transfer in context: replication and adaptation in knowledge transfer relationships

Strategic Management JournalStrat. Mgmt. J., 28: 867–889 (2007)

Published online 4 May 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/smj.614

Received 4 October 2004; Final revision received 6 November 2006

TRANSFER IN CONTEXT: REPLICATION ANDADAPTATION IN KNOWLEDGE TRANSFERRELATIONSHIPS

CHARLES WILLIAMS*Fuqua School of Business, Duke University, Durham, North Carolina, U.S.A.

This paper explores the role of replication and adaptation in knowledge transfer relationships. Idevelop a model of knowledge transfer in which firms replicate because knowledge is ambiguousand adapt because knowledge depends on context. In the model, firms replicate more whenknowledge is discrete and adapt more when they understand the interactions between differentareas of knowledge. Replication and adaptation lead to successful knowledge transfer, whichleads to improved performance of the receiving unit. The predictions are tested using a survey ofcross-border knowledge transfer relationships among firms in the telecommunications industry.The results are largely consistent with the model and point to potential areas for futureresearch, such as the drivers of replication, the depreciation rate of knowledge, and the roleof understanding in organizational knowledge. Copyright 2007 John Wiley & Sons, Ltd.

This paper presents a model of knowledge trans-fer that follows from the dual nature of knowl-edge—the fact that it is both ambiguous andcontext-dependent. Recognizing these two aspectsof knowledge has several implications for knowl-edge transfer relationships. First, knowledge trans-fer will usually involve knowledge that requiresreplication and knowledge that requires adaptation.Second, if firms combine replication and adapta-tion in knowledge transfer, we need to change ourunderstanding of replication in the transfer rela-tionship as the absence of adaptation. I develop anapproach to knowledge transfer that incorporatesthese insights and test this model on a sample ofknowledge transfer relationships in telecommuni-cations services.

Keywords: knowledge transfer; replication; adaptation;evolution*Correspondence to: Charles Williams, Fuqua School of Busi-ness, Duke University, Box 90120, Durham, NC 27708, U.S.A.E-mail: [email protected]

The idea that organizational knowledge is tacitand ambiguous has played a central role in researchon strategy and organizations (Kogut and Zander,1992; Nelson and Winter, 1982). Causal ambiguityis inherent to most complex production processes(Lippman and Rumelt, 1982), so firm membersoften do not understand the root causes of firmperformance or the interaction between individ-ual activities. Replication, which is effort towardsexact copying of a set of activities, enables thetransfer of those activities without the need tounderstand their causes, consequences, and inter-dependence. Thus, researchers have proposed thatfirms replicate knowledge to transfer it in the faceof ambiguity (Winter and Szulanski, 2002).

Management scholars have also recognized thecontext-dependent nature of knowledge in organi-zations (Argote and Ingram, 2000; Kostova andRoth, 2002; Prahalad and Doz, 1987). The richconnection between organizations and their envi-ronment is a long-standing concern of organiza-tional scholars (Henderson and Mitchell, 1997;

Copyright 2007 John Wiley & Sons, Ltd.

868 C. Williams

Nelson and Winter, 1982; Tushman and Anderson,1986), and these links determine the compositionof knowledge in the organization and its fit withthe new environment. To transfer knowledge effec-tively in the face of context dependence, firmsmust adapt knowledge to a new setting.

These insights into knowledge have arisen inseparate streams, however, so studies tend toemphasize either the need for replication or theneed for adaptation. In this study, I find that firmsare often engaged in both careful copying of prac-tices from a partner and significant adaptation ofpractices. Accounting and reporting measures, forexample, are frequently replicated because they arehighly interdependent with the transferring partner.On the other hand, customer service practices andmarketing are frequently adapted because of theextent to which they interact with the unique envi-ronment of each location. In other cases, replica-tion and adaptation are combined within the sameset of practices. A set of Latin American telephonecompanies balance replication and adaptation intheir human resource practices. The firms replicatea set of common programs for the rotation, devel-opment, and compensation of managers. Each firm,however, adapts the exact positions and levels ofthe hierarchy to its own institutional environment.

The model of knowledge transfer presented inthis paper recognizes the joint role that replica-tion and adaptation play in a knowledge transferrelationship. In the model, replication rises whenknowledge is more discrete, while adaptation riseswhen the receiving firm understands the knowl-edge it is getting. Replication and adaptation thenlead to increased knowledge transfer, which in turnleads to greater performance gains at the receivingunit.

I study knowledge transfer relationships in thecontext of the telecommunications industry. Aradically new set of tools—digital switching, fiberoptic transmission, data transmission protocols,and wireless communications—have transformedthe telecom sector. These new technologies arenecessary to build a modern communicationsindustry, but companies in less developedmarkets have found that technology alone isnot sufficient. These firms have found theyalso require complementary knowledge, in theform of practices and information, to providemodern services. To acquire this complementaryknowledge, many firms used the impetus ofregulatory reform to form knowledge transfer

relationships with industry leaders. To gather dataon these relationships, I surveyed relationshippartners.

The data from these surveys are largely consis-tent with the hypotheses presented in the paper,suggesting that replication and adaptation con-tribute to knowledge transfer, and that transfercontributes to improvements in receiver perfor-mance. The results also suggest that the most sig-nificant factor for facilitating adaptation is expe-rience within the relationship, rather than priortransfer experience. Robustness analysis suggeststhat the performance gains from knowledge trans-fer depreciate rapidly. The evidence is not con-sistent with the hypothesized relationship betweendiscrete knowledge and replication: it suggests thatfirms manage interdependence not by reducingreplication, but by increasing adaptation.

A MODEL OF KNOWLEDGETRANSFER

This section lays out five hypotheses, which formthe basis of a model of knowledge transfer. I beginby outlining the primary aspects of organizationalknowledge described by prior research, and howthey suggest the need to replicate and adapt knowl-edge in the transfer process. I then discuss how toreconcile the use of replication and adaptation inthe knowledge transfer process. I propose two con-tingencies that lead to higher levels of replicationand adaptation, respectively. Finally, I hypothe-size that replication and adaptation contribute toknowledge transfer, which then leads to perfor-mance improvements at the receiving unit.

Most theorists locate organizational knowledgein the actions of the organization. Nelson and Win-ter (1982: 99) ‘propose that organizations remem-ber by doing.’ Similarly, Spender (1996) empha-sizes that knowledge is inextricably linked to col-lective action in organizations. Nonaka (1994: 16)also suggests that ‘knowledge is deeply rooted inaction.’ Thus, firms possess knowledge only if theycan put it into action.

Other approaches to knowledge focus on theintegration of diverse components of knowl-edge. Argote and Ingram (2000) propose thatknowledge resides in three reservoirs within thefirm—members, tools, and tasks—and in the net-works that link them. In their framework, firms’performance depends on the extent to which these

Copyright 2007 John Wiley & Sons, Ltd. Strat. Mgmt. J., 28: 867–889 (2007)DOI: 10.1002/smj

Transfer in Context 869

networks are compatible with each other and withthe external environment. In a similar vein, Grant(1996) argues that knowledge resides in the orga-nization’s ability to take individual knowledge andintegrate it.

These aspects of knowledge suggest two keycharacteristics of organizational knowledge: causalambiguity and context dependence. Causal ambi-guity (Lippman and Rumelt, 1982) arises becauseknowledge is embodied in the repeated activi-ties of the organization, known as routines. Rou-tines link together the actions of organizationmembers, who may not understand, or even beaware of, actions elsewhere in the chain. Sincethese chains are long and incompletely understood,no member of the organization will completelyunderstand the relationship between an organiza-tion’s actions and outcomes (Nelson and Win-ter, 1982). On the other hand, context depen-dence arises because knowledge integrates com-ponents of knowledge—such as people, personalnetworks, or information—which vary betweensettings. Since compatibility between componentsis necessary for knowledge to be effective (Argoteand Ingram, 2000), context will lead knowledge tovary in order to achieve similar outcomes whileintegrating differing components.

Since ambiguous causality and context depen-dence are fundamental aspects of organizationalknowledge, knowledge transfer requires firms toresolve a number of uncertain relationships: therelationship between an activity and its outcome,the relationship between an activity and the restof the organization, and the relationship betweenan activity and the firm’s environment. Resolutionof these relationships will involve a continuousprocess of modification and observation: change apractice, observe outcomes, observe interactions,change again. By applying iterative changes atthe receiving unit followed by observations at thesource and receiving units, firms can graduallycopy practices from the source and integrate themwith the new context, thus accomplishing knowl-edge transfer.

To study firms’ approach to this process, I mea-sure their transfer activities (replication and adap-tation) and the outcome of those activities (knowl-edge transfer) in two defined periods: the firstyear of the knowledge transfer relationship, whichvaries between the firms, and the year of the sur-vey, 2001. Data from the first year are used to testthe structural model, which includes simultaneous

relationships between replication and adaptation,knowledge transfer, and performance. Data from2001 are used to compare the contingencies drivingreplication and adaptation early in the relationshipto later periods.

This approach to knowledge transfer differsfrom recent studies in two key ways: the focus isthe knowledge transfer relationship rather than thetransfer of a single practice, and knowledge trans-fer is treated as the outcome of the process ratherthan a distinct point in the process. While recentstudies of knowledge transfer focus on the trans-fer of a single, targeted practice (e.g., Kostova andRoth, 2002; Szulanski, 1996), knowledge transferrelationships frequently encompass a wide varietyof areas targeted for transfer (Argote, 1999). Forinstance, the firms in this study face simultane-ous pressure to expand networks, improve servicequality, establish positive customer relationships,and eliminate waiting lists for service, so they turnto their partner for many types of knowledge. Tomanage the complexity of these multiple links inthe knowledge transfer relationship, firms need toapproach the transfer relationship with a consistentstrategy, and the nature of these strategies is thefocus of this study.

When a firm is engaged in a knowledge trans-fer relationship, it must decide how to allocate itseffort with regard to two competing goals: chang-ing the receiving unit’s operations to be more likeits partner’s—replication—and changing its oper-ations to integrate with local context—adaptation.Theoretical approaches to replication have empha-sized the costly mistakes created by adaptation(Winter, 1995; Winter and Szulanski, 1998, 2002).From this perspective, replication reduces or rulesout adaptation, which poses problems if, in addi-tion to causal ambiguity, organizational knowledgeexhibits context dependence. Though replicationand adaptation are typically understood as oppos-ing approaches to transfer, this study examines thepossibility that firms might use them together.

The joint use of replication and adaptation sug-gests an apparent contradiction between copying apractice exactly and modifying it: at some fun-damental level, exact copying and modificationmight be mutually exclusive. I resolve this con-flict by treating replication and adaptation as efforttowards a goal. Thus, I define replication as effortaimed at creating activities at one location thatare identical to those at another location. Sim-ilarly, I define adaptation as effort toward the


870 C. Williams

goal of modifying or combining practices from asource unit. Replication and adaptation are modes,then, by which organizations accomplish knowl-edge transfer, which I define as the acquisitionfrom another unit of useful information or prac-tices. This approach allows for combined modesof transfer: an organization can invest substantialeffort towards exact copying, but still invest someeffort in adaptation in areas where exact copyingis difficult or impossible. This might arise if afirm is working to copy a complex and successfulset of practices for dispatching network mainte-nance teams quickly, but also needs to adapt themto the nature of local infrastructure—for instance,whether lines are strung above ground on poles orunderground in sewers.

Whether firms typically combine these modesof transfer or whether replication arises separatelyfrom adaptation is a question that can be empiri-cally resolved. To distinguish whether replicationand adaptation are two ends of a single spectrum oftransfer practices or two separate modes of trans-fer, we can test whether they vary together or sep-arately. If replication and adaptation are mutuallyexclusive transfer strategies then the two will varyinversely—as replication rises, adaptation will fall,and vice versa. In this case, they can be representedas opposite indicators of a single construct of trans-fer mode. In contrast, when replication is definedas effort they may vary separately—replicationmay rise without affecting adaptation and viceversa. If the two vary independently, a factormodel with two transfer modes will fit the databetter than one with a single construct of transfermode. This leads to my first hypothesis.

Hypothesis 1: Replication and adaptation by thereceiving unit represent two separate constructsrather than a single continuum of transfer mode.

Levels of replication and adaptation

If replication and adaptation vary separately, spe-cific contingencies will lead them to rise and fall. Ipropose that replication will rise when knowledgeis more discrete and adaptation will rise when firmsunderstand the interactions between different areasof knowledge.

A firm’s knowledge is more discrete when itis more self-contained, with fewer connectionsto actors outside the firm, such as customers,suppliers, or local institutions and infrastructure.

When knowledge is more discrete, the costs ofreplication are lower because the unit may copya set of activities with less consideration of theirexternal links. It will be harder and more expensiveto replicate knowledge that is less discrete becauseit is intertwined with areas outside the firm. If afirm replicates highly interconnected knowledgewithout context or related areas of knowledge,it will find this knowledge less well integrated,and less useful. It follows, then, that the cost ofreplicating knowledge falls as knowledge becomesmore discrete. As these costs fall, I expect thatfirms will invest more in replication.

Hypothesis 2: When knowledge to be transferredis more discrete, replication by the receiving unitwill be higher.

Adaptation, on the other hand, can be costlybecause of the challenge of predicting interactionsbetween interdependent knowledge in the organi-zation (Winter and Szulanski, 2002). When a firmmakes more accurate predictions of the interactionbetween a potential business practice and otherparts of the organization, adaptation will be lesscostly. In interviews, some participants in transferrelationships pointed to the ability to understandinterdependence as an essential practice for suc-cessful knowledge transfer. I define understand-ing of knowledge to represent two capabilities: theability to predict how a change in one area ofknowledge will affect other parts of the organi-zation and the ability to identify the essential ele-ments of a set of practices. Understanding enablesthe firm to tailor changes by anticipating inter-dependence and thereby reduces the unintendedproblems from adaptation. Increasing the effective-ness of adaptation reduces the costs of adaptation.Thus, I predict adaptation will increase with under-standing.

Hypothesis 3: When the units possess greaterunderstanding of their knowledge, adaptation bythe receiving unit will be higher.

A structural model of transfer andperformance

I have proposed that adaptation and replication areseparate mechanisms for transfer and that firmsvary their use based on contingent factors of dis-crete knowledge and understanding. Next, I present



three hypotheses that complete a structural model.The full model begins in the previous section withthe contingencies leading to higher replication oradaptation. The next two hypotheses propose thatreplication and adaptation will each contribute toknowledge transfer. The final hypothesis proposesthat knowledge transfer will lead to subsequentimprovements in performance at the receiving unit.

Replication leads to knowledge transfer becauseit is necessary to create a new workingcopy of complex and ambiguous knowledge(Winter, 1995). When knowledge is complex andcausally ambiguous, a firm may not be able topredict which elements of the knowledge areessential for its effective operation. By investingsignificant effort in copying a set of practicesexactly—replicating—a firm insures that thetransferred practices contain any elements of theknowledge that might turn out to be essential.A set of practices with all essential elementsis more likely to be effective, and when newpractices are more effective they will be morevaluable to the receiving partner. This representsincreased knowledge transfer. Thus, the more areceiving unit invests in replication the greater theknowledge transfer it will accomplish.

Hypothesis 4a: The greater the level of replica-tion by the receiving unit, the greater the knowl-edge transfer to the receiving unit.

Transfer only helps firms if the knowledge issuited to the new context. Many lines of researchon strategy, technology, and international businesssuggest that knowledge depends on its context.Argote and Ingram (2000: 159) argue that com-patibility between networks of knowledge reser-voirs is difficult to replicate in a new environ-ment. Winter (1995: 154) points out that repli-cation will often be accompanied by adaptation:‘In many actual cases the objective is not exactreplication but partial replication, accompanied byadaptive or innovative change in some routines.’Allen (1977) proposes that firms require techno-logical gatekeepers because technical knowledgefrom other organizations needs to be translatedfor use within the firm. Leonard-Barton (1988)finds that the implementation of a new productiontechnology requires mutual adaptation between thetechnology and organization to such an extent thatit is best understood as an extension of the inno-vation process. Empirical studies have found that

multinationals adapt human resource practices tolocal institutions (Kostova and Roth, 2002).

Adaptation increases the total amount of knowl-edge transfer because it reduces the cost of trans-ferring a valuable set of practices. Without adapta-tion, replication has inconvenient cascading impli-cations. When using high levels of replicationwithout any adaptation, a firm that targets a setof practices for transfer will also need to replicateany activities that are linked with the focal prac-tices. These linked activities will have links of theirown, creating a widening set of practices that mustbe replicated simply to acquire the original target.Adaptation can reduce the cost of transfer by mod-ifying the connections between a set of practicesand the rest of the organization or environment,allowing those practices to be targeted withoutreplicating related areas. Adaptation enables firmsto focus on potentially valuable knowledge andintegrate it with the new context.

In addition, adaptation can make transferredknowledge more effective. The effectiveness oforganizational knowledge depends on the integra-tion of diverse components of knowledge (Argoteand Ingram, 2000; Grant, 1996). Some of thesediverse components of knowledge must vary innew contexts, so specific practices will need tochange to integrate them. For instance, if thelegacy switching equipment for the existing tele-phone network is made by a local supplier, thenthe firm’s management systems must be changedto monitor and manage the legacy equipment.

Thus, when firms engage in adaptive efforts theywill be able to transfer more knowledge and makethat knowledge more effective in the new context.

Hypothesis 4b: The greater the level of adapta-tion by the receiving unit, the greater the knowl-edge transfer to the receiving unit.

Scholars generally posit that knowledge transferis a key source of competitive advantage, butthe hypothesized link between knowledge transferand performance is rarely verified. Kogut andZander (1992) argue that successful firms mustbe able to replicate their organizational knowledgeto grow. Winter proposes that knowledge is astrategic asset (Winter, 1987) and a source of rentsfor firms (Winter, 1995). Argote and Ingram (2000)hypothesize that knowledge transfer is a basisfor the competitive advantage of firms, since theeffectiveness of transfer varies considerably among


872 C. Williams

organizations. Still, most studies of knowledgetransfer either focus on transfer alone throughsurvey measures (Gupta and Govindarajan, 2000;Szulanski, 1996; Winter and Szulanski, 1998) oruse performance improvements in the presenceof transfer relationships to proxy for knowledgetransfer (Ahuja, 2000; Argote, 1999; Baum andIngram, 1998; Darr, Argote, and Epple, 1995).

Knowledge transfer leads to performanceimprovements because the valuable new practicesand information enable the firm to accomplishits goals more quickly and at lower cost, andeven accomplish new goals. Nelson and Winter(1982: 123) point out that access to a sourceunit’s template is a key ingredient for successfulknowledge transfer, otherwise ‘[w]hen problemsarise in the copy, it is not possible to resolvethem by closer scrutiny of the original.’ Withoutaccess to the template, a firm that wishes toacquire knowledge has little information to workwith. Since the most important elements oforganizational knowledge tend to be tacit, theability to observe, ask, articulate, and check is anessential element of improving the effectiveness ofpractices through knowledge transfer relationships.Because of the ability to clarify and explorecomplex relationships in the template, knowledgetransfer relationships help firms acquire the mostcomplex, and often most valuable, practices, whichwill improve the performance of the receiving unit.

Even when an organization is not engaged inwide-scale copying of practices, information froma successful partner can help solve pressing prob-lems an organization faces. When firms face prob-lems, they search their information environmentfor potential solutions (Cyert and March, 1992;Nelson and Winter, 1982). The knowledge transferrelationship gives the receiving firm more infor-mation on potential solutions, which improves thechanges it makes.

Hypothesis 5: Greater knowledge transfer willlead to higher performance at the receiving unit.

When we recognize that ambiguity and contextdependence arise from the fundamental nature oforganizational knowledge, a more complete pictureof the knowledge transfer process emerges. Firmsreplicate because some knowledge is ambiguousand adapt because some knowledge depends oncontext. While the two transfer modes have beentreated as mutually exclusive, I define them as

effort toward copying and modifying practices,which allows for replication and adaptation inknowledge transfer. In the model, replication riseswhen knowledge is more discrete while adaptationrises when firms understand interactions betweenknowledge and context. Replication and adaptationeach contribute to knowledge transfer, and knowl-edge transfer leads to higher performance at thereceiving unit.

DATA AND METHODS

This section explains my empirical approach. Idiscuss the advantages of the empirical settingfor studying knowledge transfer. I explain thedata collection methods and statistical analysis.Finally, I describe the variables used to representthe primary constructs.

Empirical setting

I test the theoretical model in the context of inter-national transfer relationships among telecommu-nications service firms. These relationships have anumber of characteristics that make them an inter-esting setting to study knowledge transfer. Therelationships were formed with the express purposeof transferring knowledge, and the source partnerswere frequently chosen for their ability to transferknowledge. There is a clear source of knowledgeand receiver of knowledge in these settings, so theoutcome of the relationship is more straightforwardthan in other relationships where transfer can benon-cooperative and in both directions. In addi-tion, the telecom service industry features a highlevel of tacit knowledge that firms must employto provide service effectively. There is an arrayof advanced equipment, but complementary orga-nizational knowledge is required to operate theequipment effectively.

The rapid diffusion of new telecommunicationsequipment also means that the operating routinesof advanced telecom firms have some relevanceto firms in less developed markets. On the otherhand, the industry still features multi-domesticcompetition, in which coordinated internationalstrategies do not play an important role. Thus,replication is less likely to be imposed by the needfor coordination.



Data collection

I use a survey and additional archival measuresas sources of data on the firms and settings forthese transfer relationships. The population of rela-tionships studied here are cross-border investmentsin fixed-telephone and cellular service providers. Itargeted firms in these relationships using infor-mation from Pyramid Research (a former divisionof the Economist Intelligence Unit that performsmarket research on telecommunications marketsaround the world) and RCR Wireless, a pub-lisher focused on the wireless industry. I definedthe relationships as those in which a multina-tional telecommunications firm invested (minor-ity or majority stake) in a local telecommunica-tions firm. I gathered contact information for 324unique relationships using RCR’s Global Wire-less Database, Dunn & Bradstreet, and companyreports.

I developed the survey through a modifiedversion of Dillman’s Tailored Design Method(Dillman, 2000). The survey asked respondentsto rate each item on a five-point Likert scale(agree/disagree) for the current year (2001) andfor the first year of the relationship (which variesby firm). These items served as indicators for theconstructs of replication, adaptation, and knowl-edge transfer (see Variables section). I mailed thesurveys to the top decision-maker at each of thefirms in two batches in late summer and early fall2001. Follow-up to the survey included a post card3 weeks after the survey mailing, phone calls to thetargeted respondent, and regular emails to thosewho planned to complete the survey but had notyet returned it.

At the end of this process, I received 62 com-pleted surveys for a final response rate of 19percent. Two of those responses were from rela-tionships begun 30–40 years earlier, so I did notinclude them in the final analysis. I compared therespondents to the 324 targets for three countrycharacteristics: gross national income (GNI), pop-ulation, and telephone penetration (lines per 100inhabitants). Based on a comparison of means test,I did not find a significant difference between theresponses and the targeted firms for GNI or popu-lation. I also performed a Kolmogorov–Smirnovnonparametric comparison of the samples andfound that they did not differ based on popula-tion. I did find a difference between the means

of responses and target firms for telephone pen-etration. In addition, the Kolmogorov–Smirnovtest, which detects clustering in the responsesample (Conover, 1971), found that the surveyresponses cluster in some areas of GNI eventhough the means did not differ. Overall, theresponse sample is similar to the target in basiccountry characteristics of population and income,but it differs in terms of telecommunications devel-opment (slightly higher) and income levels (clus-tered data).

Analysis

I test the hypotheses in the context of a struc-tural equation model (SEM), which represents thesimultaneous relationships between replication andadaptation, knowledge transfer, and performanceof the receiving unit. This allows me to test therelationships in a framework that accounts forthe direct and indirect relationships between thesevariables. The structural model also includes ameasurement model, which corrects for measure-ment error in latent constructs (e.g., knowledgetransfer) by using multiple observed indicators ofthem.

The number of observations is low by the field’srules of thumb for SEM. Small samples are knownto bias fit statistics downward, leading to over-rejection of true models (Hu and Bentler, 1999). Astudy, which examined the effect of sample size onparameter estimates, however, found no evidenceof bias; most of the problems from small sam-ples arose because of non-convergence of the esti-mation algorithms (Marsh et al., 1998). Since thefocus of this study is the relationship between keyconstructs, which are represented by the param-eter estimates, I use SEM to test the hypotheseswhile accounting for measurement error, which isa common problem with survey studies. I also con-duct considerable robustness analysis to check ifthe findings are stable across different specifica-tions that reduce estimation problems or increasediscriminatory power. I present alternative resultsbased on ordinary least squares (OLS) regression,which has better small sample estimation proper-ties than SEM. The small sample limits the dis-criminatory power of the study in any specifica-tion, however, so the results are most likely to bevalid for large effects but may under-report thesignificance of small effects.


874 C. Williams

Tests of performance, such as Hypothesis 5,are fraught with selection problems because firmsmake investment decisions based on anticipatedperformance benefits (Masten, 1993; Shaver,1998). Selection problems arise when a populationis not randomly selected from among the possiblepopulation parameters (Greene, 2003; Heckman,1979; Maddala, 1983). In this case, selection biascould arise because the firms choose their effortstoward replication and adaptation with some fore-sight. When higher performance from transfer isanticipated, the firms are likely to invest morein replication and adaptation. A structural modelcan include this effect by modeling the selectionmechanism directly and allowing the error terms tocorrelate between the selection criteria and the out-come (Greene, 2003; Maddala, 1983; Muthen andJoreskog, 1983). This is analogous to maximumlikelihood estimation of selection in simultaneousequations (Greene, 2003). Conveniently, Hypothe-ses 2 and 3, along with a number of controls forcountry attractiveness and firm size, directly modelfirms’ propensity for replication and adaptation.

Identification of the parameters is a serious issuein structural equation models (Bollen, 1989; Gold-berger, 1991; Greene, 2003), since not all specifiedmodels may be estimated in the population. Thestructural model presented in this paper is identi-fied by Bollen’s two-step rule (Bollen, 1989: 328):the measurement model is identified by the three-indicator rule and the structural model is identifiedby the rank and order conditions for non-recursivemodels.

The models were estimated using full-infor-mation maximum likelihood in AMOS 5.0, whichuses all available data to estimate the model. Thisapproach provides consistent and efficient estima-tors if the data are missing completely at randomand is consistent if the data are missing at random(Arbuckle, 1996; Arbuckle and Wothke, 1999).There are 40 missing values out of 2400 data pointsin this set. The missing values are clustered in theperformance measure (nine missing values) and thesubscriber data (seven missing for receiver, fourmissing for source).

I conducted all significance and hypothesis testsusing likelihood ratio tests, which are scale inde-pendent (Gonzalez and Griffin, 2001; Greene,2003). This is a significant issue for SEM, sinceall latent variables are scaled, but defaults in moststatistical packages rely on Wald tests, which may

vary with different scales assigned to a parame-ter. Generally, this approach reduced the level ofsignificance of the relationships compared to thep-values from Wald tests.

Variables

Appendix 1 presents descriptions and descriptivestatistics for all 42 observed variables in the study,including the survey items used to measure thelatent constructs of replication, adaptation, andknowledge transfer. Appendix 2 presents the cor-relation of these variables.

Table 1 presents the items representing the latentvariables of knowledge transfer, replication, andadaptation, as well as the results of the measure-ment model using the responses referring to thefirst year of the relationship (the reliability of theconstructs is even higher for the 2001 data). Eachconstruct is represented by three or four surveyitems, which span different aspects of the con-struct. Each of the items loads significantly onthe construct, and the reliability indicator (Cron-bach’s alpha) is well above the standard 0.7 cut-offfor all three constructs. In addition, the measure-ment model demonstrates very close fit with theobserved data and a non-significant χ 2 test. Thus,confirmatory factor analysis suggests that the mea-surement model demonstrates high reliability anda close fit with the observed data.

The additional variables are described below.

Discrete knowledge

Three measures represent the extent of links bet-ween knowledge in the receiving firm and out-side actors, institutions, and infrastructure—thatis, between the firm and the outside environment.The variable cell, which was coded from surveyresponses, indicates whether the firm operates onlyin the cellular telecommunications market. Cellu-lar firms have fewer links with the environmentbecause regulators do not impose universal serviceobligations and because the infrastructure does notrequire installation in every neighborhood. Thevariable new indicates whether the receiving firmwas founded at the same time as the transferrelationship, which was coded from surveys. His-torical routines will acquire connections to theenvironment as they face new contingencies overtime, which makes them less discrete. The vari-able template indicates whether the source firm



Table 1. Measurement of latent constructs

Latent variable(Cronbach’s α)

Survey items (item number, refers to initial year of relationship) Estimates S.E.

Knowledge transfer (α = 0 .89 )Our partner provided valuable information for our business (pb1) 1.07∗∗∗ 0.15We learned a lot from our partner (pb2) 1.40∗∗∗ 0.19Our partner provided examples of new ways to manage our business (pb3) 1.00

Replication (α = 0 .89 )We tried to manage our business exactly like our partner (pc1) 0.77∗∗∗ 0.13We tried to implement practices from our partner exactly as they existed (pc5) 1.11∗∗∗ 0.12We tried to copy practices from our partner down to smallest detail (pc6) 1.00We spent substantial time making sure practices we adopted from our partner worked 0.90∗∗∗ 0.12

just as they did there (pc7)

Adaptation (α = 0 .84 )We usually modified practices from our partner when we 1.00

implemented them in our business (pe1)We usually combined ideas from our partner with other ideas when we adopted them (pe4) 0.88∗∗∗ 0.13We spent substantial time modifying practices from our partner to 0.64∗∗∗ 0.15

make them work in our business (pe5)We carefully selected practices from our partner to adopt in our business (pe6) 0.96∗∗∗ 0.15

Model fitχ 2 (d.f.) 53.22 (41)IFI (delta 2) 0.97CFI 0.97

Significant at ∗ 0.10 level; ∗∗ 0.05 level; ∗∗∗ 0.01 level (by one-tailed likelihood ratio test).

had operations in its home country and was drawnfrom company reports. Firms without home oper-ations are more likely to transfer knowledge frommultiple source locations, introducing connectionswith multiple source environments. As a result,firms with a home template possess more discreteknowledge.

Understanding

Three measures represent the degree to whichthe transfer partners build understanding of theirknowledge through experience. The variable scou-ntries represents the number of countries wherethe source firm provided telecom services in 2001(from company reports). Source units build under-standing of connections through repeated trans-fer. The variable srelat represents the number ofsource–firm relationships within the same countryas the receiving firm (from company reports). Thisvariable represents source-unit understanding ofthe specific country setting built through multiplerelationships. The variable time indicates the num-ber of years the knowledge transfer relationshiphad existed in 2001 (from survey responses). This

variable represents understanding built throughexperience in this relationship.

Performance

The performance of the receiving firm is measuredas improvements in efficiency in the year after theknowledge transfer relationship was founded. Effi-ciency is measured as the number of subscribersper employee. This figure is calculated for the endof the first year of the relationship and the end ofthe second year of the relationship. The variableedelt12 is defined as the percentage change in thismeasure between the first year and the second yearof the relationship.

Controls

A number of controls for country attractivenessand firm characteristics are included in the modelto account for the relative propensity of firms toinvest in replication and adaptation. Year repre-sents the year the relationship was founded, whichwas coded from survey responses. Lnrecsubs rep-resents the natural logarithm of receiving firm


876 C. Williams

subscribers in 2001, which was coded from sur-vey responses and company reports. Lnsrcsubsrepresents the natural logarithm of source-firmsubscribers (in all countries, equity adjusted) in2001, which was coded from company reports.Lngni01 is the log of gross national income percapita in 2001, while lngdp i is the log of GDPper capita for the first year of the relationship.These variables were coded from the World BankDevelopment Indicators. Lnpop01 is the log of thenational population of the receiving firm’s countryin 2001, while lnpop i is the log of the populationin the first year of the relationship. The popula-tion variables were coded from the World Bank’s(2002) World Development Report. Telpen01 is thenumber of telephone lines per 100 inhabitants in

the target country in 2001, while telpen i is thetelephone penetration in the first year of the rela-tionship. The telephone penetration data were gath-ered from the International Telecom Union. Polconis a measure of political risk for the receiving coun-try in 2001, while polcon i is the same measure ofpolitical risk in the initial year (Henisz, 2002). Cur-rown is the source firm’s ownership stake in thereceiving firm in 2001, while firstown is the sourcefirm’s ownership at the beginning of the relation-ship. These were coded from survey responses.

RESULTS AND DISCUSSION

Tables 2 and 3 present the results of the analysis.The models build from a regression with latent

Table 2. Levels of replication and adaptation (2001)

Hypothesis(predicted sign)

Model 1Contingencies

Model 2Contingencies

plus time

Model 3Contingencies plus

time, source, and size

currown → R 0.32∗∗ 0.32∗∗ 0.34∗∗∗

lngni01 → R −0.42∗ −0.42∗ −0.28lnpop01 → R 0.05 0.05 −0.09telpen01 → R −0.17 −0.18 −0.35polcon → R 0.22 0.23 0.22cell → R H2 (+) 0.19 0.19 0.17new → R H2 (+) −0.14 −0.14 −0.07template → R H2 (+) 0.18 0.18 −0.10time → R −0.02 −0.09scountries → R −0.10srelat → R −0.14lnsrcsubs → R 0.48lnrecsubs → R 0.06

currown → A 0.37∗∗∗ 0.39∗∗∗ 0.35∗∗∗

lngni01 → A −0.10 −0.16 −0.19lnpop01 → A 0.12 0.16 0.01telpen01 → A 0.05 0.20 0.19polcon → A 0.31∗ 0.25 0.20cell → A −0.47∗∗∗ −0.40∗∗∗ −0.59∗∗∗

new → A 0.37∗∗∗ 0.23 0.44∗∗∗

template → A 0.10 0.10 0.01time → A H3 (+) 0.31∗∗ 0.27∗∗

scountries → A H3 (+) 0.02srelat → A H3 (+) −0.08lnsrcsubs → A −0.06lnrecsubs → A 0.41∗∗∗

χ 2 (d.f.) 155.69 (108) 149.94 (106) 132.74 (98)IFI (delta 2) 0.93 0.94 0.95CFI 0.92 0.92 0.94

n = 60; standardized coefficients; significance by one-tailed likelihood ratio tests; 2001 data; A represents latent constructfor adaptation; R represents latent construct for replication; relationships between indicators and latent variables andbetween independent variables (IVs) are suppressed for brevity; models are nested with all IVs included in each model.Significant at ∗ 0.10 level; ∗∗ 0.05 level; ∗∗∗ 0.01 level.



dependent variables (replication and adaptation)to a full structural model in which adaptationand replication lead to knowledge transfer, whichin turn leads to performance improvements. Themodels are nested by including all variables in eachestimated model, but gradually adding the rela-tionships between the variables. Table 2 presentsModels 1–3 using 2001 data to test Hypotheses 2and 3. Table 3 presents Models 4–7 using datafrom the beginning of each knowledge transferrelationship to test Hypotheses 4 and 5. Model 1examines the relationship between adaptation andreplication and eight exogenous variables. Model2 adds the exogenous variable time to Model1. Model 3 adds several source and receiving-firm variables to Model 2. Model 4 replicates

Model 2 with data from the first year of theknowledge transfer relationship (year substitutesfor time, but the two variables have a perfect,inverse correlation). Model 5 adds the effect ofadaptation and replication on knowledge transfer.Model 6 adds the effect of knowledge transfer onperformance improvements. Model 7 incorporatespotential selection bias arising when firms investmore in transfer mechanisms based on expectationsof performance improvements. Finally, Table 4recreates the components of Models 3 and 6using ordinary least squares. Overall, the resultsare consistent with a model of replication andadaptation as separate constructs that both con-tribute to knowledge transfer, which leads to per-formance improvements. The results are mixed

Table 3. Structural model of knowledge transfer and performance (first year)

Hypothesis(predicted sign)

Model 4Adaptation and

replicationcontingencies

Model 5Adaptation,replication

and transfer

Model 6Transfer andperformance

Model 7Full modelw/selection

firstown → R 0.09 0.08 0.08 0.09lngdp−i → R 0.01 0.00 0.00 0.00lnpop−i → R −0.12 −0.12 −0.12 −0.12telpen−i → R −0.28 −0.26 −0.26 −0.27polcon−i → R −0.05 −0.05 −0.05 −0.05cell → R −0.04 −0.03 −0.03 −0.03new → R 0.14 0.14 0.14 0.14template → R 0.16 0.16 0.16 0.16firstyear → R −0.21 −0.21 −0.21 −0.21

firstown → A 0.17 0.16 0.16 0.14lngdp−i → A −0.27 −0.28 −0.28 −0.29lnpop−i → A 0.27 0.27 0.27 0.30∗

telpen−i → A 0.45∗ 0.46∗ 0.46∗ 0.46∗

polcon−i → A 0.04 0.05 0.05 0.08cell → A −0.44∗∗ −0.42∗∗ −0.42∗∗ −0.36∗

new → A 0.41∗∗ 0.40∗∗ 0.40∗∗ 0.43∗∗

template → A −0.03 −0.02 −0.02 −0.02firstyear → A −0.00 −0.00 −0.00 0.04

R → KT H4a (+) 0.40∗∗∗ 0.40∗∗∗ 0.40∗∗∗

A → KT H4b (+) 0.40∗∗∗ 0.40∗∗∗ 0.41∗∗∗

KT → edelt12 H5 (+) 0.25∗ 0.34∗

errorR —errorp 0.02errorA—errorp −0.23

χ 2 (d.f.) 225.82 (145) 204.23 (143) 201.26 (142) 199.75 (140)IFI (delta 2) 0.88 0.91 0.91 0.91CFI 0.86 0.89 0.90 0.90

n = 60; standardized coefficients; significance by one-tailed likelihood ratio test; data from initial year of relationship; A representslatent construct for adaptation; R represents latent construct for replication; KT represents latent construct for knowledge transfer;errorR—errorp and errorA—errorp represent the correlation of the error terms for replication and adaptation (respectively) with theerror term for edelt12; relationships between indicators and latent variables and between independent variables suppressed for brevity;models are nested with all IVs included for each model.Significant at ∗ 0.10 level; ∗∗ 0.05 level; ∗∗∗ 0.01 level.


878 C. Williams

Table 4. OLS regression of components of the structural models (Models 6 and 3 from Tables 2 and 3)

Initialyear:

(1)DV:

replicate

(2)DV: adapt

(3)DV: Ktx

(4)DV:

edelt12

2001 : (5)DV:

replicate

(6)DV: adapt

(7)DV: Ktx

firstown −0.001 −0.013 curr−own 0.098∗∗ 0.083∗∗∗

(0.057) (0.036) (0.052) (0.032)lngdp−i 1.243 −0.492 lngni01 −0.784 −0.290

(2.108) (1.337) (2.198) (1.362)lnpop−i −1.339 0.543 lnpop01 −0.180 0.339

(1.612) (0.731) (1.058) (0.656)telpen−i −0.174∗ 0.080 telpen01 −0.141 0.047

(0.113) (0.071) (0.115) (0.071)polcon−i −4.296 0.198 polcon 7.657 3.553

(6.883) (4.357) (6.944) (4.303)cell 1.040 −2.404 cell 4.470∗ −5.810∗∗∗

(3.244) (2.042) (3.281) (2.033)new 1.610 2.769∗ new −3.415 3.871∗∗

(2.999) (1.890) (3.203) (1.985)template 1.177 −2.841 template −0.593 −1.259

(3.908) (2.477) (4.995) (3.095)firstyear −0.465 −0.094 time −0.103 0.465∗∗

(0.389) (0.245) (0.371) (0.230)scountries −0.036 0.006

(0.135) (0.083)srelat −0.838 −0.369

(0.770) (0.477)lnsrcsubs 2.456∗∗ −0.389

(1.404) (0.870)lnrecsubs 0.137 1.065∗∗∗

(0.651) (0.404)replicate−i 0.165∗∗∗ replicate 0.248∗∗∗

(0.047) (0.49)adapt−i 0.221∗∗∗ adapt 0.209∗∗∗

(0.070) (0.062)ktx−i 0.078∗

(0.054)Intercept 945.673 209.668 3.336∗∗ −0.158 Intercept −19.528 9.237 1.361

(772.880) (486.403) (1.489) (0.621) (25.145) (15.581) (1.457)Observations 56 55 58 51 Observations 47 47 60R2 0.16 0.13 0.35 0.04 R2 0.37 0.50 0.44

Data from 2001 survey; latent variables represented by sum of observed indicators; significance by one-tailed Wald tests.Significant at ∗ 10%; ∗∗ 5%; ∗∗∗ 1%.

for the contingencies affecting replication andadaptation.

The results of confirmatory factor analysis areconsistent with Hypothesis 1. When the indicatorsof adaptation and replication are constrained to asingle factor in the measurement model, the modelfit declines sharply over a model with two factorsthat correlate freely. The difference between thetwo models is highly significant, with a χ 2 (d.f. =1) of 14.2. As described above, the baseline mea-surement model cannot be rejected by the data,while the model with a single factor is significantly

at odds with the data (χ 2(42) = 67.4, p = 0.008).In addition, when the indicators are loaded on asingle factor, all the item loadings are positive,while one would expect one set of loadings tobe negative in the one-factor case. Finally, in thetwo-factor model adaptation and replication havea positive, albeit non-significant, correlation. Thus,the data are consistent with the proposition thatadaptation and replication are separate constructs.

The proposition that replication increases withdiscrete knowledge (Hypothesis 2) is not sup-ported, since the variables representing discrete



knowledge (new, template, and cell ) do not have asignificant relationship with replication. The vari-able new, which indicates whether the receivingunit was founded at the same time as the transferrelationship, does not significantly affect replica-tion. In addition, its effect varies between positive(first year, Table 3) and negative (2001, Table 2),so while it is possible that the effect is too small tobe distinguished from a non-effect with this sam-ple, it does not appear to have a consistent impacton replication. In contrast to its lack of effecton replication, the variable new is also associatedwith significantly higher levels of adaptation inTables 2 and 3.

Similarly, the existence of a clear home template(template) does not significantly increase replica-tion among these firms. The relationship betweentemplate and replication is not significant in anyof the models, though it is generally positive andcould be small but significant with a larger sam-ple. Finally, the variable cell does not lead tosignificantly higher replication in any of the mod-els, except in one instance in the OLS regression(Table 4, column 5) where it is weakly signifi-cant. In addition, the effect changes from negative(first year, Table 3) to positive (2001, Table 2).The impact of cell on adaptation, however, is neg-ative and significant in all models. While thesefirms do not significantly increase replication ofmore discrete knowledge in cellular markets, theydo reduce adaptation in this setting.

The prediction that replication would rise asknowledge became more discrete relied on a costmechanism, in which firms would turn to repli-cation as the cost of integrating knowledge fell.Instead, these results suggest that firms choose alevel of replication based on the reward for thetransfer of ambiguous knowledge, and then choosea level of adaptation to achieve integration con-tingent upon the interdependence with the con-text. The lack of a relationship between indicatorsof discrete knowledge and replication might alsoarise if firms make knowledge more discrete byaccounting for contingencies within the knowledgeitself.1 Alternatively, the relationship between dis-crete knowledge and replication might be masked

1 For instance, if a manufacturing process depends on tempera-ture or humidity, the knowledge may include operating routinesthat are contingent upon these context variables. I am grateful toa referee for pointing out this potentially endogenous aspect ofdiscrete knowledge. However, if firms are generally successful inincorporating contingencies into the knowledge to be transferred,

in this study if replication is largely dependenton the policies of the source unit rather than thereceiving unit. Secondary analysis using receiving-firm reports of source-firm investments in replica-tion, however, found that none of these measuresof discrete knowledge led to significant variationin source-firm replication.

The results are mixed with regard to the impactof understanding on adaptation (Hypothesis 3). InModels 2 and 3, adaptation increases significantlyas the length of the relationship rises (time). Thus,understanding gained through experience in therelationship does increase the use of adaptation bythe receiving partner. Relationship-specific experi-ence appears to be necessary for building under-standing, since the source firm’s prior relationships(scountries) and country experience (srelat) do nothave a significant impact on adaptation in Model3. The lack of a relationship between source-firm experience and adaptation could arise becausesource firms that operate in many countries mustimpose uniform practices to reduce coordinationcosts. One might also conclude that these variablesaffect source adaptation without changing receiveradaptation, but secondary analysis using receiving-firm reports of source adaptation found that neitherof these measures led to significant variation insource-firm adaptation.

The results are consistent with Hypotheses4a and 4b. Replication and adaptation eachhave a positive and highly significant impacton knowledge transfer (see Table 3, Models5–7). Firms that engage in higher levels ofreplication and adaptation also report greatertransfer of knowledge from the source partner.These relationships are robust to all specificationsof the model and are significant at the .01 level.The size of the effect is about the same for eachmechanism—a one standard deviation increase inadaptation or replication leads to a 0.4 standarddeviation increase in knowledge transfer.

Finally, knowledge transfer has a positive,significant impact on efficiency improvements(Hypothesis 5) in Models 6 and 7. This result

then we might see fairly uniform replication across different rela-tionships with context. In this case, however, I would not expectto see the reduction of adaptation in the face of more discreteknowledge that is observed in all specifications. In addition, Iwould expect firms to develop this contingent knowledge (whichcan be more easily replicated) through the experience of repeatedtransfer, but there is not a significant relationship between pasttransfer (scountries) and replication.


880 C. Williams

is somewhat larger when selection bias isaccounted for by allowing errors to correlatebetween investments in mechanisms and impacton performance in Model 7. Since firms are likelyto invest more when they anticipate performancepay-offs, this suggests that a selection bias mayexist in the data, though the correlation betweenerrors is not significant.2

Of the controls included in the study, the owner-ship variable has the strongest effect on replicationand adaptation, though only in the 2001 data. Own-ership of the source firm has a positive and highlysignificant impact on replication and adaptation in2001. Firms appear to invest more in both replica-tion and adaptation when the source owns more ofthe receiving firm. This is consistent with gover-nance theories that predict knowledge transfer haz-ards are reduced by governance under a single firm(Oxley, 1999). In the data from the initial year ofthe relationship, however, source ownership doesnot have a significant effect on adaptation or repli-cation. This might arise if source firms believedthat investments in knowledge transfer will allowthem to acquire higher levels of ownership in thefuture despite the risk of opportunism.

Country attractiveness has surprisingly littleeffect on adaptation. Telephone penetration isassociated with more adaptation in the initialyear of the relationship (telpen in Table 3,Models 4–7), though not in the 2001 data(Table 2, Models 1–3). Firms in countrieswith higher telephone penetration possess moretechnical understanding, which might increase theeffectiveness of adaptation. Higher penetrationis also associated with earlier liberalization, andcompetitive pressures early in the relationshipmay make adaptation essential for success.Larger receiving firms adapt significantly more(lnrecsubs), possibly because they have more linksto the environment than smaller firms. Country size(lnpop) and political risk (polcon) have a positive,non-significant effect on adaptation in both timeperiods, while GNI per capita (lngni01 in Table 2)and GDP per capita (lngdp i in Table 3) havenegative but non-significant effects.

2 In an alternative specification, I modeled selection at the levelof knowledge transfer, which led to results consistent with thosein Model 7, though the selection effect was not significant inthis specification. In this specification, the relationship betweenknowledge transfer and performance becomes non-significant.

On the other hand, replication has a gener-ally negative relationship with measures of coun-try attractiveness. GNI per capita weakly reducesreplication in the 2001 data. When telpen and pol-con, which are correlated with GNI per capita (0.80and 0.64, respectively), are not included in theregression then the negative effect of lngni01 isstrongly significant. This is consistent with a pullperspective on knowledge—whereby knowledgeis pulled into less developed countries becauselocal firms have more to learn. In the second timeperiod (2001, Table 2), source size and politicalrisk have a positive, non-significant effect on repli-cation.3 Finally, country size (lnpop) and politicalrisk (polcon) have a negative and non-significanteffect on replication in the first time period buta generally positive and non-significant effect in2001.

While model fit is not the focus of the anal-ysis in this paper, a number of fit indicators areincluded in Tables 2 and 3. Model fit is accept-able using 2001 data in Table 2. Models 1–3 allhave fit indices (incremental fit index (IFI or delta2) and comparative fit index (CFI)) between 0.90and 0.95. Using data from the first year of the rela-tionship the fit is less good, but above 0.90 for thefull model (Models 5–7 in Table 3).

Robustness

I analyzed the data under alternate approaches toestimation in order to gauge the robustness ofthe SEM results given the small sample and toexplore alternative measures of performance. Theresults of these robustness checks are broadly con-sistent with the results of the hypothesized model.Time in the relationship increases adaptation inall specifications. Measures of discrete knowledgeincrease adaptation in most specifications. Replica-tion and adaptation contribute to knowledge trans-fer in all specifications, while knowledge transferleads to performance improvements for measuresthat emphasize short-term improvement, that is, inthe year following transfer. For measures of per-formance improvement over the length of the rela-tionship, I find knowledge transfer has no effect.So, while the results are broadly consistent with thehypotheses in the paper, some interesting nuancesemerge from these analyses.

3 Except in the OLS analysis, where lnsrcsubs has a significant,positive effect on replication.



In particular, OLS analysis is less sensitive tothe small-sample estimation issues that might arisein the SEM method used as the primary analy-sis because OLS approaches asymptotic behaviormore quickly than maximum likelihood estima-tion (used in SEM). Table 4 shows the results ofOLS analysis of each component (equation) withinthe full SEM model for Model 3 and Model 6(Tables 2 and 3). In these models, the latent vari-ables are treated as composite indexes—averagesof indicator variables from the survey—so themodels do not account for measurement error inestimating the variables of replication, adaptation,and knowledge transfer. The results of these indi-vidual regressions closely match those reportedin the SEM analysis (Tables 2 and 3). The maindifference is in the OLS analysis for the initialyear, where the variables telpen i, cell, and newhave weak or no significant effect on adaptation,which could arise because measurement error isa larger concern in data requiring significant his-torical recall on the part of survey respondents.4

Together, the OLS regressions suggest that theSEM results presented in the tables do not arisebecause of errors in estimation from the maximumlikelihood estimation technique.

In the final robustness analysis, I explored alter-native measures of performance. The relation-ship between knowledge transfer and performanceremains significant for alternative short-term per-formance measures, but not for long-term mea-sures. Growth in subscriber base is a very impor-tant metric for telecom firms, especially in emerg-ing markets where they face a pent-up demandfrom consumers. When Model 7 uses growth insubscribers from year 1 to year 2 in place of theefficiency performance measure, the relationshipbetween knowledge transfer and performance ispositive and significant at the 0.05 level. When Isubstitute measures of performance improvementover the course of the relationship (first year until2001), however, there is not a significant rela-tionship between knowledge transfer and perfor-mance improvements. These long-term measures

4 Table 4 shows the following minor discrepancies with theSEM analysis. In two cases, variables (telpen i and cell ) in theinitial year lose a significant impact on adaptation, presumablybecause of higher measurement error with longer recall. In threecases, variables (firstown, cell, template) that are not significantlydifferent from zero change sign in the OLS results. Finally,three non-significant relationships between replication and othervariables (telpen i, cell, lnsrcsubs) in the SEM analysis gainweak significance in the OLS.

include efficiency and growth measures from pub-lic reports as well as self-reported improvements infive categories: service quality, efficiency, growth,innovation, and profitability. The lack of signif-icance for long-term performance improvementssuggests that the value of knowledge depreciatesquickly (Argote, 1999; Darr et al., 1995). Knowl-edge has an impact one year after transfer, butthe effect does not appear to lead to compoundingimprovements over the following years.

Summary

Overall, the results are consistent with the theo-retical arguments laid out in the paper. Adapta-tion and replication are distinct transfer mecha-nisms that firms use simultaneously when transfer-ring knowledge. Both contribute to transfer, whichleads to improved efficiency. Adaptation rises withtime, suggesting that time within the relationship isan important source of understanding. Replicationdoes not rise when knowledge is more discrete,though adaptation does fall. Finally, the effect ofgovernance on knowledge transfer changes overthe course of the relationship. The source firm’sownership of the receiving firm does not affecttransfer in the first year of the relationship, thoughlater both replication and adaptation rise whensource ownership is higher.

CONCLUSION

This paper contributes to our theory of orga-nizational knowledge by bringing together twostreams of research that have emphasized differ-ent aspects of knowledge: context dependence andcausal ambiguity. The implicit assumption of thesestreams has been that replication and adaptationare mutually exclusive approaches to transfer. Inthis study, I find that the actions of firms trans-ferring knowledge are consistent with the theorythat knowledge is simultaneously context depen-dent and causally ambiguous. Firms replicate morewhen organizational knowledge is ambiguous andmust be copied exactly, and firms adapt morewhen organizational knowledge depends on con-text and must be modified for the new setting. Idefine replication and adaptation as effort towardsa goal, which opens the possibility that firms repli-cate and adapt simultaneously, and I find that theyfrequently do. In sum, I find that replication and


882 C. Williams

adaptation vary separately, are used jointly, andcontribute significantly to knowledge transfer.

This study is just a first step in developing theseinsights about knowledge. Any conclusions mustbe tempered by our understanding of the study’slimits. The study’s small sample size raises thequestion of whether the findings would be repli-cated in another study. Since SEM makes heavydemands of small samples, it is possible that therelationships between variables in this study mightnot represent the true asymptotic values of theparameters. Yet, the robustness analysis with OLS,which requires fewer observations, finds a simi-lar pattern of relationships, mitigating this concern.Similarly, response bias could skew the results ifthis is an unrepresentative sample. Comparing oursample to the population of relationships, however,uncovers only small differences between them.Finally, the power of a test depends on the size ofthe effect, so this study has less power to determinethe significance of small effects than large ones. Itis likely that another study with a larger samplewould find some of the non-significant parametersto be significant, though likely smaller than therelationships discussed in this paper.

In addition, the empirical setting raises twoquestions about how the results would generalizeto other firms. As service firms, the companies inthe study interact with the environment throughouttheir operations. In contrast, manufacturing firmscan buffer some of their operations within facil-ities where there is less interaction with outsideactors (Thompson, 1967). With careful buffering,the need for adaptation may be less than in ser-vice firms, and the effectiveness of replication maybe increased. Service firms represent an interestingsetting to study knowledge that is embodied in theroutines of the firm, but it remains an empiricalquestion if these results will generalize to settingswhere firms can buffer more of their core oper-ations. Another limitation is that the study usesreports from the receiving partner to represent thepolicies of the transfer relationship. The relation-ship includes a source partner as well, and it ispossible that the source’s actions differ signifi-cantly from those of the receiver. Responses fromreceiving units suggest that source firm replicationis correlated almost 0.9 with the receiving unit andsource firm adaptation is correlated nearly 0.6 withreceiving unit adaptation, but this study still lacksthe source unit’s perspective and the impact of itsactions on transfer and performance.

Finally, some of the design choices in the studylimit the extent to which we can distinguish theunderlying mechanisms and compare alternativeapproaches to knowledge transfer. While I predictand find that firms are engaged in both replicationand adaptation at the level of the relationship, thisstudy cannot rule out the possibility that at somesufficiently fine-grained level of individual prac-tice firms only employ one or the other transfermechanism. The logic developed here suggests thateven at a fine-grained level organizational knowl-edge is less like discrete atoms than like gears in amachine, which must be adapted to work togetherin a new setting. Future research will be needed,however, to explore how firms balance, combine,and trade off between replication and adaptation atthe level of an individual practice. Similarly, themeasurement of replication and adaptation as sus-tained practices over a year of the transfer relation-ship means that this study cannot distinguish fine-grained differences in the timing of the two activ-ities. Szulanski’s (2003) stage model of transferincludes four stages of transfer—initiation, imple-mentation, ramp-up, and integration—and Szulan-ski and Winter (2002) recommend that adapta-tion should only occur after implementation. I findthat, in the first year of the relationship, greaterinvestment in adaptation leads to more knowledgetransfer. While the theory developed here suggeststhat interdependent knowledge could not be imple-mented without some adaptation, this study cannotrule out the possibility that the adaptation reportedin the first year occurs entirely after the actualimplementation of new practices.

Despite these limitations, a number of interest-ing results emerge from the study that can help usdevelop our theory of organizational knowledgeand suggest new paths for research. Adaptationemerges from the study as the primary tool bywhich firms calibrate transfer to a particular sit-uation. When firms transfer more discrete knowl-edge to units in cellular markets, they adapt lessbecause there are fewer connections. Firms adaptmore when they possess understanding of the con-nections between knowledge and its environment.The finding that firms are quite active in managingthe levels of adaptation to fit these contingenciessuggests that adaptation deserves a central role inour theories of organizational knowledge.

That we have much more to learn about adapta-tion is suggested by two surprising results: whenreceiving units are newly founded they increase



adaptation, but when source firms have prior expe-rience with transfer they do not shift adaptation.Understanding is not something typically associ-ated with organizations, but I propose that orga-nizations possess understanding when they canpredict how a given practice will interact withother elements of the organization and environ-ment. One respondent from a source firm pointedto the management of interacting practices as anessential aspect of transfer: ‘Some organizationsare very good at [managing] the individual slices,but they do not have a good idea of how thoseslices fit together . . . [my company] used to movepeople around and we developed a good feel forhow the pieces fit together, which is not typicalelsewhere.’ Understanding interconnected knowl-edge might well contribute to recombination andinnovation within firms (Teece, Pisano, and Shuen,1997; Winter, 2003). A valuable future projectwould be to study how firms build understandingand where it must reside to be useful for knowl-edge transfer or recombination.

I find that knowledge transfer has a measurableimpact on firm performance the year after knowl-edge is transferred. This effect is weak, but robustacross several measures of performance, and itis larger when the model accounts for selectionbias. In addition, the results are likely to under-state the impact of transfer on performance sincethey do not include simultaneous transfer and per-formance improvements in the same year. How-ever, the knowledge acquired by the receivingunit appears to depreciate rapidly, since there isno long-term performance impact from knowledgetransfer in the first year of the relationship. Thefinding that knowledge in organizations depreci-ates at a fairly rapid pace also dovetails with otherrecent research (Argote, 1999; Darr et al., 1995).Our evolutionary theories of the firm need to beextended to explain the friction that keeps success-ful early transfer from leading to later transfer, andempirical studies need to examine the mechanismsthat lead to rapid depreciation of knowledge.

From an evolutionary perspective, the picture ofmultinational firms that emerges from the study isof large firms evolving and changing as they moveto new contexts rather than replicating uniformpractices around the world. Thus, one mechanismof change for firms might be to expand to newenvironments where existing routines will requireadaptation. From an evolutionary perspective, thestudy suggests an important question: when will

the process of adaptation generate knowledge thatfirms carry back to their core operations and whatfirm policies will facilitate this knowledge creationand transfer?

In addition, the appropriate governance ofknowledge transfer appears to vary over the rela-tionship. Consistent with the transaction–cost per-spective on the mitigation of hazards in knowl-edge transfer (Oxley, 1999), source ownership hasa sizeable and significant impact on replicationand adaptation later in the relationship. In the firstyear, however, ownership does not have a signif-icant effect on investments in knowledge transfer.These results suggest that governance models forknowledge transfer need to incorporate the stageof relationship as a contingent factor shaping thechoice of ownership.

While replication is clearly an important mech-anism for knowledge transfer, this study suggestsmore about which factors do not lead to higherreplication than those which do. Variables repre-senting prior experience of the source firm andthe level of discreteness of the knowledge havenegligible impacts on replication. Some other fac-tors, such as country development and source size,have large but non-significant effects, which mightbe significant in larger samples. Still, most of thecontingencies for replication explored in this studyappear to have less influence on replication thanon adaptation. Perhaps replication is calibrated tothe level of ambiguity in the targeted knowledge.In that case, future studies at the level of individ-ual practices might explain the contingent natureof replication by measuring the relative causalambiguity of different practices.5 Some of the con-tingencies that drive replication may also be thefactors that lead firms to enter knowledge transferrelationships. In this case, the decision to replicatemay be tightly bound with the decision to trans-fer knowledge, after which adaptation is calibratedto the context. This suggests an alternative, mul-tilevel model, in which adaptation increases theeffectiveness of replication when knowledge is lessdiscrete or understanding is greater. Such a modelis beyond the possibilities of these data, but might

5 One would expect, however, that practices would become lessambiguous as firms gained experience transferring them to newlocations, but prior transfer has a negligible effect on replicationin the study. Still, this could arise if causal ambiguity is sodeep that it is not reduced through repeated transfer, which isthe implicit assumption behind Intel’s ‘Copy Exactly!’ policy(McDonald, 1998).


884 C. Williams

be fruitful for future exploration. In the final analy-sis, there remains unexplained variance in replica-tion, which awaits explanation as we develop ourunderstanding of organizational knowledge.

When firms set out to transfer knowledge, thetheory and empirical analysis in this paper sug-gest they must assess the nature of the knowl-edge to be transferred. Firms need to differenti-ate between ambiguous knowledge, which mustbe copied exactly, and knowledge intertwinedwith the environment, which requires modification.While exact copying of knowledge is difficult innew settings, effort invested in copying will signif-icantly increase the transfer of valuable knowledgeeven if perfect copying is not achieved. In addition,the interdependent nature of knowledge means thatfirms will need to identify the interfaces betweenambiguous knowledge and the context to targetthem for adaptation. Understanding the connec-tions between knowledge, the organization, and itsenvironment takes substantial experience in a spe-cific relationship, but it can improve knowledgetransfer and may create capabilities that are usefulin other parts of the firm.

ACKNOWLEDGEMENTS

This research was supported by grants from theUniversity of Michigan’s Interdepartmental Con-sortium on Organization Studies and Center forInternational Business Education.

REFERENCES

Ahuja G. 2000. Collaboration networks, structural holes,and innovation: a longitudinal study. AdministrativeSciences Quarterly 45(3): 425–455.

Allen TJ. 1977. Managing the Flow of Technology: Tech-nology Transfer and the Dissemination of Technolog-ical Information within the R&D Organization . MITPress: Cambridge, MA.

Arbuckle JL. 1996. Full information estimation inthe presence of incomplete data. In AdvancedStructural Equation Modeling: Issues and Techniques ,Marcoulides GA, Schumacker RE (eds). Erlbaum:Mahwah, NJ; 243–277.

Arbuckle JL, Wothke W. 1999. Amos 4.0 Users Guide.SmallWaters: Chicago, IL.

Argote L. 1999. Organizational Learning: Creating,Retaining, and Transferring Knowledge. KluwerAcademic: Boston, MA.

Argote L, Ingram P. 2000. Knowledge transfer: a basisfor competitive advantage in firms. Organizational

Behavior and Human Decision Processes 82(1):150–169.

Baum JAC, Ingram P. 1998. Survival-enhancing learningin the Manhattan hotel industry, 1898–1980.Management Science 44(7): 996–1016.

Bollen KA. 1989. Structural Equations with LatentVariables . Wiley: New York.

Conover WJ. 1971. Practical Nonparametric Statistics .Wiley: New York.

Cyert RM, March JG. 1992. A Behavioral Theory of theFirm (2nd edn). Blackwell Business: Cambridge, MA.

Darr ED, Argote L, Epple D. 1995. The acquisition,transfer, and depreciation of knowledge in serviceorganizations: productivity in franchises. ManagementScience 41(11): 1750–1762.

Dillman DA. 2000. Mail and Internet Surveys: TheTailored Design Method (2nd edn). Wiley: New York.

Goldberger AS. 1991. A Course in Econometrics .Harvard University Press: Cambridge, MA.

Gonzalez R, Griffin D. 2001. Testing parameters instructural equation modeling: every ‘one’ matters.Psychological Methods 6(3): 258–269.

Grant RM. 1996. Prospering in dynamically-competitiveenvironments: organizational capability as knowledgeintegration. Organization Science 7(4): 375–387.

Greene WH. 2003. Econometric Analysis (5th edn).Prentice-Hall: Upper Saddle River, NJ.

Gupta AK, Govindarajan V. 2000. Knowledge flowswithin multinational corporations. Strategic Manage-ment Journal 21(4): 473–496.

Heckman JJ. 1979. Sample selection bias as aspecification error. Econometrica 47(1): 153–161.

Henderson R, Mitchell W. 1997. The interactions oforganizational and competitive influences on strategyand performance. Strategic Management Journal ,Summer Special Issue 18: 5–14.

Henisz WJ. 2002. The institutional environment forinfrastructure investment. Industrial and CorporateChange 11(2): 355–389.

Hu L-t, Bentler PM. 1999. Cutoff criteria for fit indexesin covariance structure analysis: conventional criteriaversus new alternatives. Structural Equation Modeling6(1): 1–55.

Kogut B, Zander U. 1992. Knowledge of the firm,combinative capabilities, and the replication oftechnology. Organization Science 3(3): 383–397.

Kostova T, Roth K. 2002. Adoption of an organizationalpractice by subsidiaries of multinational corporations:institutional and relational effects. Academy ofManagement Journal 45(1): 215–233.

Leonard-Barton D. 1988. Implementation as mutualadaptation of technology and organization. ResearchPolicy 17: 251–267.

Lippman SA, Rumelt RP. 1982. Uncertain imitability:an analysis of interfirm differences in efficiencyunder competition. Bell Journal of Economics 13(2):418–438.

Maddala GS. 1983. Limited-Dependent and QualitativeVariables in Econometrics . Cambridge UniversityPress: New York.

Marsh HW, Hau K-T, Balla JR, Grayson D. 1998. Ismore ever too much? The number of indicators per



factor in confirmatory factor analysis. MultivariateBehavioral Research 33(2): 181–220.

Masten SE. 1993. Transaction costs, mistakes, andperformance: assessing the importance of governance.Managerial and Decision Economics 14(2): 119–129.

McDonald CJ. 1998. The evolution of Intel’s CopyExactly! technology transfer method. Intel TechnologyJournal 4th quarter: 1–6.

Muthen B, Joreskog KG. 1983. Selectivity problems inquasi-experimental studies. Evaluation Review 7(2):139–174.

Nelson RR, Winter SG. 1982. An Evolutionary Theory ofEconomic Change. Belknap Press: Cambridge, MA.

Nonaka I. 1994. A dynamic theory of organizationalknowledge creation. Organization Science 5(1):14–37.

Oxley JE. 1999. Governance of International StrategicAlliances: Technology and Transaction Costs . Har-wood: Amsterdam.

Prahalad CK, Doz YL. 1987. The Multinational Mission:Balancing Local Demands and Global Vision . FreePress: New York.

Shaver JM. 1998. Accounting for endogeneity whenassessing strategy performance: does entry modechoice affect FDI survival? Management Science44(4): 571–585.

Spender J-C. 1996. Making knowledge the basis of adynamic theory of the firm. Strategic ManagementJournal , Winter Special Issue 17: 45–62.

Szulanski G. 1996. Exploring internal stickiness: imped-iments to the transfer of best practice within the firm.Strategic Management Journal , Winter Special Issue17: 27–43.

Szulanski G. 2003. Sticky Knowledge. Sage: ThousandOaks, CA.

Szulanski G, Winter S. 2002. Getting it right the secondtime. Harvard Business Review 80(1): 62–69.

Teece DJ, Pisano G, Shuen A. 1997. Dynamic capabili-ties and strategic management. Strategic ManagementJournal 18(7): 509–533.

Thompson JD. 1967. Organizations in Action . McGraw-Hill: New York.

Tushman ML, Anderson P. 1986. Technological discon-tinuities and organizational environments. Administra-tive Science Quarterly 31: 439–465.

Winter SG. 1987. Knowledge and competence asstrategic assets. In The Competitive Challenge:Strategies for Industrial Innovation and Renewal ,Teece DJ (ed). Ballinger: Cambridge, MA; 159–184.

Winter SG. 1995. Four R’s of profitability: rents,resources, routines, and replication. In Resource-Basedand Evolutionary Theories of the Firm: Towards aSynthesis , Montgomery CA (ed). Kluwer Academic:Boston, MA; 147–175.

Winter SG. 2003. Understanding dynamic capabilities.Strategic Management Journal 24(10): 991–995.

Winter SG, Szulanski G. 1998. Replication as strategy.Working paper, The Wharton School.

Winter SG, Szulanski G. 2002. Replication of organi-zational routines: conceptualizing the exploitation ofknowledge assets. In The Strategic Management ofIntellectual Capital and Organizational Knowledge:A Collection of Readings , Bontis N, Choo CW (ed).Oxford University Press: New York; 207–221.

World Bank. 2002. World Development Report . OxfordUniversity Press: New York.

APPENDIX 1: DESCRIPTIVE STATISTICS

Variable Description Range Mean S.D.

(1) b1 Knowledge transfer survey item 2001 (Table 1) 1–5 3.47 1.27(2) b2 Knowledge transfer survey item 2001 (Table 1) 1–5 3.32 1.38(3) b3 Knowledge transfer survey item 2001 (Table 1) 1–5 3.38 1.14(4) c1 Replication survey item 2001 (Table 1) 1–5 2.65 1.12(5) c5 Replication survey item 2001 (Table 1) 1–5 2.38 1.09(6) c6 Replication survey item 2001 (Table 1) 1–5 1.83 0.89(7) c7 Replication survey item 2001 (Table 1) 1–5 2.20 1.02(8) e1 Adaptation survey item 2001 (Table 1) 1–5 3.50 1.14(9) e4 Adaptation survey item 2001 (Table 1) 1–5 3.90 1.10(10) e5 Adaptation survey item 2001 (Table 1) 1–5 2.80 1.10(11) e6 Adaptation survey item 2001 (Table 1) 1–5 3.43 1.28(12) pb1 Knowledge transfer survey item initial year (Table 1) 1–5 3.88 1.10(13) pb2 Knowledge transfer survey item initial year (Table 1) 1–5 3.64 1.32(14) pb3 Knowledge transfer survey item initial year (Table 1) 1–5 3.51 1.25(15) pc1 Replication survey item initial year (Table 1) 1–5 3.02 1.14(16) pc5 Replication survey item initial year (Table 1) 1–5 2.71 1.22(17) pc6 Replication survey item initial year (Table 1) 1–5 2.25 1.20

(continued overleaf )


886 C. Williams

APPENDIX 1 (Continued)

Variable Description Range Mean S.D.

(18) pc7 Replication survey item initial year (Table 1) 1–5 2.47 1.15(19) pe1 Adaptation survey item initial year (Table 1) 1–5 3.27 1.06(20) pe4 Adaptation survey item initial year (Table 1) 1–5 3.47 1.06(21) pe5 Adaptation survey item initial year (Table 1) 1–5 2.78 1.10(22) pe6 Adaptation survey item initial year (Table 1) 1–5 3.08 1.21(23) edelt12 Percentage change in subscribers/employee

from year 1 to year 2−0.33–6.76 0.70 1.25

(24) time Years since beginning of relationship 0–12 4.77 3.33(25) year First year of relationship 1989–2001 1996.23 3.33(26) new 1 = Receiving firm founded initial year of

relationship0–1 0.47 0.50

0 = Receiving firm founded before relationship(27) template 1 = Source firm has home country operation 0–1 0.85 0.36

0 = Source firm has no home country operation(28) cell 1 = Receiving firm operates only in cellular

markets0–1 0.52 0.50

0 = Receiving firm operates in fixed servicemkts

(29) curr−own Ownership stake of source firm, 2001 7–100 50.37 23.63(30) first−own Ownership stake of source firm, initial year 6–100 41.55 22.81(31) telpen01 Telephones per 100 inhabitants, country 2001 0.20–90.71 23.65 19.45(32) polcon Political risk 2001 0.00–0.78 0.36 0.22(33) telpen−i Telephones per 100 inhabitants, country initial

year0.20–90.71 23.52 19.44

(34) polcon−i Political risk initial year 0.00–0.78 0.36 0.22(35) lngdp−i Log(e) gdp per capita, initial year 5.72–10.47 8.05 1.14(36) lnpop−i Log(e) population, country initial year −2.81–5.10 2.55 1.53(37) lnpop01 Log(e) of population, country 2001 −2.81–5.15 2.54 1.57(38) lngni01 Log(e) of gross national income, 2001 5.56–10.55 8.18 1.10(39) lnsrcsubs Log(e) of source firm subscribers, 2001 10.52–18.78 17.11 1.63(40) lnrecsubs Log(e) of receiving firm subscribers, 2001 7.94–16.91 13.22 2.10(41) scountries Count of countries where source firm provides

telecom services1–52 17.98 12.88

(42) srelat Number of source firm relationships withinsame country as receiving firm

1–8 2.38 2.07



APP

EN

DIX

2:C

OR

RE

LA

TIO

NM

AT

RIX

(OB

SER

VE

DV

AR

IAB

LE

S)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(1)

b11.

00(2

)b2

0.96

1.00

(3)

b30.

750.

731.

00(4

)c1

0.46

0.37

0.42

1.00

(5)

c50.

430.

380.

560.

641.

00(6

)c6

0.46

0.42

0.58

0.48

0.77

1.00

(7)

c70.

440.

420.

520.

630.

680.

771.

00(8

)e1

0.34

0.30

0.28

−0.0

8−0

.02

−0.0

30.

041.

00(9

)e4

0.40

0.39

0.43

0.07

0.11

0.07

0.12

0.70

1.00

(10)

e50.

200.

160.

200.

110.

110.

160.

190.

570.

561.

00(1

1)e6

0.53

0.55

0.58

0.07

0.16

0.14

0.23

0.67

0.57

0.57

1.00

(12)

pb1

0.54

0.51

0.39

0.13

0.23

0.22

0.21

0.42

0.42

0.09

0.49

1.00

(13)

pb2

0.57

0.58

0.41

0.07

0.15

0.14

0.23

0.47

0.48

0.07

0.52

0.88

1.00

(14)

pb3

0.33

0.29

0.50

0.14

0.29

0.19

0.32

0.39

0.39

0.14

0.46

0.71

0.76

1.00

(15)

pc1

0.17

0.09

0.22

0.64

0.55

0.35

0.53

−0.0

9−0

.03

0.14

0.11

0.21

0.15

0.25

1.00

(16)

pc5

0.09

0.03

0.33

0.35

0.66

0.49

0.53

0.12

0.19

0.17

0.21

0.30

0.26

0.47

0.52

1.00

(17)

pc6

0.20

0.13

0.42

0.25

0.57

0.71

0.57

0.07

0.16

0.12

0.12

0.33

0.33

0.39

0.43

0.78

1.00

(18)

pc7

0.11

0.12

0.25

0.39

0.57

0.54

0.65

0.05

0.24

0.14

0.21

0.29

0.29

0.34

0.63

0.74

0.70

1.00

(19)

pe1

0.18

0.11

0.06

−0.0

1−0

.13

−0.1

4−0

.04

0.51

0.37

0.40

0.44

0.50

0.47

0.31

0.16

0.09

0.07

0.08

(20)

pe4

0.26

0.22

0.21

0.16

0.04

−0.0

60.

000.

220.

470.

240.

350.

420.

480.

390.

200.

000.

000.

12(2

1)pe

50.

010.

000.

120.

170.

170.

170.

130.

290.

480.

630.

300.

150.

160.

120.

270.

230.

270.

38(2

2)pe

60.

340.

330.

220.

13−0

.03

−0.0

80.

040.

440.

330.

370.

590.

390.

420.

250.

21−0

.01

−0.1

00.

00(2

3)ed

elt1

20.

120.

160.

130.

190.

090.

110.

270.

00−0

.04

−0.0

10.

010.

200.

290.

230.

100.

070.

100.

23(2

4)ti

me

0.13

0.10

0.19

−0.0

50.

130.

200.

140.

330.

350.

280.

290.

270.

240.

260.

210.

260.

360.

26


888 C. Williams

APP

EN

DIX

2:(C

onti

nued

)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(25)

year

−0.1

3−0

.10

−0.1

90.

05−0

.13

−0.2

0−0

.14

−0.3

3−0

.35

−0.2

8−0

.29

−0.2

7−0

.24

−0.2

6−0

.21

−0.2

6−0

.36

−0.2

6(2

6)ne

w0.

100.

090.

010.

04−0

.04

0.10

0.06

0.24

0.16

0.09

0.06

0.23

0.40

0.09

0.09

0.14

0.35

0.10

(27)

tem

plat

e−0

.18

−0.1

8−0

.25

−0.2

1−0

.25

−0.2

20.

18−0

.05

−0.1

40.

180.

07−0

.01

−0.0

20.

07−0

.13

−0.0

3−0

.23

0.05

(28)

cell

0.24

0.22

0.15

0.31

0.06

0.12

0.06

0.06

−0.0

6−0

.18

−0.0

80.

220.

340.

110.

230.

070.

21−0

.01

(29)

curr

−ow

n0.

530.

500.

310.

300.

270.

070.

140.

340.

250.

260.

340.

260.

300.

140.

240.

040.

020.

12(3

0)fir

st−o

wn

0.38

0.37

0.36

0.34

0.19

0.28

0.21

0.22

0.15

0.08

0.16

−0.1

1−0

.02

−0.2

4−0

.02

0.02

0.15

0.02

(31)

telp

en01

0.10

0.08

−0.0

3−0

.22

−0.3

7−0

.34

−0.2

90.

210.

17−0

.10

0.09

0.19

0.17

−0.0

2−0

.42

−0.1

6−0

.23

−0.2

9(3

2)po

lcon

0.11

0.15

0.32

−0.0

8−0

.07

0.05

0.06

0.21

0.22

0.15

0.26

0.07

0.13

0.24

−0.2

20.

010.

03−0

.10

(33)

telp

en−i

0.10

0.08

−0.0

3−0

.22

−0.3

7−0

.34

−0.2

90.

210.

17−0

.10

0.09

0.19

0.17

−0.0

2−0

.42

−0.1

6−0

.23

−0.3

0(3

4)po

lcon

−i0.

110.

150.

32−0

.08

−0.0

70.

050.

060.

210.

220.

150.

260.

070.

130.

24−0

.22

0.01

0.04

−0.1

0(3

5)ln

gdp −

i0.

150.

10−0

.01

−0.0

4−0

.21

−0.2

5−0

.18

0.21

0.07

−0.1

1−0

.01

0.10

0.17

0.11

−0.3

1−0

.17

−0.2

0−0

.20

(36)

lnpo

p −i

0.11

0.16

0.01

0.01

0.10

0.08

0.12

0.13

−0.0

20.

13−0

.04

0.05

0.24

0.23

0.03

−0.0

80.

050.

12(3

7)ln

pop0

10.

090.

13−0

.02

0.00

0.15

0.12

0.15

0.12

−0.0

10.

12−0

.05

0.02

0.21

0.18

0.04

−0.0

20.

090.

15(3

8)ln

gni0

10.

150.

110.

02−0

.11

−0.2

3−0

.26

−0.2

10.

400.

21−0

.04

0.16

0.21

0.22

0.13

−0.3

1−0

.15

−0.2

1−0

.23

(39)

lnsr

csub

s0.

150.

100.

180.

100.

150.

180.

260.

140.

140.

110.

270.

180.

090.

20−0

.04

0.17

0.17

0.22

(40)

lnre

csub

s0.

240.

190.

260.

230.

220.

190.

280.

280.

110.

400.

280.

130.

060.

170.

200.

180.

090.

21(4

1)sc

ount

ries

0.26

0.28

0.23

0.08

0.20

0.00

0.17

0.25

0.18

0.08

0.33

0.11

0.18

0.15

0.14

0.01

−0.0

30.

14(4

2)sr

elat

−0.0

60.

00−0

.11

−0.2

10.

02−0

.13

−0.0

40.

110.

130.

110.

00−0

.09

0.02

0.02

0.00

−0.1

8−0

.20

−0.0

6

(19)

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27)

(28)

(29)

(30)

(31)

(32)

(33)

(34)

(35)

(36)

(19)

pe1

1.00

(20)

pe4

0.67

1.00

(21)

pe5

0.54

0.47

1.00

(22)

pe6

0.63

0.59

0.35

1.00

(23)

edel

t12

0.04

0.02

0.10

0.06

1.00

(24)

tim

e0.

150.

080.

12−0

.02

−0.0

51.

00(2

5)ye

ar−0

.15

−0.0

8−0

.12

0.02

0.05

−1.0

01.

00(2

6)ne

w0.

340.

220.

110.

180.

390.

20−0

.20

1.00

(27)

tem

plat

e0.

05−0

.06

0.00

0.15

−0.0

3−0

.31

0.31

−0.1

51.

00(2

8)ce

ll0.

05−0

.01

−0.0

70.

160.

430.

000.

000.

66−0

.18

1.00



(29)

curr

−ow

n0.

260.

230.

190.

23−0

.15

0.13

−0.1

30.

11−0

.34

0.17

1.00

(30)

first

−ow

n0.

07−0

.06

−0.0

20.

070.

12−0

.10

0.10

0.32

−0.3

20.

490.

321.

00(3

1)te

lpen

010.

390.

120.

020.

220.

16−0

.18

0.18

0.18

0.19

0.15

0.06

0.23

1.00

(32)

polc

on0.

05−0

.04

0.11

0.10

0.19

0.19

−0.1

90.

020.

14−0

.02

−0.0

40.

050.

391.

00(3

3)te

lpen

−i0.

390.

120.

010.

220.

16−0

.18

0.18

0.18

0.19

0.15

0.06

0.23

1.00

0.39

1.00

(34)

polc

on−i

0.05

−0.0

40.

110.

100.

190.

19−0

.19

0.02

0.14

−0.0

2−0

.04

0.05

0.39

1.00

0.39

1.00

(35)

lngd

p −i

0.23

0.11

0.03

0.22

0.19

−0.3

20.

320.

080.

290.

090.

210.

080.

740.

390.

740.

391.

00(3

6)ln

pop −

i−0

.10

0.11

0.17

0.14

0.23

−0.1

20.

120.

12−0

.02

0.12

0.11

−0.1

3−0

.31

−0.2

4−0

.31

−0.2

40.

191.

00(3

7)ln

pop0

1−0

.12

0.09

0.16

0.11

0.24

−0.0

60.

060.

15−0

.03

0.08

0.09

−0.1

3−0

.34

−0.2

5−0

.34

−0.2

50.

140.

98(3

8)ln

gni0

10.

300.

100.

020.

320.

20−0

.06

0.06

0.14

0.23

0.11

0.19

0.07

0.80

0.50

0.80

0.50

0.90

0.02

(39)

lnsr

csub

s0.

120.

100.

100.

11−0

.09

0.10

−0.1

0−0

.05

0.33

−0.1

90.

03−0

.06

−0.0

10.

06−0

.01

0.06

0.18

0.21

(40)

lnre

csub

s0.

03−0

.16

0.24

0.29

0.17

−0.0

80.

08−0

.14

0.11

0.22

0.23

0.19

0.05

0.11

0.05

0.11

0.16

0.23

(41)

scou

ntri

es−0

.01

0.08

−0.0

30.

18−0

.09

0.11

−0.1

1−0

.03

0.00

−0.0

30.

430.

03−0

.17

0.06

−0.1

70.

060.

090.

37(4

2)sr

elat

−0.0

60.

110.

04−0

.01

−0.2

40.

18−0

.18

−0.0

60.

12−0

.19

0.37

−0.2

7−0

.16

0.10

−0.1

60.

100.

150.

35

APP

EN

DIX

2:(C

onti

nued

)

(37)

(38)

(39)

(40)

(41)

(42)

(37)

lnpo

p01

1.00

(38)

lngn

i01

−0.0

11.

00(3

9)ln

srcs

ubs

0.27

0.13

1.00

(40)

lnre

csub

s0.

210.

180.

191.

00(4

1)sc

ount

ries

0.41

0.08

0.56

0.29

1.00

(42)

srel

at0.

370.

140.

22−0

.02

0.63

1.00


transfer in context: replication and adaptation in knowledge transfer relationships

Documents