dynamknowlflow

The dynamics ofknowledge flows:human capital mobility,knowledge retentionand change

Tammy L. MadsenElaine Mosakowski andSrilata Zaheer

Variation or change and knowledge retentionaffect a firms knowledge production.Tradeoffs in the amount of resourcesdedicated to these activities influences thedevelopment and distribution of knowledgewithin firms across time (March, 1991). Forinstance, a firm that preserves the past viaretention may dedicate less resources tocreating knowledge through variation activityand to acquiring tacit knowledge and skills, orhuman capital. On the other hand, a firm mayimport more knowledge via inflows of humancapital when it trades off exploiting the pastfor increasing knowledge creation or variationactivity. As a consequence, a general concernof studies examining learning and adaptationis the balance between a firms variation andretention activities (Madsen and McKelvey,1996). How do variation and knowledgeretention affect the development anddistribution of knowledge within firms? Morespecifically, how do these activities affect theflow of tacit knowledge and skills, or humancapital, into a firm?The movement of personnel is widely

recognized as a mechanism for distributingtacit knowledge and skills, or human capital,across space and time (Almeida and Kogut,1999; Cooper, 2001; Gruenfeld et al., 2000).Moreover, research suggests that variations infirms knowledge bases are facilitated byindividuals (Argote and Ingram, 2000). Thetacit knowledge and skills held by a firmsmembers, whether the members areestablished or newcomers, are thereforecrucial to a firms knowledge production. Fewlongitudinal empirical studies, however,explore the links between knowledge flowswithin or across a firm s boundaries andactivities underlying a firms knowledgeproduction, such as variation and retention.This is surprising because knowledge inflowsmight stimulate a firms variation orknowledge creation activity whereasknowledge retained from a firms pastexperience might attenuate future knowledgeinflows. We address these dynamics byexamining how a firms variation intensity andknowledge retention intensity affect the

The authors

Tammy L. Madsen is the Dean Witter Foundation Fellowand Assistant Professor of Strategy, Management

Department, Leavey School of Business, Santa Clara

University, Santa Clara, California, USA.

Elaine Mosakowski is based at Krannert School, PurdueUniversity, West Lafayette, Indiana, USA.

Srilata Zaheer is Carlson School Term Professor ofInternational Management at Carlson School of

Management, University of Minnesota, Minneapolis,

Minnesota, USA.

Keywords

Human capital theory, Personnel, Job mobility,

Knowledge, Learning

Abstract

This empirical paper investigates the relationships

between the amount of human capital that flows into a

firm and two activities underlying a firms knowledge

production, variation or change and knowledge retention.

We track the flow of human capital within and across

organizational and geographic space for all multi-unit

banks operating in the world foreign exchange trade

industry from 1973 to 1993. The findings indicate that an

increased reliance on past experience reduces how much

human capital a firm imports in the future. This effect is

moderated by a self-reinforcing cycle of human capital

inflow. Inflows of human capital also decline when a firm

has recently adopted novel changes in its operations. The

paper uses evolutionary thinking to define a model for

intrafirm knowledge production.

Electronic access

The current issue and full text archive of this journal is

available at

http://www.emeraldinsight.com/1367-3270.htmThis paper s development benefited fromdiscussions between the first author and JacquesDelacroix. The first author gratefully acknowledgesthe Dean Witter Foundation for their financialsupport. All errors are the responsibility of theauthors.

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Volume 6 . Number 2 . 2002 . pp. 164176

# MCB UP Limited . ISSN 1367-3270

DOI 10.1108/13673270210424684

amount of knowledge it imports via inflows ofhuman capital.The classic evolutionary process of

variation, selection, and retention (VSR) isviewed as a mechanism for developing andorganizing knowledge within a firm (seeCampbell, 1969; Weick, 1969, 1979).Research suggests that, given a context ofcompetitive pressures, VSR interact inside afirm to form a machine or engine forproducing knowledge (Plotkin, 1994). Ourmodel of a firms knowledge-productionengine builds on this work. We first define theengine s variation and retention components;ensuing sections describe the engine.Variation involves the creation of knowledgethat generates novel or wholesale-typechanges in a firms ways of operating.Variation intensity refers to the amount ofnovel changes that a firm adopts at a point intime. Retention explains the preservation andrefinement of changes or variations in thebehaviors adopted by a firm and thesubsequent dispersion of these changes acrossthe firms subunits. Through dispersion, afirm leverages its new and past knowledgeacross space and time. Retention intensityindicates how much retention a firm pursuesannually. The content that is retained by afirm represents knowledge about its existingand past behaviors and is stored in differentretention bins that form the firms memory(Walsh and Ungson, 1991). The state of eachretention bin partially reflects the outcomes ofpast human capital inflow. A retention bin scontent also feeds back to a firms variationactivity, affecting its future stock of humancapital inflow. In this process, a firm sretained knowledge may be combined withnew knowledge to generate a novel change orvariation. The relationship between variationand retention is thereby recursive rather thanopposing. However, firms engage in variationand retention simultaneously. These activitiesconsequently compete for resources within afirm. Examining variation and retentionsimultaneously controls for these effects.The context for this longitudinal study is

the world population of multi-unit banksoperating in foreign exchange (FX) trading.The data span multiple levels of analysis andallow us to track the movement of over150,000 currency traders (individuals) across47 countries and 2,300 trading rooms(subunits) of 431 parent banks (firms) from1973 to 1993. We examine variation in and

retention of one critical organizing pattern,the mix of experience in a bank s tradingrooms.A trading rooms success is substantially

dependent on the mix of talent among itstraders (Cetina and Bruegger, 2001). Thistalent is largely based on tacit knowledgedeveloped through experiential learning. Arecent study of a Swiss bank shows that thebank typically monitors and adjusts theexperience mix in its trading rooms; one ofthe bank s managers explained that eachroom requires a particular mix of stars , talents and reliables (Cetina andBruegger, 2001, p. 189). Star traders holdthe room together and build the firmsreputation, talents grow into and eventuallyreplace stars, and reliables fill out the mix(Cetina and Bruegger, 2001, p. 189). Roomswith too many stars might encounterleadership conflicts whereas rooms with onlyone star, and without a sufficient mix ofsupport, expose a bank to risk. A large gap intraders experience might also harm a tradingroom s performance. Firms consequentlyattempt to assemble a mix of traders withexperience that is not too distant as well as amix that forms a solid team. Moreover, aportion of each trader s experience is based ontheir position in the trading room and on theirrelationships with other traders. For instance,inexperienced traders often work withseasoned traders to gain experience beforeentering the market on their own. A tradingroom s experience mix therefore underlies aset of roles and interrelationships that definehow the traders in a room interact andcoordinate their behaviors. As these roles andinterrelationships become embedded in afirm, depicting task differentiation andcontrol, they form a set of higher-orderorganizing patterns. Over time these patternsare stored in a firms memory, forming aretention bin (Walsh and Ungson, 1991).This experience mix, referenced as a bank sorganizing pattern, thus represents a way ofstructuring the relationships among traders ina room in response to external or internalpressures on a firm.Banks in the FX industry operate multiple

units dispersed in different nationalenvironments and experience human capitalinflows from multiple sources. This allows usto track human capital mobility within andacross organizational and geographic spaceover time. The term inflow references the

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Volume 6 . Number 2 . 2002 . 164176

movement of human capital into a firmssubunit. Prior research disaggregates theinflows into types based on their spatialorigin, such as interfirm vs intrafirm or cross-border vs local (Madsen et al., 2001). For thisstudy, we define the aggregate amount ofhuman capital that flows into a firm annuallybased on the subunits operated by the firm.The next section presents the theory andhypotheses. For brevity, we limit ourarguments to two main streams of research.

Theory and hypotheses

Variation and retention: parts of aknowledge-production engineFirms evolve through a cycle of VSR thatoccurs at multiple hierarchical levels (e.g.individual, subunit, firm, population,community) (Aldrich, 1979; Baum andSingh, 1994). This paper focuses on theintrafirm level of analysis. We do not presumethat firms evolve in exactly the same way thatspecies do. Instead, we view evolutionarythinking as a tool, such as a hammer; it is ablunt tool but useful in many circumstances.We also acknowledge that by considering onlytwo components of the VSR process that wedo not measure evolutionary learning.Nevertheless, since we are interested in howvariation and retention affect a firms stock ofhuman capital inflow it is useful tounderstand the roles of variation andretention in a firms knowledge-productionengine.Variation is primarily driven by a firms

ability to create new ways of operating andnew opportunity sets via combinative learning(Aldrich, 1999). Based on these subactivities,firms develop a portfolio of variations fromwhich managers select. A firm s internalselection process is guided by variousevaluation or control mechanisms that stemfrom the firms social norms andadministrative structures (McKelvey andAldrich, 1983; Burgelman, 1994; Miner,1994). Variations selected by a firm areretained in the firms memory and dispersedacross the firms subunits. The content that isretained by a firm feeds back to the variationand selection processes thereby activelyinfluencing subsequent knowledge creationand evaluation activities. New ideas generatedin the variation phase are therefore,conditioned by a firms stock of experientially

developed knowledge and are subject toscreening based on the firm s existingstructures and norms.Retention encompasses the preservation,

dispersion and refinement of retainedvariations. A behavioral pattern or routineretained by a firm in one of its subunits maysubsequently be dispersed to a peer subunit.Applying the pattern in this new context maygenerate refinements in the pattern sexecution and performance. Other scholarsview this replication or dispersion process asseparate from retention (see Zollo andWinter, forthcoming). But it is difficult todefine either process without invoking theother. Moreover, in multi-unit firms, thewider spread of a behavioral patternrepresents both preservation at the firm leveland subsequent dispersion of the pattern atthe subunit level. Finally, external forcesselectively discriminate against some firmsand favor others based on the variationsretained by those firms. Figure 1 lists some ofthe general activities underlying eachcomponent of the knowledge-productionengine. The figure also indicates thatpressures internal and external to a firm mightaffect its knowledge production. Sincevariation and retention have somewhatcompeting roles in knowledge production,they may affect the stock of knowledge thatflows into a firm in different ways.Subsequent sections explore thesedifferences.

Variation intensity and human capitalinflowLearning or knowledge-based argumentssuggest that firms that are more disposed tovariation or exploratory-type behavior may bemore receptive to inflows of knowledge.Firms that have developed experience withchange tend to remain flexible and in turn, areless reluctant to explore unfamiliar ways oforganizing (Hedberg et al., 1977). Theirsearch for knowledge is therefore, less likely tobe limited to their immediate neighborhoodsof expertise. Moreover, new personnel mightprovide the knowledge diversity needed forgenerating new ways of organizing. Thesecharacteristics suggest that increases invariation intensity will contribute to anincrease in the amount of human capitalflowing into a firm.Research on organizational ecology also

suggests that increases in variation intensity

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may stimulate increases in human capitalinflow. Change disrupts organizationaloperations (Amburgey et al., 1993; Delacroixand Swaminathan, 1991). Turnover, basedon the outflow and inflow of personnel, isoften a by-product of such disruptions (Baronet al., 2001). One explanation is thatvariations in a firms organizing patterns mayaffect communication and coordinationpatterns among personnel. Because anindividual s attachment to a firm developsover time as a function of their experienceswith the firm, changes to the firms organizingpatterns may disrupt the individual straditional ways of operating and theiracquired legitimacy. These effects may loweran individual s attachment to the firm andincrease the likelihood that the individual willleave (Sorensen, 2000). These argumentssuggest that personnel departures mayincrease among individuals who haveexperienced novel or wholesale changes intheir teams, groups or subunits. In response,firms seeking to replenish their human capitalmay increase their stock of human capitalinflow. These arguments lead us to predictthat increases in a firms variation intensitywill generate an increase in human capitalinflow.H1. Increases in a firms variation intensity

will generate an increase in humancapital inflow.

We are also interested in understanding ifvariation attenuates or strengthens therelationship between a firms current stock ofhuman capital inflow and its future stock ofhuman capital inflow. If history matters, afirms propensity to import human capitalmay be a function of the amount it imported

in the past. As a firm gains experience withimporting human capital, it may view inflowsas useful mechanisms for solving a broaderand more complex set of problems. If thisholds then one might expect a firms stock ofhuman capital inflow to have positive pathdependence or to be self-reinforcing. Giventhe prediction that variation intensity is alsopositively related to future inflows of humancapital (H1), we expect that the interaction ofthese two effects will yield a positive effect.Stated another way, we hypothesize thatincreases in a firms variation intensity willaccentuate the positive relationship between afirm s existing stock of human capital inflowand its future stock of human capital inflow.H2. Variation intensity will accentuate the

positive relationship between a firmsexisting stock of human capital inflowand its future stock of human capitalinflow.

Retention intensity and human capitalinflowLearning or knowledge-based arguments alsosuggest that firms that adhere to theirtraditional ways of operating may be lessreceptive to importing knowledge via humancapital inflow. One explanation is thatindividuals new to a firm may introducediversity that disrupts the firms ways ofoperating. For one, new personnel may carryhuman capital that differs from the recipientfirm s human capital stock. These differencesmay lead a firms members to question orchallenge the efficacy of the firm s retainedorganizing patterns. It is also possible thatnew personnel bring human capital thatoverlaps with a recipient firms human capital

Figure 1 Internal VSR as a knowledge-production engine

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stock or that new personnel are lessknowledgeable than a recipient s existingmembers. In both instances, diversity maystill increase in the recipient firm because thenew personnel are less socialized to therecipient s organizational context. Moreover,frequent changes to the tenure distribution ofpersonnel disrupt the socialization processbecause they affect the dynamics ofinteraction among employees (March, 1991;Sorensen, 2000). Changes to employee sinteraction patterns may, in turn, affect afirms organizing patterns. This effect may bemore prevalent in contexts such as the FXindustry where employees frequently interactto accomplish their daily activities. Sinceinflows of human capital might disrupt afirms traditional organizing patterns, firmsthat increasingly rely on their past mayrestrict, or at least reduce, the amount ofhuman capital they import.The preceding arguments are consistent

with research in organizational ecology thatsuggests that increasing retention intensity islikely to promote a firms resistance to changeor to factors that might stimulate change.When firms repeat a pattern of behavior, theircompetence at the behavior increases and thebehavior becomes institutionalized within thefirm. Increases in competence increase thelikelihood of rewards from the behavior andthe likelihood that the firm will retain thebehavior for future use. This self-reinforcingcycle makes exposure to new personnel thatmight disturb the status quo less attractive(Levitt and March, 1988). Past learning,through refinement of a behavioral pattern,may therefore inhibit future opportunities forknowledge development (March, 1991).These arguments lead us to predict thathuman capital inflow may decline withincreases in a firms retention intensity.H3. Increases in a firms retention intensity

will generate a decrease in humancapital inflow.

If one considers the interaction of retentionintensity, which predicts a negative effect onfuture human capital inflow, and humancapital inflow, which predicts a positive effecton future human capital inflow, the net resultis ambiguous. Recall that the content of afirms retention bins is based on knowledgethat the firm has developed over time.Exploiting knowledge developed in the pastincreases a firms accountability and

reliability. However, it also generates inertia,which impedes a firms ability to break fromtradition. Given these effects, we predict thata firm s retention intensity will attenuate thepositive relationship between existing andfuture stocks of human capital inflow.H4. A firm s retention intensity will

attenuate the positive relationshipbetween a firms stock of humancapital inflow and its future stock ofhuman capital inflow.

Data

Data sourcesThe data stem from annual publications ofthe Foreign Exchange and Bullion DealersDirectory (Hambros Bank, London, 1973-1993). Data collection occurred in twophases. Phase one documented all themarket-making trading rooms in the FXindustry (approximately 26,763 rooms heldby over 1,500 parent banks worldwide) from1973-1993, their parent bank affiliations, thenumber of hierarchical levels in a room, andthe trading rooms locations (city andcountry). In 1993, trading rooms werelocated in 47 countries and parent banks wereheadquartered in 65 different countries.Interviews were also conducted in over 40trading rooms. Phase two documented thenames and positions of the traders operatingin the industry during the 21-year period(approximately 150,000 names) and thenumber of traders operating in each tradingroom. The number of names documented perdirectory-year ranges from 2,965 in 1973 to11,522 in 1993. In addition, 25 telephoneinterviews were conducted with US traders tounderstand the attributes that are critical to atrading rooms performance. The interviewswere semi-structured and all respondentswere asked the same set of questions. In theinterviews conducted in phase one and two,all traders indicated that two dimensions arecritical to performance: the experience oftraders in a trading room; and how a room isorganized.The archival data is restricted to market-

making trading banks, which are bankscommitted to buying or selling any amount ofcurrency in the interbank market. The datawere cleaned to ensure that listing lapses werenot counted as room exits. One concern isthat the directory only prints the names and

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positions of 12 traders per room. Therefore,in 11 percent of all trading rooms on averageover time, we do not know if the 12 traderslisted represent the complete trader roster. Inall other cases, however, we expect the traderinformation to be relatively complete. Noincentive exists to omit traders as inclusion inthe directory facilitates contact by othertraders and each trader listed receives acomplimentary copy of the directory. Weacknowledge that this characteristic mayunderstate trader mobility for large tradingrooms. The archival data are also leftcensored since many rooms existed prior to1973. We recognize that beginning theobservation period in 1973 may introduceerrors due to left censoring (Tuma andHannan, 1984, pp. 128-35). Although dataare available prior to 1973, the introductionof floating exchange rates in 1973 representedan institutional event that significantlyaffected currency trading. In addition,Reuters introduced a computerized foreignexchange system in 1973 that facilitated thedevelopment of an electronic market. Eventhough the computerized system initiallyprovided only general news and priceinformation, it eventually made marketinformation more explicit and more readilyaccessible worldwide. This event, and themore significant institutional change, suggestthat 1973 provides an effective demarcationpoint for beginning our analysis. A detailedindustry description is available from the firstauthor on request.

Sample and unit of analysisOur sample includes all banks operatingmultiple trading rooms (N = 431). The focalunit of analysis is the parent bank. Datacollection spanned three levels of analysis the parent firm or bank, the trading room orsubunit and the individual trader. In all cases,we aggregate data at lower levels to the parentfirm level of analysis. Theoretical andempirical issues motivate this approach. First,from a theoretical standpoint, multi-unitfirms, especially multinational ones, arecomplex organizations. Subunits within thesame parent firm are more tightly linked witheach other than with subunits operated by acompeting parent firm. These strong tiescoupled with common firm-specificknowledge and language enhance the efficacyof intrafirm knowledge diffusion (Kogut andZander, 1996; Zander and Kogut, 1995). As

a consequence, a change in behavior adoptedby one subunit will be shared with andcommunicated more readily to a siblingsubunit than to a rival s subunit. Through thissystematic process of inheritance, subunitsbenefit from the knowledge production ofother subunits operated by their parent firmin addition to their own knowledgeproduction.Turning to empirical issues, our interest lies

in testing the effects of the amount ofretention and variation on human capitalinflow. Measuring a multi-unit firms amountof retention activity at one point in timerequires examining whether the firm dispersesbehaviors across its multiple subunits. Firmsoperating only one subunit may preservebehaviors over time but, by definition, cannotaffect wider dispersion of behaviors acrossmultiple subunits. Conducting the analysis atthe subunit level would thereby not allow usto capture both of the activities that underlieretention, preservation and dispersion.Conducting the study at the subunit levelwould also limit our research scope toinvestigating whether or not a variation eventoccurred rather than how shifts in a firmsamount of variation activity affect its futurestock of human capital inflow.

MeasuresHuman capital inflowA human capital inflow event occurs when atrader moves into a trading room fromanother trading room operated by its parentbank, locally or across borders, or from atrading room operated by a rival parent bank,locally or across borders. For each event, wedefine the amount of experience that a traderbrings into a recipient room. These amountsare summed at the room level and weightedby a room s size (the number of tradersoperating in a room). The weighted averagesare aggregated to the parent bank level. Thisaggregate value is weighted by a parent bank ssize (the number of rooms operated by aparent bank). The resulting bank levelvariable represents the annual stock of humancapital inflow to a bank.

Retention intensityRetention involves the preservation andsubsequent dispersion of organizing patterns,previously adopted by a bank in certainsubunits, across a bank s other subunits. Tobegin, we classify a trading rooms behavior in

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a given year into three mutually exclusiveevent categories: retention, variation (change)and no change. Although the no changecategory is not included in our analysis, itserves two critical purposes: first, to capturecases where a retention or variation eventdoes not occur and, second, to allow thatretention and variation are not simplyopposite constructs. As mentioned, we focuson the retention of one critical organizingpattern, the mix of trader experience in atrading room.The experience mix captures the diversity

of the knowledge base among traders in aroom. A shift in the experience mix, such asmoving a trader with 20 years of experience orintroducing a set of rookie traders, influencesthe knowledge base of a trading room andimpacts its day to day operations. Juniortraders may be less responsive to rapid marketchanges whereas more experienced tradersmay be more sensitive to rate movements andmarket liquidity. Moreover, traders in a roomoften work as a team, with experiencedtraders assuming a mentor role. For instance,senior traders might assist junior traders incovering positions prior to market closing.Rookie traders thereby benefit fromexperienced traders expertise.A rooms experience mix also represents a

set of roles and interrelationships that defineacceptable behaviors. Over time, thesebehaviors become embedded in a firm andreflect codes that define how a firm respondsto external forces (Walsh and Ungson, 1991).The shared codes among a firms membersguide organizing patterns, such ascoordination and communication patterns.Nevertheless, there is a downside toexperience. The fast-paced nature of currencytrading wears on traders over time and as aresult, traders often burn out after just a fewyears of trading. Traders do not necessarilyburn out from expertise they have developedbut from the pace of practical experience thathelped them to develop expertise. As a result,firms experiment with a balance betweenyouth and experience in their trading rooms.Extensive interviews with over 65 tradingroom managers indicated that somecombination of senior, mid-level and juniortraders must exist in a room moreexperience is not necessarily an advantage. Intheir continual search for an optimalexperience mix, managers decide whether to

retain the status quo in their rooms or to try adifferent mix.Defining a trading room s experience mix

required developing an algorithm thatcalculated each trader s cumulativeexperience (the number of years a traderworked in the industry) from 1973 to 1993.The algorithm compares a trader s last name,first initial and second initial across years.When a match occurs (all three parts at time tmatch all three parts at time t 1), a trader sexperience count is incremented. Wheneverpossible, the experience measures wereconstructed using unique trader names. Incases where a match is ambiguous, data onthe bank and country assignments are used toidentify a trader s history[1]. The algorithmcalculates a trader s cumulative experience inthe industry; the experience counter is notreset to zero when a trader moves to a newlocation or works for a different parent bank.For example, consider a trader who works forCitibank for three years and then works forFuji Bank for two years. Based on thematching algorithm, the trader s totalexperience is equal to five years.A rooms experience mix is the sum of the

experience of the traders in that room.Because the experience mix might vary withchanges in the number of traders in a room,the construct is normalized by the number oftraders in a room (e.g. a rooms size). Thisratio, referred to as a room s experience ratio,equals the total amount of trader experiencein a room divided by the number of traders ina room. We calculate each parent bank saverage room experience ratio annually basedon all the trading rooms operated by a parentbank. A bank s average experience ratioreflects a pattern of acceptable organizingbehaviors that a bank has implemented in itsrooms. This baseline pattern, or commoncode, defines how a parent bank responds toexternal pressures and serves as a template forreplicating the organizing pattern across thebank s rooms. The baseline pattern becomesinstitutionalized as the number of a parentbank s trading rooms adopting the organizingpattern increases (Tolbert and Zucker, 1983).We do not presume that any pattern adoptedis effective. We do suggest, however, that abank s wider dispersion of a pattern across itsrooms signals that the bank may value thepattern.Retention at the room level is then defined

as a change in a rooms experience ratio that

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moves a room to a position that is less thanone standard deviation away from its parentbank s average experience ratio in theprevious year. This measure is similar to adistance measure; that is, we examine howclose or how far a room is to its past behaviorand to its parent bank s baseline behavior.The retention construct is adjusted for theeffect that total room experience evolves withcumulative trader experience. We sum theretention event counts across each parentbank for each year. The construct is weightedby a parent bank s size since a firms retentionamount will vary by firm size. The finalconstruct thus captures a firms retentionintensity.

Variation intensityVariation represents a meaningful alterationin a firms state, form or function. Since onlythe variations selected and retained by a firmare subject to external market pressures(Nelson and Winter, 1982), we focus onactual variations in a trading room or subunit.A variation event occurs when a change in arooms experience ratio moves the room to anexperience ratio that is greater than onestandard deviation away from the parentbank s average experience ratio. This design isintended to capture only meaningful or novelchanges in a rooms organizing pattern ratherthan cosmetic or incremental deviations froma rooms organizing pattern. A variation eventis therefore not simply any type of change.The design is distinguished from existingstudies because we define a variation event byrelating a change in a rooms organizingpattern to its parent firm s past baselinepattern. The variation event counts aresummed across each parent bank annuallyand the measures are normalized for thenumber of rooms a bank operates. Thisapproach allows us to identify the annualamount of variation that a bank is engaging inrather than simply whether or not a variationevent occurred.

Control variablesOther firm effectsThe model specifications include a measurefor a parent bank s size. Large firms mayoperate a higher number of trading rooms andin turn, employ more traders than small firms.Large firms chances of experiencing humancapital inflow and outflow may therefore begreater than small firms chances. Research

also suggests that large firms are susceptibleto inertial effects (Barron et al., 1994). If thisholds then large firms may be less receptive tohuman capital inflow than small firms. Toaddress these issues, the model specificationsinclude a measure of a parent bank s size,defined as the number of rooms a parent bankoperates in a given year. We use the logtransformation of a parent bank s size toreduce skewness in the distribution. We alsomeasure the total amount of human capitaloutflow from a parent bank s trading rooms.A firm may increase its stock of human capitalinflow to replace human capital lost viapersonnel departures. A human capitaloutflow event occurs when a trader exits atrading room and the trader is absent from theroom for at least two years. The humancapital outflow events are summed acrosseach parent bank annually. The ages of abank s trading rooms may also affect its futurestock of human capital inflow. In the simplestsense, a bank with new or young tradingrooms might source more human capital thana bank with a set of established rooms. As atrading room ages, the relationships and thepatterns of coordination and communicationamong its members become moreinstitutionalized. Older trading rooms maytherefore engage in lower amounts of humancapital inflow than younger trading rooms. Tocontrol for these effects, we include a variablethat captures the average age of a bank strading rooms after institutional change. Weuse the log transformation of a rooms age toadjust for skewness in the distribution.

Industry effectsThe analysis also includes measures forindustry characteristics that might affecthuman capital inflow. Density captures thenumber of competitors in the industry andrepresents a proxy for the number of jobsavailable in the worldwide currency exchangemarket (see Sorensen (2000) for a similarapproach). The density measure assumes thateach firm presents an equal competitivethreat. Variation in firm size, however,contributes to variance in firms competitivestrengths (Barnett and Amburgey, 1990).Including a mass variable, defined as the sumof the log(size) of all the firms minus thelog(size) of the firm under observation,partially captures this heterogeneity in ability(Barnett and Amburgey, 1990). Bothmeasures are calculated annually. We also

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include a dummy variable for each yearobserved to control for time period effects.

Model specification and estimation

Model specificationsThe two models used to test the hypothesesare specified as follows:

HCIit 1HCIit1 1Retentionit1 2Variationit1 3Variationit1HCIit1 1zit1 1vi it1 "1it

1

HCIit 2HCIit1 4Retentionit1 5Variationit1 6Variationit1HCIit1 2zit1 2vi it1 "2it

2

and

"nit "nit1 unit 3

Where subscript i references the firm,subscript t indexes the time period, is thecoefficient on the lag dependent variable, z isa vector containing a set of control variablescapturing firm and industry effects, v indexesa set of dummy variables that capture the timeperiod effects, and represents a selectionparameter. The error term, ", includes aninfluence from error in the prior time period,"nit-1, and a disturbance associated with thecurrent time period, unit (Greene, 1993).Parameter determines the magnitude ofcarryover in error from the prior time period.In equation (1), H1 predicts that 2 > 0 andH2 predicts that 3 > 0, conditional on1 > 0. In equation (2), H3 predicts that 4 < 0 and H4 predicts that 6 < 0,conditional on 2 > 0.Pooling observations over time for each

firm increases the chance that OLSrequirements of independence are violated.This may bias parameter estimates. Toaddress this issue, we estimate fixed effectsmodels by subtracting the mean for eachvariable across all observations of a firm overtime from the value of each variable andsurpressing the intercept. We also use aproportionality variable to correct forheteroscedasticity effects. Lag lengths wereidentified by evaluating Akaike sInformation Criteria and the correlogramsfor each variable.

The use of time series data mandates a testfor autocorrelation of the error terms in eachequation. The standard Durbin-Watson testis inappropriate for models with lagdependent variables (Greene, 1993). Insteadwe use a modified Breusch-Godfrey test(Greene, 1993, p. 428). The test does notreject the null hypothesis of zeroautocorrelation in each equation s errorterms. When a model includes a lagdependent variable and the error terms areautocorrelated, the OLS estimator isinconsistent and the residuals on which isbased are also inconsistent. The traditionalGLS estimators are therefore not usable.Estimation can be achieved, however, usingan alternative instrumental variables methodfollowing Hatanaka (1976) (see also Greene,1993). This method decomposes the errorterm into both random noise and an estimateof serial correlation, therefore, we can befairly confident that our dependent variable smeasure is not confounded by persistentunobserved effects.Finally, sample selection bias may result

when parameters that influence the inflow ofhuman capital also cause firms to be selectedout of the sample (Heckman, 1979). Themodels include a parameter to control for theimpact of selection processes on a firms stockof human capital inflow. Using ageneralization of Heckmans (1979) two stagesample selection model according to Lee(1983), the selection parameter, , iscalculated as follows:

1Ft

1 Ft

where is the probability density function, is the cumulative distribution function andF(t) is the cumulative hazard function. Theselection parameter, , is calculated for everyfirm-year.

Results

Table I summarizes the results of thehypotheses tests. The results of equations (1)and (2) are presented in Table II. Prior todiscussing the results in detail, we brieflysummarize some of the patterns observed inthe main variables of interest. For brevity, wedo not report the correlation matrix here.However, it is important to note that thecorrelation between variation intensity and

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retention intensity is only 0.28; thecorrelations between human capital inflowand a firms variation intensity and retentionintensity, respectively, are less than theabsolute value of 0.1. Figure 2 shows thatduring the initial years following institutionalchange, firms in the FX industry on averagepursued more variation activity than retentionactivity. After 1977, the data indicate that thefirms engaged in approximately twice as muchretention activity than variation activity.Figure 3 shows that the average amount ofexperience that a trader brings to a firmfollows an upward trend until 1987. For theremaining years observed, the averageexperience carried into a firm ranges from fiveyears to approximately 6.5 years.Table II reports the parameter estimates for

equations (1) and (2). The coefficients on thelag dependent variable are positive andsignificant in both models. This findingsuggests that a firms stock of human capitalinflow is self-reinforcing. This is consistentwith our conjecture that the more experiencea firm has with importing human capital, themore likely the firm may look to humancapital inflow as a mechanism for solving abroader set of problems. Models 1 and 2 showthat the coefficient for variation intensity isnegative and significant. This findingcounters our prediction that increases invariation intensity will generate an increase ina firms stock of human capital inflow.Instead, it appears that, in the FX industry,firms that increase their variation intensityresist additional inflows of human capital inthe immediate future. Model 1 shows that thecoefficient for the interaction of variationintensity and human capital inflow is alsonegative and significant, accentuating the

main negative effect of variation intensity onhuman capital inflow. This finding is counterto H2. These results suggest that increases invariation intensity may have stronger effectson firms that have engaged in high amounts ofhuman capital inflow than on firms that haveimported lower amounts of human capital.Consistent with H3, model 2 indicates that

increases in retention intensity generate adecrease in the amount of human capital thatflows into a firm. The coefficient for retentionintensity is negative and significant in model 2.

Table I Summary of results

Results

H1. Increases in a firms variation intensity will generatean increase in human capital inflow Rejected

H2. Variation intensity will accentuate the positiverelationship between a firms existing stock of human

capital inflow and its future stock of human capital

inflow Rejected

H3. Increases in a firms retention intensity will generate adecrease in human capital inflow Supported

H4. Retention intensity will attenuate the positiverelationship between a firms existing stock of human

capital inflow and its future stock of human capital

inflow Rejected

Table II The effects of variation intensity and retention intensity onhuman capital inflow for multi-unit banks in the foreign exchange trade

industry, 1973-1993

Dependent variableHuman capital inflowt

Independent variables 1 2

Human capital inflowt-1 0.23***

(0.02)

0.26****

(0.02)

Variation intensity 0.21***

(0.06)

0.33****

(0.06)

Retention intensity 0.06(0.04)

0.13**

(0.04)

Variation intensity human capital Inflowt-1 0.26)****(0.02)

Retention intensity human capital inflowt-1 0.13****(0.02)

Firm effectsHuman capital outflow 0.02****

(0.004)

0.02****

(0.004)

Log(size) 0.28*

(0.12)

0.28*

(0.12)

Banks average subunit log(age) 0.11(0.10)

0.07

(0.10)

Industry effectsDensity 0.004

(0.004)

0.006

(0.004)

Mass 0.03****

(0.008)

0.03****

(0.008)

0.03**

(0.009)

0.03***

(0.009)

0.007****

(0.0002)

0.007****

(0.0002)

Adj R2 0.71 0.71Number of firms 431 431

Notes:* p B 0.05, **p B 0.01, *** p B 0.001, **** p B 0.0001All variables are lagged one time period, standard errors are inparenthesesThe models are fixed effects using a mean-centering approach accordingto Greene (1993)Models were estimated using Hatanaka correction for autoregression withlag dependent variables. Weighted least squares estimation was used tocorrect for heteroskedasti c errors is the adjustment for sample selection bias (Lee, 1983)

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The coefficient for retention intensity inmodel 1 is also negative but not significant.The coefficient for the interaction of retentionintensity and human capital inflow issignificant but positive rather than negative.This finding suggests that the indirect effectsof retention on future human capital infloware lower for firms that have a high stock ofhuman capital inflow. One interpretation ofthis finding is that a continuous inflow ofhuman capital might assist firms with highretention intensity in breaking from tradition.The results for the firm and industry level

effects are consistent across models. Asexpected, the findings show that humancapital inflow increases with increases in afirms size and with increases in rivalscompetitive strengths. The coefficients for thelog of firm size and for mass are positive andsignificant in both models. We were surprisedto find that inflow decreases with increases inhuman capital outflow; the coefficient for

human capital outflow is negative andsignificant in both models. In sum, thefindings are consistent with H3 but opposeH1, H2 and H4.

Discussion

Firms are increasingly interested in factorsthat facilitate or constrain their abilities toproduce, leverage and transfer knowledge.Moving tacit knowledge and skills isparticularly challenging given that it is largelyembedded in individuals. Tracking humancapital mobility within an industry over 21years offers one way to systematically explorethe dynamics of one type of knowledge flow.Furthermore, relating a firms variation andretention activity to human capital inflowprovides a way of understanding howactivities underlying knowledge productionmight affect a firms future stock of tacitknowledge and skills. Limitationsnotwithstanding, the findings provide strongsupport for the idea that knowledge retainedin the past may restrict how much humancapital a firm imports in the future.Nevertheless, the self-reinforcing cycle ofhuman capital inflow appears to moderatethis effect. Surprisingly, inflows of humancapital also tend to decline with recentexperience with change. This negative effectappears stronger for firms that have importedlarge amounts of human capital than forfirms that have imported less human capital.We highlight a few implications of thisstudy below.

Figure 2 Average variation and retention intensity for multi-unit banks in the foreign exchange trade industry,1975-1993

Figure 3 Average amount of experience a trader carries into a firm,multi-unit banks, foreign exchange industry, 1974-1993

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Human capital inflow: a way to breakfrom tradition?In some regions, such as Silicon Valley,human capital mobility among firms isrampant. Increasing competitive andeconomic pressures also drive firms tocontinuously change routines and capabilitiesin order to maintain their competitivepositions. But balancing variation andknowledge retention and understanding howthese activities affect human capital mobilityis difficult. Our findings suggest that firmsthat rely more on their past may benefit fromengaging in continuous inflows of humancapital. New personnel may introducesufficient diversity, either to a firmsknowledge stock or a firms social context, tohelp these firms avoid competence traps. Theidea of bringing in new personnel to introducediversity, or shake things up, is not new. Theresults suggest however, that a one-timeinflow event is insufficient to attenuate thepotential adverse effects of a firms retentionintensity. A continuous inflow of humancapital is necessary over time.

Experience with change may inhibithuman capital inflowWe were somewhat surprised to find thathuman capital inflows decrease when a firmincreases its change activity. Oneinterpretation of this finding is that firms maydelay importing additional human capitaluntil they have digested changes they haveadopted in the past. This suggests thatexperience with novel change, or similarexploration-type behavior, may inhibit futureinflows of tacit knowledge and skills. Recentexperience with change may therefore limitfuture opportunities for learning. Moreover,the negative effect between variation intensityand human capital inflow is likely to be morepronounced for firms that have experiencedhigh amounts of human capital inflow thanfor firms with low amounts.

The dynamics of human capital inflowThe findings show a self-reinforcing cycle ofhuman capital inflow. This may have positiveor negative implications for a firm. On thepositive side, the cycle provides a continuousflow of human capital to a firm and in turn,creates opportunities for knowledgedevelopment and combinative learning. Onthe negative side, the self-reinforcing cyclecould generate a path of random drift rather

than a path of development that satisfies orexceeds environmental needs. Moreover, athreshold may exist beyond which the numberof new personnel entering a firm is moredisruptive than productive. Increasing inflowsmay not necessarily stimulate usefulknowledge development. Firms shouldtherefore be sensitive to the path dependenceassociated with inflows of human capital.Developing organizational systems thatenable a firm to simultaneously leverage newemployees knowledge and integrate newemployees into a firm with the least amount ofdisruption might be one way to address thisissue.In conclusion, this study shows that time

matters for research investigating knowledgeflows. Examining flows at a cross-section intime tells us little about their dynamicrelationships and their responses to otherfactors related to knowledge production. Infact, it is difficult to explore knowledgedevelopment or learning at all without somesense of time. Prior research shows thatexamining mobility across different spatialboundaries also matters (Madsen et al.,2001). Combined these findings suggest thatfuture research investigating knowledge flowsshould control for time and space dimensions.

Note

1 When a tie exists for a name, first initial and secondinitial, such as two Browns, A.T. (referenced asBrown, A.T.1 and Brown, A.T.2), we examine thelocation and parent bank affiliation for the tradersover time. A match between time t and time t + 1 isdefined, for example, for Brown, A.T.1, when thetrader is located in the same country and affiliatedwith the same parent bank at time t and time t + 1.Duplicate names are identified within years, trackedacross years and assigned a unique identifier basedon the above logic. Duplicate names account forless than 2.3 per cent of the total trader population.

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