naturally be novel to the firm. Also, the presence of new domains may reveal inter-dependencies of which the firm was previously unaware. When these additional interdependen-cies are considered, the firm may become aware of more valuable configurations in the immediate neighborhood. The current performance will then be deemed unsatisfactory, and change will be seen as desirable. While there may be disruptions due to disagreements about which new domains to enter and which search directions to pursue, most of the disagreements would occur before these decisions are made. Thus, once a firm decides on a particular course of action and change is ongoing, disruption is less likely when some domains are new and, consequently, search is more effective with new and existing domains than with existing domains alone.
Further, by coupling new and existing knowl-edge, firms can engage in boundary-spanning exploration (Rosenkopf and Nerkar, 2001), which reinvigorates searches with existing knowledge as new ideas are introduced and also enables the firm to explore new technological, market, or geographic opportunities by leveraging its exist-ing strengths (Nerkar, 2003). The benefits of
such new couplings go beyond the generation of boundary-spanning exploratory inventions, since these couplings imply that the firm’s knowledge base itself has changed. Hence, coupling between new and existing knowledge domains can have a lasting effect on search processes within the firm, unlike boundary-spanning exploration that, by itself, may or may not have such an effect. We thus expect more valuable inventions to arise when new knowledge is well integrated with exist-ing knowledge domains.
Hypothesis 2: Coupling between new and exist-ing knowledge domains has a positive influence on firm innovation performance.
Domain complexity
We argue that a firm’s technological environ-ment will determine the payoff from knowledge recombinations and thereby moderate the effects of search strategies on innovation outcomes. We focus on domain complexity, the contextual char-acteristic most often highlighted in the literature on innovation landscapes (Ethiraj and Levinthal, 2004; Levinthal and Warglien, 1999; Nickerson and Zenger, 2004; Rivkin, 2000). The degree of domain complexity is low when there are few interdependencies among the knowledge domains in a firm’s consideration set. In such situations, the firm needs to examine only a few combina-tions. Since there is less uncertainty about which combinations are valuable, a firm searching locally using its existing knowledge base will have fewer directions to pursue that will lead to enhanced innovation performance. A high degree of domain complexity, in contrast, leads to greater uncertainty about which combinations are valuable and how the search should progress. Further, the presence of a large number of interdependencies implies that a change made to one domain during the search pro-cess may require changes in many other domains (Rivkin and Siggelkow, 2003). Discovery of most of the valuable combinations available requires the firm to follow a different search path (i.e., to “think outside the box”) and move to parts of the search space that it has not yet explored.
In our arguments for Hypothesis 1, we pointed out the benefits and costs of changes in the cou-plings among existing knowledge domains. The benefits of such changes are likely to be greater when the degree of domain complexity is high
for the following reasons. Changing the couplings among existing knowledge domains leads to new combinations or a different mix of combinations and can lead the firm away from its local neighbor-hood. Such changes, which usually reflect changes in the mental models of R&D researchers, chang-ing cognitive frameworks, and the approachchang-ing of problems from a fresh angle (Barr et al., 1992; Kiesler and Sproull, 1982), are more valuable in a highly complex environment. Experiments with various recombinations of knowledge domains provide new starting points for further exploration, thereby helping the firm to avoid being stuck on a competence peak (Ethiraj and Levinthal, 2004; Kauffman, 1993; Siggelkow and Rivkin, 2005). The resulting search diversity is preferable in an environment characterized by a high degree of complexity and multiple opportunities (Levinthal, 1997; Siggelkow and Rivkin, 2005). In simpler innovation contexts, in which the search space is devoid of peaks or has already been explored, there is less to be gained from reconfiguring existing coupling relationships. Hence, a change in cou-pling among existing knowledge domains is more useful when the underlying technologies are more complex.
Hypothesis 3: Domain complexity positively moderates the effect of change in coupling among existing knowledge domains on firm innovation performance (or attenuates the neg-ative effect of such a change).
search space that are less promising than antici-pated. In a highly complex environment, it may be preferable for a firm to follow a less inte-grated approach, keeping highly interdependent and familiar domains together while leaving new and unfamiliar domains loosely coupled (Ethi-raj and Levinthal, 2004; Sanchez and Mahoney, 1996). We thus expect that the greater the domain complexity, the less effective the coupling of new knowledge domains with existing knowledge domains will be in generating valuable inventions.
Hypothesis 4: Domain complexity negatively moderates the effect of coupling between new and existing knowledge domains on firm inno-vation performance.
METHODS
Sample
Our measures of changes in firms’ knowledge base and of contextual variables are based on the patents granted by the U.S. Patent and Trademark Office (USPTO). We tested our hypotheses on a longitudinal data set comprising all U.S. firms listed in Standard & Poor’s Compustat database from 1976 to 2004. Choosing firms from a wide range of industries made it possible for us to test the moderating effects of domain complexity. These firms were then matched to the assignee names on the patents issued by the USPTO via a procedure based on data from Professor Bronwyn Hall’s patent name-matching project (http://elsa. berkeley.edu/∼bhhall/pat/namematch.html). For all patent-related variables, we followed previous research in using the patent’s date of application, unless otherwise stated. Because we needed data from the six previous years to compute our measures of change, a firm entered our sample in year t only if it had patents in at least two years during the t-6 to t-4 period and in at least two years during the t-3 to t-1 period. We were thus left with a sample of 1,750 firms for which we were able to build change- and context-related measures.
Dependent variable
The dependent variable Firm innovation perfor-mance was measured as the number of citations
that a firm’s patents filed in year t had received in subsequent years up to 2004. The number of citations that a patent receives is generally accepted as a significant predictor of both its value (Hall, Jaffe, and Trajtenberg, 2005; Harhoff
et al., 1999) and technological impact. In our context, changes in coupling are expected to affect the quality of inventions that are generated and, thereby, affect the number of citations received. There could be concern that a firm may be erecting barriers to imitation when it attempts new couplings whether between existing knowledge domains or between existing and new domains, which then would affect the number of citations made by other firms. This is unlikely to be an issue in our context since what is new to a firm is not necessarily new to other firms.
Independent variables
We used the USPTO’s technology class data to build our measures of knowledge couplings. The USPTO assigns every patent it issues to one or more three-digit technology classes. We used the classification that was current in 2005.6 Unlike citations, which are prone to examiner bias, there should be few errors in class assignments because the USPTO takes adequate care when classifying patents. Following Fleming (2001) and Fleming and Sorenson (2001), we considered the technology classes assigned to patents as proxies for knowledge domains, and the co-listing of classes as indicative of a recombinant search process.7
We assumed a firm’s knowledge base or patent portfolio att to consist of all of the patents that it
6Whenever the USPTO changes its technology classification system, it retroactively changes the class assignments for all previous patents to maintain consistency at a particular point in time. Hence, our measures are unaffected by changes in the classification system.
7Unlike Fleming and Sorenson (2001) who consider technolog-ical subclasses as domains, we chose to consider technology classes as domains for the following reasons. Building coupling matrices using technological subclasses would result in sparse matrices and an overestimation of changes from one time period to another, as even a small inconsequential difference would count as a change. Coarser aggregation such as technological categories (with each category consisting of related technological classes), in contrast, would lead to an underestimation of change, since firms typically do not make broad changes at the techno-logical category level. Analysis at the technotechno-logical class level avoids both extremes. Further, using subclasses would result in very large matrices that are difficult to handle computationally. We therefore chose to use classes for our coupling measure.
had accumulated fromt-3 tot-1. We used a three-year window to minimize the effects of three-yearly fluctuations in patent applications. The coupling between technology classes j and k for firm i,
Li,j−k,t−3to t−1, can be calculated as
Li,j−k,t−3to t−1=
njk
nj +nk −njk
, (1)
where nj is the number of patents assigned to
class j, nk is the number of patents assigned to
class k, andnjk is the number of patents assigned
to both classes (Yayavaram and Ahuja, 2008). Coupling matrix Li,j−k,t−3to t−1, consisting of
Li,j−k,t−3to t−1 for all pairs of domains,
repre-sents the firm’s knowledge base. The logic of this measure is that a firm’s repeated recombination of two domains implies that the two are strongly cou-pled in its knowledge base. An important limitation of this measure of revealed couplings is that it does not capture all of the couplings that were attempted but did not result in patentable inventions.
To calculate the changes in a firm’s knowledge couplings, we compared each firm’s coupling matrix for the t-6 to t-4 period with that for the
t-3 to t-1 period. Doing so precluded common patents between the two coupling matrices being compared. We measured change in coupling as the weighted number of technology class pairs that had undergone a significant change in coupling between the two time periods (Yayavaram and Ahuja, 2008). A significant change was defined as a change that exceeded one quartile. We estimated the 25th, 50th, and 75th percentile values of coupling as a function of the size of the firm’s patent portfolio and time.
We used a quartile ranking-based measure to avoid two kinds of bias in measuring change: (1) no change being recorded as a change because of measurement errors,8and (2) coupling strength varying with the size of a firm’s knowledge base (Yayavaram and Ahuja, 2008). We estimated the quartile rankings using all nonzero couplings in
8Consider an error ofε1in measuring coupling L during the first time period and an error of ε2 during the second. If there has been no change in the true value of the coupling, the difference between the two time periods is
|L+ε1 – L –ε2|, which is equal to
|ε1–ε2|. Because
|ε1–ε2|is greater than zero, this measure implies a change even though there has been no real change in the coupling value. We consider absolute values for our measures since our theoretical arguments concern change, not simply increases or decreases in coupling.
the data set. In setting a baseline, we needed to control for size because the median level of coupling can change with size. As the size of a coupling matrix grows (i.e., as the number of domains in a knowledge base increases), the number of possible couplings grows at a squared rate. Naturally, the average coupling level between any two domains can be expected to decline as the size of the coupling matrix increases. Our quartile ranking estimates took this effect into consideration by estimating the 25th, 50th, and 75th percentile values of coupling as a function of the size of a firm’s patent portfolio and time, since there may be changes in the median level of coupling over time.
The coupling for each class pair can be placed in one of four quartiles in both the earlier and later time periods. We classified an increase in coupling as significant when it changed from (1) a zero value9 in the earlier time period to the second,
third, or fourth quartile in the later period, (2) from the first quartile in the earlier time period to the third or fourth quartile in the later period, or (3) from the second to the fourth quartile. Likewise, a significant decrease was considered to occur when the coupling for a class pair changed from (4) the fourth quartile to a zero value or the first or second quartile, (5) from the third quartile to a zero value or the first quartile, or (6) from the second quartile to a zero coupling. A coupling change was then measured as the weighted number of technology class pairs that had undergone a significant change in coupling between the two time periods. The weight is equal topi +pj
/2+
(pi′+pj′)/2 where pi (resp. pj) and p ′
i (resp. p ′ j)
represent the percentage of patents that belong to technology class i (resp. j) during the t-6 to
t-4 and t-3 to t-1 periods, respectively. Change in couplings among existing knowledge domains
was measured as the change in coupling between domains that were present in both time periods.
Coupling between new and existing knowledge domains was measured as the changes resulting from the addition of couplings between these domains (see Table S1 for an illustration).
Domain complexity depends on the number of interdependencies between the domains in a firm’s consideration set. Since the exact level of inter-dependencies is unknown, we use a proxy mea-sure that is likely to be a close approximation of the true level of interdependence. If two domains are interdependent, a past search is likely to have combined searches across them, and the domains would thus be co-listed on a patent. We used all patents in the patent database to construct this mea-sure of interdependence. Since this data is very comprehensive, past searches should be a reli-able indicator of the presence of interdependence between domains. While past searches may be less accurate about which two domains are interdepen-dent, they would be more accurate at an aggregate level about how many other domains are interde-pendent with a focal domain. Accordingly, count-ing the number of other domains that have been co-listed with the focal domain can be used to measure that domain’s interdependence, or what Fleming and Sorenson (2001) call the potential for recombination (Etk).
Our estimate of Etk was based on the measure
developed by Fleming and Sorenson (2001). To maintain consistency with their measure, we mea-sured Etk at the technology subclass level rather
than at the class level. Since we used all patents in the patent database, it is an exogenous measure, unlike coupling, which is endogenous to the firm. We calculated it as the number of other subclasses with which a subclass had been combined (i.e., co-listed in a patent) in the previous five years divided by the number of patents assigned to that subclass in the same period:
Etk =
count of subclasses previously combined with subclass k count of previous patents in subclass k
(2) We then calculated the domain complexity (Ci,t)
of the technological environment in which firm i
engages in inventing during yeart as a weighted measure of the potential for recombination of each of the technological subclasses in which the firm has patents. The weights (gitk) for each
subclass are the fraction of patents held by focal firm i in each technological subclass k. Thus, we have
Domain complexity=Ci,t =
gitk×Etk (3)
When measuring complexity, we must examine the time period during which the firm is under-going change. We therefore calculated an average of this measure for each firm over the previous six years that correspond to the two time periods for which we calculate change:
Ci,t−6to t−1=
Ci,t−n
6 , n =1, 2, . . ., 6 (4) Although our measure of complexity is based on that of Fleming and Sorenson (2001), there is a difference in the way it is constructed owing to a difference in what is being measured. They measure the interdependence of the domains in a patent as the number of subclasses in a patent/ Etk, whereas we stop at calculating Etk
for a technology subclass. A minor difference between the two measures is that Fleming and Sorenson (2001) use data from the previous 10 years, whereas we use data from the previous five years to maximize the time period for our study. Also, using subclasses for the complexity measure and classes for the coupling measure does not lead to bias because what is being measured is different in these two cases: one is a measure of the environment, and the other is a measure of coupling at the firm level.
We incorporated patent information from 1976 to 2004. Because we require patent data from the previous 11 years (five previous years for the complexity-related variables and six previous years over which we are calculating change), the first year in our panel is 1987. Similarly, the need to observe at least five years’ worth of forward citations led us to calculate the effect of inventions only for patents that had been granted in or before 1999. Thus, we have an unbalanced panel data set for 1,750 firms over a 13-year period.
Control variables
A firm with a large knowledge base can experiment with more recombinations and thus may be more successful at technological search (Kogut and Zan-der, 1992). We therefore controlled for Size of knowledge base, measured as the total number of patents for which the firm had applied (and subse-quently been granted) in the previous three years. Previous work suggests that technological diversi-fication can have a positive effect on innovative capabilities (Garcia-Vega, 2006; Quintana-Garc´ıa
and Benavides-Velasco, 2008). In addition, it may influence the level of coupling between classes and the way in which such coupling changes over time. We therefore controlled for Technological diversification based on the Herfindahl index of concentration:
Technological diversificationi,t =1−
fitk2 ,
(5) wherefitk is the fraction of firmi’s patents that are
in patent classk during thet-3 tot-1 period. We also controlled for Use of new knowledge domains, since adding a new domain to a knowl-edge base may have an effect on the inventions that are generated (Ahuja and Lampert, 2001; Katila and Ahuja, 2002; Rosenkopf and Nerkar, 2001). Our measure for this variable was based on a firm’s technological position (Jaffe, 1986) in year t.Use of new knowledge domains was measured as the sum of the fractions for all technological classes
k that were new to the firm’s knowledge base in yeart compared with yeart-3:
Use of new knowledge domainsi, t=
fitk, (6)
wherek is a technology class in which the firm has patents in years t-3 tot-1, but not int-6 tot-4.
Because the number of citations that a patent receives varies significantly across technology classes, a firm may receive more citations simply because it generates patents that belong to more popular classes. To control for such cross-class differences, we created aMean technology citation control, as used by Fleming and Sorenson (2001). For each patent with issue date t, we considered all technology classes to which the patent had been assigned. For each such class, we calculated the average number of citations that patents in that class had received in the five-year window up to t (t-5.5 to t-0.5). We then weighted the term for each technology class to which the patent had been assigned by the proportion of assignments to that class. Note that the window used to consider the set of previous patents varied from patent to patent. Finally, we summed this measure for all patents for which a firm had applied in each year.
Changes in knowledge coupling may also occur in response to the external environment that is common to all firms in the population. A firm may increase (decrease) the level of coupling between
two classes because their degree of relatedness is known to be high (low). To control for this effect, we created two control measures based on the level of relatedness between classes in a firm’s knowledge base. The relatedness between technology classes j and k,Rj−k,t−3to t−1 , can
be calculated as
Rj−k,t−3to t−1=
njk
nj+nk−njk
, (7)
where nj is the number of patents in the patent
database assigned to class j, nk is the number of
patents assigned to classk, andnjkis the number of
patents assigned to both classes. In calculating this relatedness measure for each firm, we excluded that firm’s patents.
Relatedness existing-existing was measured for each firm as the weighted sum of relatedness between two classes that (1) existed in both time periods, and (2) were coupled with each other in either the early or later period, but not in both. This measure thus allowed us to control for the relatedness of class pairs in which a “tie” had been either added or dropped between the two classes. The weights are the same as those used in the coupling change measures. Relatedness existing-new was measured for each firm as the weighted sum of relatedness between two classes, only one of which existed in the first time period, whereas both existed and were coupled in the second. These measures allowed us to control for changes that may have been driven by the exogenous technological environment.
Assets (logged) were used as a measure ofFirm size to control for the effects of scale and scope on technology search and also for inertia in large firms, which may make a knowledge base rigid. FirmR&D intensity is a measure of the inputs to the technology search process (Chen and Miller, 2007). Firms that invest more in R&D generate more inventions, and it is thus necessary to control for this input measure. We measuredR&D intensity
as R&D expenditure divided by net sales. We included Firm past performance as a control variable because prior performance affects search behavior (Greve, 2003). Firm past performance
a significant effect on innovation performance (Ahuja and Katila, 2001). Hence, we included
Number of prior acquisitions as an additional control. Using data from the SDC Platinum database, we measured this variable as the number of acquisitions made by the focal firm in the previous three years.Year dummies were included to control for any time-varying effects, such as trends in patenting or citation rates, and for the longer time period over which patents from earlier years could be cited compared to those from later years.
The model
Because our dependent variable is a count vari-able with a high degree of variance relative to its mean, we used negative binomial regression analyses with random effects. We chose to use random effects rather than fixed effects because our key independent measures are change-related measures. Fixed effects estimation is based on within-firm changes in variables, which in this study would imply changes in changes. Because the changes that occur in a firm’s knowledge base may not vary sufficiently over time, fixed effects estimation may be inappropriate. One concern with random effects models is the problem of unob-served heterogeneity. To address this issue, we used the pre-sample fixed effects approach (Blun-dell, Griffith, and Van Reenen, 1999). Pre-sample estimators address the problem of unobserved het-erogeneity by generating an additional variable from pre-sample data rather than using a within estimator (Blundellet al., 1999). In this estimation, we used data from 10 years before the study period to create a pre-sample value of the dependent vari-able, Mean innovation performanceT-1 to T-10 and
then used this pre-sample variable as an addi-tional regressor. To build this variable, we took the mean of innovation performance for each firm over this 10-year period prior to the study period.
RESULTS
Table 1 presents the descriptive statistics and cor-relations for all variables. It can be seen that the
Mean technology citation controlhas a high degree of correlation with the dependent variable, which reemphasizes the need to control for cross-firm
variation in technological classes. Several of the strong correlations (such as Technological diver-sification and Relatedness existing) are an effect of knowledge base size. Larger organizations have greater scope for change than smaller organiza-tions, and hence it is important to control for knowledge base size.10Table 2 presents the results
of hypothesis testing for firm innovation perfor-mance. Model 1 in Table 2 shows the results for the control variables. In Model 2, we introduce the Change in coupling among existing knowledge domains variable, and the results indicate that it has a negative effect on innovation performance. Hypothesis 1b is thus supported. In Model 3, we include the interaction term with Domain com-plexity. While the coefficient for the change vari-able is negative and significant, the coefficient for the interaction term is positive and significant. In Model 4, we introduce the Coupling between new and existing knowledge domains variable, and it provides support for Hypothesis 2, which predicts that coupling between new and existing knowl-edge domains leads to inventions of greater value. In Model 5, we include the interaction term with
Domain complexity. While the coefficient of the change variable is positive and significant, the coefficient of the interaction term is negative and significant at the 10 percent level. In Model 6, we include all of the variables.
Since the coefficients of interaction terms in nonlinear models do not represent the true interac-tion effects (Hoetker, 2007; Wiersema and Bowen, 2009), we use graphical analysis to interpret the interactions in Model 6. We plot the margins for the entire range of values of Change in coupling among existing knowledge domains and two differ-ent values—high and low—of domain complexity in Figure 2a. This figure shows that Change in coupling among existing knowledge domains and innovation performance are negatively related at low levels of domain complexity and positively related at high levels of domain complexity. We plot the average marginal effects of Change in coupling among existing knowledge domains for various values ofDomain complexity in Figure 2b. Figure 2b shows that the average marginal effect of Change in coupling among existing knowledge domains is negative when domain complexity
10Multicollinearity checks with a linear regression model did not indicate any problems. All of the variance inflation factors were less than 10, and the condition number was less than 30.
S.
Y
a
yavaram
and
W.-R.
Chen
Table 1. Descriptive statistics and correlations
Variable Mean S.D. Min. Max. 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Firm innovation performance 353.91 1465.27 0 32, 007 2. Mean innovation
performanceT-1 to T-10/1,000
231.52 763.22 0 9510.82 0.80
3. Size of knowledge base 2.95 1.61 0.69 9.17 0.52 0.59 4. Firm size 5.80 2.28 −3.86 12.48 0.34 0.42 0.65 5. Firm past performance −0.04 0.37 −16.04 4.10 0.05 0.06 0.14 0.34 6. R&D intensity 1.97 31.19 0 1639.00 −0.01 −0.02 −0.03 −0.07 −0.20 7. Technological diversification 0.75 0.19 0 0.98 0.21 0.25 0.52 0.47 0.16 −0.05 8. Mean technology citation
control
73.57 342.74 0 10121.76 0.85 0.69 0.50 0.32 0.05 −0.01 0.19
9. Number of prior acquisitions 1.48 4.56 0 119 0.29 0.31 0.31 0.39 0.08 −0.02 0.17 0.34 10. Relatedness existing-existing 0.11 0.15 0 1.19 0.31 0.39 0.62 0.40 0.05 0.00 0.42 0.26 0.18 11. Relatedness existing-new 0.10 0.15 0 1.81 −0.04 −0.09 0.04 −0.11 −0.10 0.08 0.10 −0.04 −0.05 0.08 12. Use of new knowledge domains 0.58 0.51 0 4.00 −0.19 −0.24 −0.42 −0.15 0.02 −0.03 0.18 −0.18 −0.10 −0.35 0.14 13. Domain complexity 3.00 1.06 0.57 10.73 0.02 0.08 0.17 0.22 0.06 −0.01 0.32 −0.01 0.06 0.39 0.17 0.04 14. Change in coupling among
existing knowledge domains
3.91 6.24 0 46.25 0.58 0.72 0.78 0.53 0.08 −0.02 0.46 0.53 0.31 0.73 −0.02 −0.37 0.24
15 Coupling between new and existing knowledge domains
2.00 2.12 0 36.83 0.05 0.01 0.22 0.13 0.03 −0.01 0.42 0.04 0.03 0.22 0.47 0.25 0.23 0.20
n=9,209 firm years
p<0.05 for all values≥0.03 and≤-0.03
2013
John
W
iley
&
Sons,
L
td.
Strat.
Mgmt.
J.
,
36
:
377
–
396
(2015)
DOI:
Table 2. Random-effects negative binomial analyses for firm innovation performance
Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Mean innovation
performanceT-1 to T-10/1,000
−0.157*** −0.139*** −0.123*** −0.147*** −0.145*** −0.112***
(0.026) (0.027) (0.027) (0.026) (0.026) (0.027)
Size of knowledge base 0.680*** 0.691*** 0.695*** 0.674*** 0.673*** 0.688***
(0.013) (0.014) (0.014) (0.013) (0.013) (0.014)
Firm size −0.009 −0.010 −0.008 −0.007 −0.008 −0.006
(0.008) (0.008) (0.008) (0.008) (0.008) (0.008)
Firm past performance 0.289*** 0.286*** 0.277*** 0.280*** 0.279*** 0.269***
(0.049) (0.049) (0.049) (0.049) (0.049) (0.049)
R&D intensity/1,000 −0.576 −0.580 −0.618 −0.513 −0.547 −0.593
(0.438) (0.440) (0.436) (0.438) (0.438) (0.435)
Technological diversification 0.507*** 0.543*** 0.614*** 0.450*** 0.431*** 0.539***
(0.093) (0.094) (0.095) (0.093) (0.094) (0.096)
Mean technology citation control/1,000
0.143*** 0.146*** 0.165*** 0.142*** 0.142*** 0.163***
(0.015) (0.015) (0.015) (0.015) (0.015) (0.015)
Number of prior acquisitions −0.008*** −0.008*** −0.007*** −0.008*** −0.008*** −0.007***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Relatedness existing-existing 0.074 0.185 0.136 0.043 0.050 0.117
(0.086) (0.095) (0.095) (0.087) (0.087) (0.095)
Relatedness existing-new 0.386*** 0.370*** 0.387*** 0.233*** 0.251** 0.264***
(0.067) (0.067) (0.067) (0.076) (0.077) (0.078)
Use of new knowledge domains −0.047 −0.054 −0.055 −0.080** −0.082** −0.086**
(0.030) (0.030) (0.030) (0.031) (0.031) (0.031)
Domain complexity −0.034* −0.032* −0.089*** −0.037** −0.021 −0.074***
(0.013) (0.013) (0.016) (0.013) (0.016) (0.018)
Change in coupling among existing knowledge domains
−0.008** −0.039*** −0.037***
(0.003) (0.006) (0.006)
Change in coupling among existing knowledge
domains×domain complexity
0.009*** 0.008***
(0.001) (0.001)
Coupling between new and existing knowledge domains
0.024*** 0.048*** 0.046***
(0.005) (0.014) (0.014)
Coupling between new and existing knowledge
domains×domain complexity
−0.007† −0.007†
(0.004) (0.004)
Constant −1.828*** −1.872*** −1.793*** −1.781*** −1.815*** −1.785***
(0.073) (0.075) (0.075) (0.073) (0.075) (0.077)
Log likelihood −43589.05 −43585.32 −43566.49 −43578.25 −43576.45 −43555.74
Standard errors in parentheses; 9,209 observations and 1,750 groups. All models include year dummies (results not displayed). †p<0.10; *p<0.05; **p<0.01; ***p<0.001
is low and positive when domain complexity is high. Thus, these two graphs provide support for Hypothesis 3, which states that domain complex-ity attenuates the negative effects of Change in coupling among existing knowledge domains on innovation performance.
We plot the margins in Figure 2c for the entire range of values of Coupling between new and existing knowledge domains and two different values—high and low—of domain complexity. Figure 2c shows that Coupling between new
and existing knowledge domains and innovation performance are positively related at low levels of domain complexity, but this relationship weakens at higher levels of domain complexity. We plot the average marginal effects ofCoupling between new and existing knowledge domains for various values of Domain complexity in Figure 2d. This figure shows that the average marginal effect of
Coupling between new and existing knowledge domains is positive at low to medium levels of domain complexity. The average marginal
-.1
-.05
0
.05
.1
Ef
fects on linear prediction
0 1 2 3 4 5 6 7 8 9 10 11
Domain complexity
-.5
0
.5
1
Linear prediction
0 5 10 15 20
Couplings between new and existing knowledge domains
Low (Mean - 2 S.D.) High (Mean + 2 S.D.) Domain complexity
-.05
0
.05
.1
Ef
fects on linear prediction
0 1 2 3 4 5 6 7 8 9 10 11
Domain complexity
-.8
-.6
-.4
-.2
0
.2
Linear prediction
0 5 10 15 20
Change in couplings among existing knowledge domains
Low (Mean - 2 S.D.) High (Mean + 2 S.D.) Domain complexity
(c) (a)
(d) (b)
Figure 2. Interactions between change in couplings and domain complexity. (a) Predictive margins with 95 percent confidence intervals. (b) Average marginal effects of change in couplings among existing knowledge domains with 95 percent confidence intervals. (c) Predictive margins with 95 percent confidence intervals. (d) Average marginal effects
of couplings between new and existing knowledge domains with 95 percent confidence intervals
effect turns negative at high levels of domain complexity, but the confidence intervals show that it is not significantly different from zero at these levels. Thus, these two graphs provide weak support for Hypothesis 4, which predicts that domain complexity will negatively moderate the effect of couplings between new and existing knowledge domains on innovation performance.
We performed several sensitivity analyses (see Table S2). To further alleviate concerns over unobserved heterogeneity, we also performed a fixed effects estimation. As can be seen from Model 1 in Table S2, the results were almost the same. We next varied the windows for our independent variables. Our measure of a firm’s knowledge couplings was based on all of the patents that it had accumulated between t-3 and
t-1. We also considered alternative windows of
were largely the same (Model 4 in Table S2). We used subclasses to measure Etk to maintain
consistency with Fleming and Sorenson (2001). To check the sensitivity of the results, we also constructed a new class-based measure. With this new measure (Model 5 in Table S2), Hypotheses 1b, 2, and 3 were supported, but Hypothesis 4 was not. Yayavaram and Ahuja (2008) showed that the level of decomposability of a firm’s knowledge base can affect both the number of citations and changes in couplings. To ensure that our results were not driven by these relationships, we included the Level of decomposability and
Level of decomposability squared in Model 6 (Table S2). Their inclusion did not affect the results.
Firms may decide to make changes in antic-ipation of superior innovation performance. One such expectation is due to the nature of technol-ogy itself. For instance, a firm that increases the coupling between classes A and B may do so because prior external research has revealed that combining these two classes will lead to superior innovations. Relatedness variables were included to control for this possibility. We also checked the results with a two-stage model. In the first stage (not reported here), we used a logit model with a dependent variable that took the value of one if a firm entered a new technological class, and zero otherwise. The independent variables were
Relatedness existing-new,Size of knowledge base,
Firm size, Firm past performance,R&D intensity, and Technological complexity. We then included the Inverse Mills ratio in our baseline model in the second stage. The results from this model (Model 7 in Table S2) were similar to our base results.
As a robustness check, we also ran a model (Model 8 in Table S2) that included the three other types of coupling change variables (in addition to the two considered thus far), their interactions with domain complexity and the corresponding control variables for changes in knowledge domains and relatedness. Given the large number of change-related variables and interactions included in this model, there may be multicollinearity problems, and we thus urge caution in interpreting its results. The model finds support for all four hypotheses. In addition, it also shows that the three other types of change have no significant effect on firm innovation performance. When a firm enters, say, two new classes, the coupling between them does
not seem to affect firm innovation performance. What matters more is how these new classes are integrated with existing firm knowledge. Dis-continuing technologies and their coupling also appears to have no effect on such performance. Given the emphasis on forgetting in the learning literature (Argote, 1999), this is a surprising find-ing. Additional tests are necessary to determine the conditions under which such forgetting improves firm innovation performance. Finally, we also dis-aggregated the coupling changes among existing knowledge domains into coupling increases and decreases. The results (Model 9 in Table S2) show that the effect on innovation outcomes is the same for both increases and decreases, which justifies our argument that it is coupling changes rather than increases or decreases in coupling that affect innovation outcomes.
DISCUSSION AND CONCLUSION
Firms must change their knowledge bases over time to keep pace with the changing technolog-ical environment and to avoid exhausting combi-nations. At the same time, attempts to change often fail to produce the desired benefits because of dis-ruptions in the information-process routines and problem-solving approaches used within the firm. The many ways of changing a firm’s knowledge base and the difficulties a firm faces in undertak-ing knowledge renewal should make knowledge-base change a prominent discussion area in the fields of innovation management and technological search. In contrast to previous research focusing on changes in knowledge domains, in this paper we investigate changes in the knowledge couplings that a firm makes between its knowledge domains. A change in couplings implies a change in the knowledge combinations a firm considers. Hence, coupling changes are likely to have a significant influence on innovation performance. We argue that to understand how changes in knowledge cou-plings affect innovation performance, it is neces-sary to disaggregate these changes into different types based on whether the knowledge domains involved are new or familiar to the firm. Further, it is also important to consider the technological environment, which may have an effect on the ben-efits and costs of a coupling change.
Our findings reveal that a change in the cou-plings among existing knowledge domains leads
to poorer firm innovation performance. Although such coupling changes have both benefits and costs, our results suggest that the costs of man-aging change are likely to outweigh the benefits of new combinations or a different mix of com-binations. However, when there is a high degree of domain complexity (i.e., there are numerous interdependencies among technologies), the neg-ative effects of change are attenuated. Our results also show that coupling between new and existing knowledge domains leads to invention outcomes of greater value. Compared to changes involv-ing existinvolv-ing knowledge domains alone, those that involve new knowledge domains are easier to man-age. The presence of new knowledge makes it easier for firms to recognize and accept the need for change. Coupling existing and new knowledge domains may break cognitive frames and prompt a firm to consider recombinations that it has not con-sidered before. Further, whereas a local search with existing knowledge domains makes it difficult for a firm to escape competency traps, a local search involving new knowledge can lead to the explo-ration of nonlocal neighborhoods, although domain complexity can render the process more difficult. When domain complexity is high, the difficulty of understanding the potential relationships between existing and relatively unfamiliar new domains may impair the search process. Hence, coupling existing and new knowledge domains is less beneficial when the level of domain complexity is high.
This paper contributes to the fields of innovation management and technological search in several ways. First, we consider the changes in knowledge couplings rather than the changes in knowledge domains. The prior technological search literature conceptualizes a firm’s knowledge base as a set of knowledge domains. For example, a firm’s knowledge base can be defined by placing its existing patents (Jaffe, 1986; Katila and Ahuja, 2002; Rosenkopf and Nerkar, 2001; Silverman, 1999; Stuart and Podolny, 1996), R&D expen-ditures (Chen and Miller, 2007; Greve, 2003; Helfat, 1994), or human resources (Chang, 1996) into various categories, with each representing a knowledge domain. In this paper, we argue that, in addition to these domains, a firm’s knowledge also resides in the combinative relationships or coupling between domains (Galunic and Rodan, 1998; Kogut and Zander, 1992; Yayavaram and Ahuja, 2008). By considering the changes in these
couplings, we gain insight into important factors affecting technological search.
Second, our focus on knowledge couplings allows us to distinguish between a change that occurs in the couplings among existing domains and one that occurs because of the addition of new domains. It is important to distinguish between these types of change because they may affect the technological search in different ways, as borne out by our empirical findings. Third, the distinction we make between couplings that are endogenous to the firm and interdependencies that are exoge-nous makes it possible for us to investigate how the effects of firm-level coupling changes are moderated by interdependencies in the technolog-ical environment. More speciftechnolog-ically, we find that domain complexity determines which type of cou-pling change is beneficial. Changing the coucou-plings between existing knowledge domains makes use of the wide number of possible and unexplored combinations that exist in a complex technological environment, whereas the coupling of new and existing knowledge domains yields better results in a simple technological environment in which it is easier to understand the relationships between those domains. Previous studies have explored the difficulties that firms encounter in undertaking an architectural change in product development within specific industries or environments (Hen-derson and Clark, 1990). We go a step further by considering the effects of coupling changes across industries and the influence of contextual factors on search strategy success. Our results show that the environment determines whether or not a knowledge change will be beneficial.
This paper has two principal limitations. First, we characterize a firm’s knowledge base by the patents that it possesses. As has been widely noted, not all firm knowledge is patented or even patentable. Patents provide, at best, a weak measure of a firm’s technical knowledge base, but there are few other information sources from which to build a multi-year sample that includes several industries. Second, our findings are derived from patent-intensive firms whose knowledge paths can be traced, and they should therefore be interpreted with caution.
