DARR-ARGOTE-EPPLE-1995-An Investigation of Partner Similarity Dimensions on Knowledge Transfer

7/27/2019 DARR-ARGOTE-EPPLE-1995-An Investigation of Partner Similarity Dimensions on Knowledge Transfer

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T h e Acquisition, Transfer, a n d Depreciationo f Knowledge i n S e r v i c e Organizations:

Productivity i n FranchisesEric D. Darr * Linda Argote * Dennis EppleThe Anderson GraduateSchool of Management, UCLA,405 Hilgard Avenue, LosAngeles, California 90024-1481GraduateSchool of IndustrialAdministration, CarnegieMellon University, Schenley Park,Pittsburgh, Pennsylvania 15213

GraduateSchool of IndustrialAdministration, CarnegieMellon University, Schenley Park,Pittsburgh, Pennsylvania 15213

The paper examines the acquisition, depreciation and transferof knowledge acquired throughlearning by doing in service organizations. The analysis is based on weekly data collectedover a one and a half year period from 36 pizza stores located in Southwestern Pennsylvania.The 36 stores, which are franchised from the same corporation, are owned by 10 different

franchisees. We find evidence of learning-in these service organizations: as the organizationsgain experience in production, the unit cost of production declines significantly. Knowledgeacquired through learning by doing is found to depreciate rapidly in these organizations.Knowledge is found to transfer across stores owned by the same franchisee but not across storesowned by different franchisees. Theoretical and practicalimplications of the work are discussed.(Organizational Learning;LearningCurves; Productivity; Knowledge Transfer)

1. IntroductionAs organizations produce more of a product, the unitcost of production typically decreases at a decreasingrate. This phenomenon or close variants of it is referredto as a learning curve, a progress curve, an experiencecurve, or learning by doing. "Learning curves" havebeen found in many organizations, including thoseproducing aircraft, ships, trucks, and refined petroleumproducts (Argote and Epple 1990). Reviews of the lit-erature on organizational learning curves can be foundin Argote (1993), Dutton and Thomas (1984), and Yelle(1979). Most previous research on learning curves hasfocused on manufacturing organizations. The currentstudy extends this work by examining learning in serviceorganizations. More specifically, we examine produc-tivity gains in 36 pizza stores owned by 10 franchiseesin Southwestern Pennsylvania.The current study also extends previous work on or-ganizational learning by analyzing the transfer of

learning in service organizations. By transfer, we meanwhether organizations learn from the experience ofother organizations. Levitt and March(1988) and Huber(1991) have suggested that organizations learn fromthe experience of others as well as from their own directexperience. We empiricallyexamine whether knowledgetransfers across organizations by analyzing whetherstores benefit from production experience at other stores.The study of learning transfer has important implica-tions for firms planning for the start-up of multiple fa-cilities, for competitive strategy, for antitrust policy(Spence 1981), for trade policy (Gruenspecht 1988),for the success of joint ventures (Kogut 1988), and forexplaining interfirm and international differences inrates of learning (Mody 1989).A further contribution of the current study is its anal-ysis of the depreciation of organizational knowledge.The classic learning curve formulation (e.g., see Yelle1979) assumes that learning is cumulative and that it

0025-1909/95/411 1/1750$01.25Copyright ?) 1995, Institute for Operations Research1750 MANAGEMENT SCIENCE/VO1.41, No. 11, November 1995 and the Management Sciences

INTER ORGANNISATIONNELEL DANS CET ARTICLEinter orga


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persists through time. More recent work suggests, how-ever, that knowledge acquired through learning bydoing may depreciate (Argote et al. 1990). The currentstudy empirically estimates whether depreciation occursin these service organizations.Information about whether knowledge depreciateshas important implications for forecasting productionquality, costs and rates. Failure to allow for depreciationof learningmay result in forecasts with large errors(e.g.,see Argote et al. 1990 analysis of Lockheed's productionof the L-1011 as reported in the Wall Street Journal,1980-1981). Additionally, the extent of knowledge de-preciation has implications for developing human re-sourcepolicies concerning personnel retention in generaland downsizing in particular. Information about de-preciation also has implications for competitive strategy(Argote et al. 1990) and for explaining interfirm differ-ences in rates of productivity gains (Argote and Epple1990).In the sections that follow, we review empirical evi-dence on the transfer and depreciation of organizationallearning. Two examples of knowledge transfer that oc-curred in the organizations we studied are discussed.We also review empirical evidence on the effectivenessof various transfer mechanisms to develop our researchhypotheses.

1.1. Empirical Evidence on Transfer of LearningSeveral researchers have empirically examined thetransfer of organizational learning. Their collective re-sults indicate that knowledge transfer is selective.Zimmerman (1982) examined the transfer of con-struction knowledge relating to 10 nuclear reactorsbuiltover a ten-year period. He analyzed the effects of firmexperience and industry experience on the unit cost ofconstruction. Results indicated that transfer of learningoccurred: the industry experience variable accountedfor a significant portion of variance in plant cost. Thus,learning accrued to individual firms as a result of in-dustry-wide experience. Firm-specificexperience, how-ever, was more significant than industry experience.Alternatively, Joskow and Rose (1985) found no ev-idence of industry experience transfer for 411 coal-burning steam-electric generating units built between1960 and 1980. The researchers analyzed the effects of

firm-specific experience, architect/ engineer experience,and industry experience on the unit cost of construction.Results indicated that only firm-specific and architect/engineer experience accounted for significant portionsof variance in the unit cost of construction.Argote et al. (1990) found that shipyards which beganproduction later were more productive initially thanshipyards with earlier start dates. Once shipyards beganproduction, however, they did not benefit from pro-duction experience at other yards.

Epple et al. (1991) and Epple et al. (in press) analyzedtransfer across shifts within two manufacturing facilities.The researchers found evidence of transfer at both sites.There were differences across sites, however, in the ex-tent to which knowledge acquired on the first shifttransferred to the second.Evidence of experience transfer has also been foundfor angioplasty surgery success rates at different hos-pitals (Kelsey et al. 1984). The researchers used calendartime as a proxy variable for technical progress in theenvironment. The researchers found that success ratesimproved with calendar time for only the first 20 or sooperations performed by a surgeon. Thus, transfer oflearning appeared to influence the early but not laterperformance of surgeons.

1.2. Qualitative Evidence on Transfer of Learningin the Production of PizzasTwo events that occurred in the stores we studied il-lustrate that transfer of organizational knowledge is se-lective. The first incident concerned the developmentand transfer of an innovation for placing pepperoni.The usual procedure for placing pepperoni on a pizzais to distribute it evenly over the entire pie. When thisprocedure was used on pan pizzas, which are thick-crusted, it often resulted in finished pizzas with moundsof pepperoni pooled in the center. One solution to thisproblem was to monitor and modify pepperoni place-ment during baking. This was a difficult and time-consuming task.Another solution to the problem was developed thatinvolved a different initial method of placing pepperoni.Rather than distributing the pepperoni equally over theentire pie, pepperoni is placed in spokes around the pie.As the pan pizza cooks and the cheese flows, the

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pepperoni fans out across the pie, resulting in finishedpan pizzas with equally distributed pepperoni.The innovation was discovered at a franchise storein western Pennsylvania. Initially, it transferred only toother stores owned by the same franchisee. The advan-tages of the placement innovation soon became evidentto a visiting franchisee who carried it back to his fran-chise organization. The adopting franchisee was so im-pressed with the consistent results of the placementprocedure, he recommended it at the next quarterlymeeting of all western Pennsylvania franchisees. Theplacement procedure was soon in use at all stores withinthose franchise organizations. A consultant from theparent corporation visited western Pennsylvania atabout this time, and was impressed with the pepperoniplacement procedure. Within a year, the procedure wasin use at 90% of the stores nation-wide. The pepperoniplacement procedure for pan pizzas started as a store-level innovation and eventually produced corporate-wide benefits through extensive transfer of learning.The second incident concerned the layout for theworkflow. The final step in the pizza production processinvolves placing a finished pizza pie in an appropriatelysized box. Traditionally, the phone operator takes theorderinformation (e.g., size and type of pizza, address)and records it on a box label. The labeled boxes are thenarranged vertically on a shelf near the pizza oven. Thevertical arrangement saves space, but it forces the pizzamaker to read each label sideways and also means thatthe pizza maker has to open each box prior to placinga pie inside. While opening a box may not seem like adifficult or time-consuming task, it becomes more dif-ficult while balancing a hot pizza in one hand. Morethan one pizza has been dropped on the floor whilemoving a pizza from the oven and opening a box at thesame time.A better boxing arrangement was discovered that in-volved placing opened boxes horizontally on a largetable near the pizza oven. This arrangement allows apizza maker to read the label in its natural position andto move a finished pizza directly from the oven into thebox. The new boxing arrangement saves time and re-duces waste from dropped pizzas.The boxing innovation was discovered at a franchisestore in western Pennsylvania. It transferred to otherstores owned by the same franchisee but not to stores

owned by different franchisees. Thus, not all innova-tions produce benefits outside the store or franchise oforigin.1.3. Organizational Relationships and TransferMechanismsA potentially important factor that may explain whetherknowledge transfers across organizations is the rela-tionship that exists between the organizations involvedin the transfer. Transfer of knowledge can occur be-tween independent organizations or between subdivi-sions of a single organization. In our sample, storesowned by different franchisees are conceptualized asindependent organizations, whereas stores owned bythe same franchisee are seen as subdivisions of a singleorganization.Tushman and his colleagues theorized that interor-ganizational relationships between independent orga-nizations differ from relationships between organiza-tions owned by the same firm (e.g., Tushman 1977,Tichy et al. 1979). The researchers demonstrated thatthe extent of social networks and commonality of lan-guage were greater between subdivisions of a singleorganization than between independent organizations.Tushman (1977) conceptualized social networks to beregular communication and personal acquaintances.Regular communication and personal acquaintanceshave both been proposed as mechanisms for transferof knowledge. This suggests that the process and rateof knowledge transfer between organizations owned bythe same franchisee and those owned by different fran-chisees may differ.

Two bodies of literature are related to the rate ofknowledge transfer: the adoption/ diffusion of inno-vations literature and the technology transfer literature.Both literatures focus on the role of transfer mecha-nisms, conduits or agents through which transfer ofknowledge takes place, in facilitating the knowledgetransfer process. The transfer mechanisms particularlyrelevant for the present study are regular communica-tion, personal acquaintances, and meetings. In general,the literaturessuggest that high levels of transfer mech-anism use are associated with high levels of technologytransfer.

Regular Communication. Regular communicationrefers to exchanges of information which occur at stan-

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be more use of mechanisms for transferringknowledgeacross stores that belong to the same franchisee.

2. Data and MethodThe conventional form of the learning curve is:y = ax-b where (1)

y = the cost/ unit to produce the xth unit,a = the cost of producing the first unit,x = the cumulative number of units produced, andb = parameter measuring the rate costs are reducedas cumulative output increasesFor estimation purposes, the learning curve can berewritten:

log y = log a - b log x. (2)In the above expression, the cumulative number of unitsproduced is a proxy variable for knowledge acquiredthrough production. If unit costs decrease as a functionof this knowledge (i.e., the coefficient of cumulativeoutput is statistically significant), other things heldconstant, organizational learning is said to occur.The learning curve format provides a method forevaluating organizational learning and its transfer (Ar-gote and Epple 1990). Store-specific learning, intra-franchise transfer of learning and inter-franchise trans-fer of learning are of interest in this research. Store-specific knowledge may be measured by cumulatingstore pizza production through time. Franchisee knowl-edge may be measured by aggregating the cumulativeoutput across all stores owned by a common franchisee.Interfranchise knowledge may then be measured byaggregating the cumulative output across all franchiseesin our sample.2.1. Source of DataData for this research were collected from the entire setof stores in southwestern Pennsylvania that are fran-chised from one of the largest pizza corporations. Thesample centered around the Pittsburgh area, and in-cluded 10 different franchisees who owned a total of36 stores. The largest franchisee owned 11 stores,whereas five of the franchisees were single store owners.The oldest franchise organization had been in businessfor 11 years, and the youngest for just 3 months. The

average age of the franchise organizations was 3.75years.The corporation's regional office provided data con-cerning pizzas sold and production costs for each storeby week from January 1, 1989 through June 15, 1990.Structuredinterviews with the franchisees provided in-formation on the frequency that phone calls, meetings,and personal acquaintances were used to transfer in-formation.The data are from a very desirable situation. The in-puts (i.e., the raw materials) are homogeneous. There-fore, input characteristicsare controlled for naturally inthe sample. Differences in technology across pizza storesare very small. Product mix and economies of scale willbe controlled for in the analyses.2.2. Analysis PlanThe symbols used throughout the paper and the vari-ables they represent are listed below.t-calendar time in weeks,

J,1-number of stores in franchise n,qniit-pizzas produced by franchisee n in store i inweek t,C,1it-costs (food and labor) for store i in franchiseen in week t,Qlit= s=Oq,1is-cumulative number of pizzas pro-duced by store i in franchisee n through week t,FQ,1t {=1Q,,it-cumulative number of pizzas pro-duced by franchisee n through week t,IQt = E 1?=1Q,,t-cumulative number of pizzas pro-duced in all stores in all franchisees through week t,p,it-percentage of pan pizzas produced by franchiseen in store i in week t, andsni-dummy variables for each store.The variableQis a proxy for store-specificknowledge.

The variable FQ is a proxy for franchisee knowledgeand IQ is a proxy for interfranchisee knowledge. Thedummy variables capture variance associated with storespecifics such as management style, age, and location.Several models are estimated in which the unit costof production depends on store-specific experience,franchisee experience, interfranchisee experience, andother variables. The most basic model we estimated us-ing least-squares regression (Column 1 in Table 1) was:

Log(c,1jt/q,jt = bo+ bi Log Q,1jt_j b2 Log FQ,1t-l+ b3 Log IQt-l + b,,is,,i+ U,1it. (3)

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We allow for serial correlation of the error term in allequations we estimate.In Equation (3), if b1 is significant, store-specificlearning has occurred. If b2 is significant, transfer oflearning between stores owned by a common franchiseehas occurred. If b3 is significant, transfer of learningbetween stores owned by different franchisees has oc-curred.In these analyses, the unit of time is a week. Thevariables Q, FQ and IQ are the cumulative pizza pro-duction through the end of the previous week. Thelagged cumulative output is used on the right-hand sideof Equation (3) because cumulative output serves as aproxy for experience acquired as a result of past output.Alternative explanations of our findings are investi-gated by estimating models with additional variables torepresent possible technological change associated withthe passage of time and economies of scale. The nextmodel we estimated (Column 2 in Table 1) was:

Log( c,it/qnit ) = bo+ bi Log Qnit-1 + b2Log FQnt1+ b3Log IQt-l + b4t + b5qnit+ b6q it+ bniSni Unit (4)

We next estimated models with variablesrepresentingchanges in the rate of learning and product mix (per-centage pan pizzas). The third model we estimated(Column 3 in Table 1) was:Log(Cnit/qnit = bo+ b,Log Qnit-1 + b2Log FQnt-1

+ b3 Log IQt-l + b4t + b5qnit+ b6qnit2 + b7[Log(Qnit1)]2

+ b8pnit bniSni unit- (5)We also investigate whether knowledge persiststhrough time or whether it depreciates by replacing cu-mulative output with the following knowledge variable

Table 1 EstimatedCoefficients or Models PredictingUnit Costa(1) (2) (3) (4) (5)

Store-specificLearning b1) -0.117t -0.098t -0.097t -0.104t -0.106t(0.019) (0.020) (0.020) (0.019) (0.022)Transferbetween commonlyowned -0.104t -0.066t -0.064t -0.059t -0.094*

stores (b2) (0.016) (0.019) (0.020) (0.022) (0.047)Transferbetweendifferently wned -0.015* -0.008 -0.009 -0.004 -0.001

stores (b3) (0.007) (0.010) (0.010) (0.010) (0.011)CalendarTime (b4) 0.003t 0.003t 0.004t 0.002t

(0.001) (0.001) (0.002) (0.0008)Current izzaCount b5) -0.0003t -0.0003t -0.0003t -0.0004t

(0.1E-04) (0.1E-04) (0.1E-04) (0.9E-05)Squareof Current izzaCount b6) 0.6E-07t 0.5E-07t 0.5E-07t 0.9E-07t

(0.4E-08) (0.4E-08) (0.4E-08) (0.4E-08)Squareof Store-SpecificLearning b7) 0.009 0.009 0.003

(0.007) (0.008) (0.009)PercentagePan Pizza (b8) 0.021 0.022 0.052(0.017) (0.021) (0.048)Depreciation f Knowledge X) 0.80t 0.83t

(0.046) (0.042)Autocorrelation oefficient 0.569t 0.5814 0.589t 0.512t 0.492t

(0.017) (0.014) (0.015) (0.022) (0.024)R2 0.237 0.557 0.565 0.593 0.653

a Standard rrorsare shown in parentheses.p < 0.05, andtP < 0.01, and tp < 0.001.

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in the next models (Columns 4 and 5 in Table 1):Knit = XKnit-1 + qnit (6)

Equation 6 allows for the possibility that knowledgedepreciates over time by the inclusion of the parameterX.If X= 1, the accumulated stock of knowledge is simplyequal to lagged cumulative output, the conventionalmeasure of learning, and there is no evidence of de-preciation. If X < 1, there is evidence of depreciation:recent output is a more important predictor of currentproductivity than past output. An iterative search al-gorithm was used to estimate the parameter X and allother parameters to minimize the sum of squared re-siduals.

The fourth model we estimated (Column 4 in Table1) was:

Log(cnit / qnit) = bo+ b, Log Knit-1 + b2Log FKnt-1+ b3 Log IKt-, + b4t + b5qnit+ b6q it + b7[Log(Knit-)]2+ b8pnit + bniSni+ Unit (7)

where FK and IKare defined analogously to FQ and IQexcept that K replaces Q in the summations. Thus,knowledge that transfers is allowed to depreciate.The most complex model we estimated included un-known production histories in the knowledge variablesK, FK and IK. The fifth model we estimated (Column5 in Table 1) was:Log(Cnit/ qnit) = bo+ b, Log(vni+ Zni+ Knit-i)

+ b2Log(vn + Zn+ FKnt-1) + b3Log(v + z + IKt-1)+ b4t + b5qnit+ b6qnit2+ b7[Log(vni + Zni+ Knit-1)]2+ b8pnit+ bniSni+ Unit, (8)

where for store i in franchise n, Vni and Zni re respec-tively the known and unknown production historiesprior to the first observation in our sample. Vn and Znare obtained by aggregating the preceding variablesacross all stores in a franchise, and v and z are aggregatesof these variables across all stores in the sample. Whenproduction history is known, the unknown productionhistory Zni 0. Similarly, when production history isunknown, the known production history Vni 0 .

Figure1 RelationBetweenTotalCostperPizzaand CumulativeNumberof Pizzas Produced

A

AS AAA AA AA A jh ALAL A

A A AA AA

Cumulative izzasProducedNote:Thesedataare roma single tore ora period f 11years.

3. ResultsA learning curve plotted from a single store is shownin Figure 1. This figure shows the characteristic learningcurve pattern:the unit cost of producing pizza decreasedat a decreasing rate as the cumulative number of pizzasproduced increased.3.1. Store-specific Learning and Transfer of

Learning EffectsResults concerning the effects of store-specific learning,transfer between commonly owned stores and transferbetween differently owned stores on cost per unit' arepresented in Table 1. Results of estimating Equation (3)using a maximum-likelihood estimation algorithm al-lowing for first-order autocorrelation of the residualsare presented in column 1 of Table 1.2 Column 1 shows1The constant term and the coefficients of the store-specific dummyvariables are not of particularinterest so are not reported to preservethe confidentiality of the data. A joint test of the null hypothesis thatthere are no store-specific effects is rejected at a high significance level(p < 0.001), so important store-specific effects appear to be presentin the sample. A regression with just the store-specific dummy vari-ables explained roughly half the variance (0.328) explained by ourfull model. Store-specific dummy variables are included in all analyses.2 The presence of the autocorrelation coefficient, p, makes the modelnonlinear in the parameters. Other parameters that we add below

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the effect of the conventional measure of store-specificlearning, lagged cumulative output for each store, oncost per unit. As can be seen from the table, the variablerepresenting store-specific learning has a significantnegative coefficient, supporting our hypothesis that theunit cost of production would decrease as the cumulativenumber of pizzas produced at each store increased.

Learning curves are often characterized in terms of aprogress ratio, p. The progress ratio, p, is related to thecoefficient for store-specific learning, b1, as follows:p = 2b. (9)

While there is considerable variance in progress ratiosfound in different studies, the modal progress ratio formanufacturing firmsis approximately 80% (Dutton andThomas 1984). Thus, for each doubling of cumulativeoutput a 20% reduction in unit cost is realized.

Based on the results shown in column 1 of Table 1,a progress ratio for the entire sample was calculated tobe p = 0.929. For each doubling of cumulative output,the unit cost of producing a pizza decreased approxi-mately 7%. Thus, pizza stores in the sample demon-strated a much slower learning rate than the modal"80% learning curve" found in manufacturing firms.The effects of transfer between commonly ownedstores and transfer between differently owned stores oncost per unit are also presented in column 1 of Table 1.The negative coefficients b2 and b3 suggest that bothtransfer between commonly owned stores and transferbetween differently owned stores account for significantdecreases in the unit cost of production.

Analysis of the residuals from Equation (3) revealedfirst-order autocorrelation. There was no evidence ofhigher order autocorrelation. All of the models shownin Table 1 correct for first-order autocorrelation byjointly estimating the correlation coefficient with othercoefficients of the models.

Models with more control variables were estimatedto explore alternative explanations for the results. Wedivided the control variables into two separate sets

including the depreciation parameter, X,and the unknown productionhistories, zni, also enter nonlinearly. Therefore, a nonlinear searchalgorithm was used to estimate all the results we report.This algorithmsearches for the parameter values that maximize the likelihood func-tion.

(Equations 2 and 3) in order to better understand theincremental impact of each control variable. In Column2 of Table 1 calendar time is introduced to capture thepossibility that technical change associated with thepassage of time rather than learning associated withorganizational experience was responsible for decreasesin unit production costs. The positive coefficient for thetime variable in Column 2 of Table 1 indicates that timeis not a viable alternative explanation for the decreaseobserved in unit production cost. Rather the coefficienton the time variable indicates that the cost of pizza pro-duction increased with the passage of time, perhapsreflecting increases in food and labor costs over the oneand a half year period of study.

Current pizza count and the square of current pizzacount are also included in Column 2 to capture the pos-sible effects of economies of scale on cost per unit. Thenegative coefficient for current pizza count and the pos-itive coefficient for the square of current pizza count inColumn 2 of Table 1 indicate that significantscale effectsare present. Cost per unit first decreased and then in-creased with increases in the current volume of pro-duction.

The decrease in cost per unit as volume rises fromrelatively low output levels is quite natural since somelabor and operating costs must be borne merely to keepa store open, and those costs are spread over more unitsas volume increases. Increasing cost per unit at highervolumes results from increased coordination costs. Co-ordination becomes difficult for high-volume produc-tion, especially since less experienced part-time em-ployees are used to supplement regular employees dur-ing peak loads.

A comparison between Column 1 and Column 2 ofTable 1 reveals that the impact of transfer between dif-ferently owned stores is no longer significant, whereasthe effects of store-specific learning and transfer be-tween commonly owned stores on the unit cost of pro-duction remain unchanged with the addition of calendartime, current pizza count, and the square of currentcount. This illustrates the importance of controlling forscale economies since other variables may pick up theireffects if scale variables are excluded.We conducted a specification test (Hausman 1978)to assess whether there might be simultaneity in thedetermination of cost per pizza and currentpizza count.



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Our model allows for current pizza count to affect coststhrough economies of scale. It is conceivable that thereis also an effect in the reverse direction if stores withlower cost per pizza charge lower prices and therebygenerate a larger sales volume. Such simultaneity orendogeneity would lead to biased coefficient estimates.To test for the possibility of endogeneity of currentcount, the model was estimated with a two-stage leastsquares procedure that Fair (1970) developed. This in-strumental variables procedure provides consistent es-timates of models with endogenous variables and se-rially correlated errors. The coefficient estimates usinginstrumental variables were compared to the originalcoefficient estimates. The two sets of coefficient esti-mates are very similar. A test statistic of 8.16 was cal-culated for Hausman's specification test. The test statisticis distributed as X 2, df = 44, with a critical value of60.48 at the 0.05 level. Thus, there is no evidence ofendogeneity of current pizza count in the model.

In Column 3 of Table 1 the square of the knowledgevariable was introduced into the model to allow forpossible changes in the learning rate. The coefficient forthis variable was insignificant, indicating that there wasno change in the rate of learning over the length of thestudy.The proportion of total pizza production accountedfor by pan pizza was also introduced into the model atthis stage to control for product mix. The estimate ofpan pizza effects in Column 3 was insignificant, indi-cating that product mix does not affect the unit cost ofproduction. A comparison between Column 2 and Col-umn 3 reveals that the learning, time and scale effectsare unchanged by the inclusion of these additional vari-ables.The possibility that the results were driven by a fewnewly opened stores in the sample was investigated, byremoving the four new stores from the sample. Theresults from the reduced sample were almost identicalto the results shown in Table 1.3.2. Depreciation ResultsResults on the depreciation of learning are presented inColumns 4 and 5 of Table 1. Column 4 is a linear modelthat does not include entire production histories,whereas Column 5 is a nonlinear model including entireproduction histories. The maximum likelihood estimate

of X for the model shown in Column 4 is 0.80. Thehypothesis of no depreciation (X = 1.0) is very stronglyrejected.Learning curve analysis has traditionally proceeded

from the beginning of production in an organization.The majority of stores in this sample were in operationseveral years prior to the beginning of data collection.Through further data collection, we obtained completeproduction histories for 18 of the 36 stores in our sample.The impact of including entire production histories foreach store on estimated learning effects was investigatedusing a nonlinear model in which pizza production priorto the beginning of our sample was added to the store,intrafranchise and interfranchise aggregates. For eachof the eighteen stores in which we were unable to obtaincomplete data, production history was treated as an un-known coefficient.The results of these analyses are shown in Column5 of Table 1. Including complete production historiesdoes not change our results concerning store-specificlearning, time, scale effects, and product mix. The effectof transfer between commonly owned stores remainedsignificant (p < 0.05) but became somewhat less sig-nificant than in the previous analysis. The maximumlikelihood estimate of X, the depreciation parameter,remained significantly less than one.The results indicate a very rapid rate of depreciation.A value of X= 0.83 implies that roughly one half (0.834)of the stock of knowledge at the beginning of a monthwould remain at the end of the month. From a stock ofknowledge available at the beginning of a year, a neg-ligible amount (0.83 52) would remain one year later. Infact, without continuing production to replenish thestock of knowledge, virtually all production knowledgewould be lost by mid-year.

3.3. Simulations of Effects of Learning,Depreciation, and Transfer on Unit CostsThe following calculations provide an indication of the

magnitude of the effects of learning, depreciation,transfer, and scale effects on costs. A typical store hasa rate of production per store on the orderof 1000 pizzasper week. Consider a store producing uniformly at thisrate from the date of opening onward. Based on theresults in Column 5 of Table 1, learning effects would

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lead such a store to have costs at the end of one monthof operation that would be 20% lower than at the endof the first week. At the end of the second month, costswould have fallen an additional 8% below the level atthe end of the first month. At the end of the fourthmonth, costs would have fallen an additional 4%belowthe level at the end of the second month. Beyond thefourth month, loss of knowledge through depreciationwould offset the contributions to knowledge from on-going production. If the store continued at the samerate of production in the future, learning would notcontribute any further cost reductions.Next consider two stores opening at the same dateeach producing 1000 units per week. Suppose one is asingle-store franchise while the other is a member of afive-store franchise, and, for simplicity, suppose allstores in the latter franchise open at the same date andproduce 1,000 pizzas per week. The results in Column5 of Table 1 imply that, at every date, a store in thefive-store franchise would have costs 14% lower thanthe single-store franchise. Similarly, at each date, a storein a ten-store franchise would have costs 20% belowthose of a one-store franchise and 6.5% below those ofa five-store franchise.

To compare intrastore learning to intrafranchiselearning, it is convenient to hold constant static scaleeffects (i.e., effects captured by the current count andsquare of current count variables). Consider a storeproducing 3,000 pizzas per week compared to a fran-chise with three stores each producing 1,000 pizzas perweek. At each date, the store producing 3,000 units perweek would have costs 11% below the costs of a storeproducing 1,000 units per week in a franchise with 3identical stores.The above comparison illustrates the scale economiesassociated with learning in multiple-store franchises.Conventional static scale effects, measured by inclusionof current count and the square of current count, arealso present. These static scale economies are most easilyillustrated by comparisons holding learning effects con-stant. A store operating at a weekly output that mini-mizes average costs (2,222 pizzas per week) would havea 6% cost advantage over a store operating at the av-erage output rate observed in our sample (1,119), a26% cost advantage over a store operating at the small-est weekly rate observed in the sample (140), and a

17% advantage over a store operating at the highestweekly rate observed in our sample (3,429).3

In sum, the results in Table 1 uniformly support thehypothesis that store-specific learning significantly de-creases unit production costs. The results also supportthe hypothesis that transferbetween commonly ownedstores significantly decreases unit production costs. Littleevidence of transfer across stores that are owned bydifferent franchisees was found.

We hypothesized that knowledge transfer betweencommonly owned stores would be greater than transferbetween differently owned stores because of greateruseof transfer mechanisms between -commonly ownedstores. Table 2 presents the results concerning the fre-quency of transfer mechanism use. As can be seen, thefrequencies of phone calls, personal acquaintances, andmeetings were significantly greater between commonlyowned stores than between differently owned stores.

4. DiscussionThe stores in our sample evidenced firm-specific learn-ing: as they gained experience, the unit cost of produc-tion decreased at a decreasing rate. The results on firm-specific learning are robust: firm-specificlearning effectscontributed to reductions in production cost indepen-dent of calendar time, scale effects and product mix.Additionally, store-specific learning was evident whenwe added complete production histories and allowedfor knowledge depreciation.

3 The various comparisons of learning and scale effects presented inthe text are calculated from the following series of relationships usingthe results of Column (5) of Table 1. For a store producing at a constantrate q per week, store-specific knowledge is K, = 0.83. K,_1+ q. Fora single store, franchise knowledge and store knowledge are the same.Holding other things constant, costs for such a store at date t comparedto costs j weeks earlier are given by (K,/K, j)- (KtlKt-j) . Costsat date t for a franchise with ii stores each producing q per periodcompared to costs at that date for a single-store franchise are givenby n10094. Abstracting from static scale economies, costs for a singlestore producing ni q units per period relative to a franchise with niidentical stores each producing q per period are given by nm-0106. b-stracting from learning effects, static scale economies imply that costsfor a store producing at rate q1 per week relative to costs for a storeproducing at rate q2 per week are e[-ooo36(q-q2)+o91x1o7(q-q2)1



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Table 2 TransferMechanismUse WithinFranchisesversus BetweenFranchises

Phone PersonalCallsa Acquaintancesb Meetingsa

Intrafranchiseransfer 2.5 2.6 1.7lnterfranchiseransfer 0.4 0.8 0.0X2 16.57 9.3 13.33p = 0.002 p = 0.05 p =0.004

a Mechanismuse was measuredon a scale from 0 = never to 5 = morethan once a week.I Mechanismuse was measuredon a scale from 0 = none to 5 = morethan 10.

This is one of the first studies to focus on learning inservice organizations. Although the modal progress ratioin the food franchises we studied was less than themodal figure found in manufacturing, learning effectswere significant contributors to the productivity of thestores. Furtherresearch is needed to determine whetherthe slower rate of learning is characteristicof most ser-vice organizations and if so, why the rate is slower thanthat typically observed in manufacturing. To accomplishthis, we believe that it will be more fruitful to movebeyond the diffuse characterization of "service" versus"manufacturing"organizations and focus on the specificvariables that differentiate the two production environ-ments.

Factors that are likely to contribute to the differencesobserved include prevailing characteristicsof individualemployees (e.g., their skill levels and lengths of service),characteristics of the organizations (e.g., opportunitiesfor specialization and standardization), and the natureof the demand function for the product. For example,when we have interviewed managers in manufacturingorganizations about factors responsible for organiza-tional learning, they emphasize matching tasks to theexpertise and interests of individual workers (Argote1993). There was much less opportunity to do this inthe pizza stores we studied than in the manufacturingplants. Thus, an important source of productivity gainswas not available to these stores. Similarly, many man-ufacturing organizations are able to sequence theirproducts in a way that maximizes productivity, while

this option may not be available to service organizationswho produce on demand.We also found that knowledge acquired throughlearning by doing transferred across stores owned by

the same franchisee. Knowledge did not appear totransfer, however, across stores owned by differentfranchisees. The frequency of phone calls, personal ac-quaintances, and meetings was significantly greater be-tween stores owned by the same franchisee than be-tween stores owned by different franchisees. Theseresults on transfer of learning extend our current un-derstanding of the conditions under which transfer oc-curs. They indicate that knowledge transfer betweenaffiliated organizations is greater than transfer betweenindependent organizations.

It is interesting to compare the transfer results in thispaper with previous transfer of learning results (Argoteet al. 1990, Epple et al. 1991). The previous investi-gations found that intraplant transfer was greater thantransfer between geographically separated productionfacilities. Intuitively, groups within a single plant areless independent than geographically separated groups.The previous results concerning transfer of learning are,therefore, consistent with the results presented here.Results indicate that rapid depreciation of knowledgeoccurs within the pizza stores sampled. This is not sur-prising given that the typical turnover rate of employeesin these stores is approximately 300% per year. Man-agerial turnover is approximately 50% per year. A greatdeal of production experience may be lost through suchrapid personnel turnover. Store-specific learning andtransfer from stores owned by the same franchisee con-tributed significantly to unit cost reductions, however,despite the rapid knowledge depreciation.Several events that occurred in the food franchisesafter we completed data collection provide some vali-dation for our findings. Three of the stores in the samplechanged owners following completion of data collectionfor the study. These stores maintained only minimaloperation or closed completely for a short time. Con-sistent with our results on depreciation, unit costs wereconsiderably higher when the stores reopened than unitcosts had been when the stores closed.

Of special import is the fact that the three stores thatclosed or changed owners were all single-store fran-

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chises. These stores were not able to benefit from pro-duction experience at other stores. Consistent with ourresults, they were less productive than theircounterpartsin multiple store franchises.

Future research is needed to understand more fullythe conditions under which knowledge transfers and todetermine the effectiveness of these and other transfermechanisms. Does the effectiveness of a particularmechanism vary as a function of the type of knowledgebeing transferred? More generally, a greater under-standing of the micro processes underlying the transferof knowledge is needed.Research is also needed to determine the conditionsunder which knowledge depreciates and variables af-fecting the rate of depreciation. Bailey (1989) performedan interesting laboratory study that analyzed certainfactors affecting individual forgetting. Bailey (1989)raised the interesting issue as to whether the rate ofindividual forgetting is constant across a spectrum oftasks.Our research has focused on organizational "forget-ting" or depreciation rather than on individual forget-ting. Comparing the estimated rate of depreciation oforganizational knowledge across several studies sug-gests that the rate is indeed not constant across pro-duction environments. The fastest rate of depreciationhas been found in the current study's analysis of fastfood franchises. The next most rapid rate of depreciationwas found in the study of shipbuilding (Argote et al.1990). Our studies of automotive production have re-vealed the slowest rate of depreciation (Epple et al. 1991,Epple et al. in press). While further research is neededto explain the variation observed in depreciation rates,the research to date suggests that the technological so-phistication of the production process may be a keyfactor. The fastest rates of depreciation were observedin organizations low in technological sophisticationwhile the slowest rates were observed in technologicallysophisticated production processes. These latter orga-nizations have more opportunity to embed knowledgein the technology through changes in tooling and pro-gramming. and the like. Knowledge embedded in thetechnology may be more resistant to depreciation thanknowledge embedded in individual workers or in otheraspects of the organization. Knowledge embedded in

technology may also be a mechanism through whichknowledge transfers across organizations in an industry(cf. Bahk and Gort 1993).

In summary, our results demonstrate that service or-ganizations also evidence learning: as stores gain ex-perience in production the unit cost of production de-clines significantly. Knowledge acquired through learn-ing by doing in these service organizations depreciatesquite rapidly. We also observe that knowledge transfersacross stores owned by the same franchisee but notacross stores owned by different franchisees. Storesbenefit from production experience acquired in otherstores in the same franchise.44 We gratefully acknowledge the Carnegie Bosch Institute at CarnegieMellon University and its support of the empirical component of thisresearch. We also thank the Decision, Risk and Management Sciencesprogram of the National Science Foundation (Grant Number SES-9009930) for its support of the development of the methods usedhere. Portions of this work were presented at the 1991 meetings ofORSA/TIMS in Anaheim, California, the 1991 conference on CurrentIssues in Productivity at Rutgers University, the 1992 meetings of theAcademy of Management in Las Vegas, Carnegie Mellon University,Cornell University, and Northwestern University. The authors wishto thank participants in these forums and the reviewers for their veryhelpful comments.

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Accepted by RichardM. Burtoni; eceivedMay 15, 1992. This paper has beenowith the authors 7 monithsor 2 revisions.

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