Vol. 52, No. 3, March 2006, pp. 352-366ISSN 0025-19091 EISSN 1526-5501 [ 061520310352

DOi 10.1287/mnsc.l050.0462©2006 INFORMS

Organizational Learning Curves forCustomer Dissatisfaction: Heterogeneity

Across Airlines

Michael A. LapreOwen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203,

michael.lapre@owen.vanderbilt.edu

Nikos TsikriktsisLondon Business School, Regent's Park, London NWl 4SA, United Kingdom,

nikos@london.edu

In the extensive literature on learning curves, scholars have ignored outcome measures of organizationalperformance evaluated by customers. We explore whether customer dissatisfaction follows a learning-curve

pattern. Do organizations learn to reduce customer dissatisfaction? Customer dissatisfaction occurs when cus-tomers' ex ante expectations about a product or service exceed ex post perceptions about the product or service.Because customers can increase expectations over time, customer dissatisfaction may not decline even when theproduct or service improves. Consequently, it is an open question whether customer dissatisfaction follows alearning-curve pattern. Drawing from the literatures on learning curves and organizational learning, we hypoth-esize that customer dissatisfaction follows a U-shaped function of operating experience (Hypothesis 1), thatfocused airlines learn faster to reduce customer dissatisfaction than full-service airlines (Hypothesis 2), and thatorganizational learning curves for customer dissatisfaction are heterogeneous across airlines (Hypothesis 3). Wetest these hypotheses with quarterly data covering 1987 to 1998 on consumer complaints against the ID largestU.S. airlines. We find strong support for Hypothesis 1 and Hypothesis 3. Hypothesis 2 is not supported in thesense that the average focused airline did not learn faster than the average full-service airline. However, we dofind that the best focused airline learns faster than the best full-service airline. We explore this result by extendinga knowledge-based view of managing productivity learning curves in factories to complaint learning curves inairlines.

Key words: learning curve; organizational learning; customer complaints; customer dissatisfaction; airlinesHistory: Accepted by John Boudreau, organizational behavior, performance, strategy, and design; received

September 20, 2004. This paper was with the authors 3 months for 2 revisions.

1. IntroductionThe learning-curve phenomenon is u'ell known. Asan organization gains experience, organizational per-formance improves at a decreasing rate. The typicallearning-curve example, often observed in manufac-turing firms, is the "power curve/' which specifiesthat the logarithm of unit cost decreases linearly asa function of the logarithm of cumulative number ofunits produced. Scholars have extensively researchedlearning curves, and managers have often used learn-ing curves for planning purposes (Argote 1999).

Recently, learning-curve scholars have expandedthe set of organizational performance measures toinclude total productivity (Adler 1990), quality (Lapreet al. 2000), timeliness (Argote and Darr 2000), prof-itability (Ingram and Simons 2002), and organiza-tional survival (Baum and Ingram 1998). Argote(1999) identified the need for learning-curve research

to continue to study a wider set of outcome mea-sures. Most learning-curve studies have examinedoutcome measures evaluated inside the organiza-tion. Learning-curve scholars have ignored outcomemeasures of organizational performance evaluated bycustomers. In this paper, we study organizationallearning curves for customer dissatisfaction, specifi-cally, whether or not organizations learn to reducecustomer dissatisfaction.

Quality of a product or service is perceived by cus-tomers. Customer dissatisfaction occurs when ex anteexpectations about a product or service exceed ex postperceptions of the product or service (Zeithaml et al.1990). Customer dissatisfaction in turn impedes cus-tomer loyalty and repeat purchase (Heskett et al.1997). Given customers' subjective assessment ofquality, it becomes important to study how cus-tomer dissatisfaction evolves. Because customers can

352

Lapre and Tsikriktsis: Organizational Learning Curves for Customer DissatisfactionManagement Science 52(3), pp. 352-366, ©2006 INFORMS 353

increase ex ante expectations, improvements in theproduct or service may not translate into reducedcustomer dissatisfaction. Consequently, it is an openquestion whether customer dissatisfaction follows alearning-curve pattern—decreasing at a decreasingrate. In this paper, we study one outcome of customerdissatisfaction—customer complaints against airlinesfiled with the government.

This paper is organized as follows. In §2, we firstdiscuss how our outcome measure of customer dissat-isfaction differs from traditional learning-curve out-come measures. Next, we develop our hypotheses.In §3, we motivate our learning-curve model. Sec-tion 4 describes our data and methodology. Section 5presents the empirical results. Section 6 concludeswith a discussion of our results, limitations, and ques-tions for future research.

2. Learning to Reduce Complaints2.1. Complaints: A Measure of Customer

DissatisfactionTo appreciate a fundamental difference between cus-tomer dissatisfaction and outcome measures previ-ously employed in learning-curve research, considerthe following scenario. In 1997, a customer flies ona transatlantic flight in coach on a Boeing 767. Shewatches a movie projected on the central screenand is satisfied with the in-flight entertainment pro-vided. One year later, she crosses the Atlantic on aBoeing 777. She is pleasantly surprised to find thather seat in coach is equipped with her own per-sonal TV screen, choice of channels, and video games.In 2000, she flies the same transatlantic route andfinds to her dismay that her plane is a Boeing 767not equipped with personal visual in-flight entertain-ment. The bundle of services changed from 1997 to2000, even though the specific service of transporta-tion did not change. This customer had increased herexpectations about the service. Even though the air-line provided the same transportation service in 2000as in 1997, the customer was dissatisfied because herincreased expectations were not met. While this cus-tomer nnay not file an actual complaint, the takeawayfrom this example is that customer dissatisfactionis a function of the gap between ex ante customerexpectations about a product or service and ex postcustomer perceptions about the product or service{Zeithaml et al. 1990). So, the more perceptions fallshort of expectations, the higher customer dissatisfac-tion will be. As customers can change their expec-tations over time, dissatisfaction can change evenif the product or service remains the same. There-fore, customer dissatisfaction is fundamentally differ-ent from previous learning-curve outcome measuressuch as unit cost, where identical performance by an

organization results in identical, objective outcomes.Even if an organization delivers identical results overtime, customer dissatisfaction may increase as cus-tomers increase their expectations. As a result, itis imperative for organizations to manage the bal-ance of customer ex ante expectations and ex postperceptions.

A learning-curve effect for complaints would cap-ture an organization's ability to better manage the bal-ance of expectations and perceptions over time. In theintroduction, we mentioned that customer dissatisfac-tion is an important impediment to customer loyaltyand repeat purchase. Why is it interesting to studycomplaints as a measure of customer dissatisfaction?All major U.S. airlines that achieved sustainable costreductions achieved lasting complaint reductions first(Lapre and Scudder 2004). Hence, while reducingcomplaints is no guarantee for organizational success,reducing complaints is necessary to achieve lastingcost reduction—which certainly in the airline indus-try is a must. Next, we develop hypotheses regardingthe shape of complaint learning curves, the impact offocus (as opposed to full service) on complaint learn-ing curves, and the variation in complaint learningcurves across airlines. Our hypotheses draw predom-inantly from the literatures on learning curves andorganizational learning.

2.2. HypothesesMarch (1991) has argued that organizations facetrade-offs between exploration and exploitation.Exploration requires investments in discovery, inno-vation, and experimentation to address new demandsor opportunities. Exploitation, on the other hand,requires investments in refinement and efficiency toget better at executing a given set of routines. Short-term gains from exploitation can make explorationseem less attractive. Conversely, exploring new alter-natives leaves an organization with less energy toinvest in exploitation. Hence, organizations face atrade-off between exploration and exploitation.

Organizational learning curves have typically fo-cused on exploitation—practice with an existing setof routines makes perfect. Organizations learn bydoing the same things over and over again. Exploita-tion builds both internal and external organiza-tional capabilities (Ingram and Baum 1997). By accu-mulating operating experience, organizations shouldbecome more efficient—the internal capability. Thereis extensive learning-curve evidence that organi-zations improve efficiency by gaining experience(Argote 1999). Learning by doing is inferred as effi-ciency improves as a function of organizational expe-rience, typically measured by the cumulative numberof units produced. Cumulative volume is a proxyvariable for knowledge acquired through repeated

354Lapre and Tsikriktsis: Or^aiiizatioim! Learning Cun'es for Customer Dissatisfaction

Management Science 52(3), pp. 352-366, ©2006 INFORMS

production. According to Ingram and Baum (1997),operating experience also contributes to the exter-nal capability of an organization. Operating experi-ence provides an organization with an opportunityto learn about customer preferences. What do cus-tomers want? In the context of airlines, each time anairline operates a flight, the airline gains experiencein a variety of activities including checking in passen-gers, turning around an aircraft, handling baggage,boarding passengers, departing on time, and servingpassengers on board. Hence, by repeatedly operatingflights, an airline learns how to reduce customer dis-satisfaction related to all aspects of flying such as mis-handled baggage, denied boarding, late arrivals, andpoor customer service. Exploitation in the short runshould therefore reduce customer dissatisfaction.

Focus on exploitation at the expense of explorationcan hurt organizations in the long run. Focus onexploitation can constrain organizations to "compe-tency traps" (Levitt and March 1988). The set ofroutines perfected by the organization is no longeradequate in a changing environment. Hence, Ingramand Baum (1997) argue that an organization's rate offailure is a U-shaped function of operating experi-ence. Initially, operating experience decreases the rateof failure (short-run gains of exploitation). Yet, in thelong run too much operating experience with a setof outdated routines increases the rate of failure, asoutdated routines are no longer adequate to addressnew demands placed on the organization. Ingramand Baum (1997) and Baum and Ingram (1998) bothfound empirical evidence supporting a U-shaped rela-tionship between the failure rate of hotel chainsand their operating experience. The authors do pointout that they expected efficiency to have continuedto increase with operating experience. Efficiency isan internal capability—fostered by better knowledgeabout internal operations, whereas organizational sur-vival is an external capability—fostered by betterknowledge about customer preferences. As reduc-ing customer dissatisfaction requires acquiring bet-ter knowledge about customer preferences, we expectcustomer dissatisfaction to follow a U-shaped func-tion of operating experience, just like the failure rateof an organization. As we discussed in §2.1, customerscan "raise the bar"^ncrease expectations based onpast experience (Zeithaml et al. 1990). There are twotypes of customer experience that can lead customersto increase their expectations: past experience withthe same organization and past experience with otherorganizations (competitors). Increasing expectationswill increase customer dissatisfaction ceteris paribus.Hence, customer dissatisfaction is a relative measureof performance like organizational survival (Ingramand Baum 1997) or profitability (Ingram and Simons2002).

In the airline industry, the U.S. Department ofTransportation (DOT) released a study in 1993 stat-ing "the dramatic growth of Southwest [Airlines] hasbecome 'the principal driving force' in changes occur-ring in the airline industry" (Hallowell and Heskett1993, p. 25). Contrary to the popular hub-and-spokemodel in the 1980s, Southwest had substantiallygrown its model of offering low fares on short-haul,point-to-point routes without operating any hubs. Yet,none of the major hub-and-spoke carriers abandonedtheir operating model. So, during the timeline of ourstudy (1987-1998), most airlines focused on exploita-tion as opposed to exploration.

HYPOTHESIS 1. Airlines learn from operating experi-ence to reduce customer dissatisfaction in the short run.However, in the long run, customer dissatisfaction followsa U-shaped function of operating experience.

Skinner (1974) introduced the notion of a "focusedfactory." A factory that focuses on a narrow prod-uct mix for a particular market niche will outperforma plant that attempts to achieve a broader mission.Skinner based his claim on a study of approximately50 plants in six industries. Simplicity, repetition,experience, and task homogeneity breed competenceresulting in better customer service and competitiveposition. Benefits of focus are not limited to manufac-turing. In the service management literature, Heskett(1986) introduced the strategic service vision. Thevision comprises the strategic integration of four ele-ments: (i) target market—who are the right customers,(ii) service concept—what are the elements of theservice that produce results for the right customers,(iii) operating strategy—what elements of the strat-egy maximize the difference between perceived valueof the service and cost of the service, and (iv) ser-vice delivery system—how will the service be deliv-ered consistent with the operating strategy. Everytime a customer interacts with a service provider(a "moment of truth"), a customer can judge aspectsof the service offering as well as the way the serviceis delivered. Consequently, every service encounteraffects customer dissatisfaction. According to Heskettet al. (1997), companies achieve high profitability byhaving either market focus (in the target market)or operating focus (in the service delivery system).Moreover, "organizations that achieve both marketand operating focus are nearly unbeatable" (p. 8).

Market focus can result in faster learning. In theU.S. hotel industry, Ingram and Baum (1997) foundthat geographic specialists learned more from theirown operating experience than geographic gener-alists: "Hotel chains that cover many geographicmarkets might be seen as aggregations of differenti-ated specialists that learn individually to succeed inlocal markets, but are unable to successfully transfer

Lapre and Tsikriktsis: Organizational Learning Curzvs for Customer DissatisfactionManagement Science 52(3), pp. 352-366, ©2006 INFORMS 355

knowledge to each other because they face differentmarket demand" (p. 79).

In the U.S. airline industry, focused airlines such asSouthwest achieve both market and operating focus.Focused airlines achieve market focus by cherry pick-ing routes in North America only. Focused airlinesachieve operating focus by flying only a few planetypes. Simplified operations make it easier to achievehigh levels of coordination and teamwork exempli-fied by fast turnaround times—times planes spendat gates between successive flights. Focus virtuallyensures that employees are able to repeat the sameactions over and over again, thus moving morequickly down their learning curves both individuallyand collectively. Through this repetition, employeesbecome entrained to respond to each ofher and to theuncertainties that impact the flying operation. In con-trast, full-service airlines such as American offer bothcontinental and intercontinental services to first-class,business-class, and coach passengers. Full-service air-lines operate several hubs. Hub-and-spoke systemsprovide large domestic coverage and feed passen-gers into intercontinental routes. Many different typesof planes within a fleet, connecting passengers, andlonger flights with higher in-flight service require-ments make for complicated operations (Heskett et al.1997, Gittell 2003, Lapre and Scudder 2004). Compli-cated operations make it much harder to achieve highlevels of coordination and teamwork. As Haunschildand Sullivan (2002) note, complex organizations tendto be more political, more hierarchical, and more com-partmentalized, which may dampen learning fromexperience. The authors found that heterogeneity incauses of errors is better for learning, and that focusedairlines learn more from heterogeneity in causes oferrors. Because focused airlines can achieve higherlevels of coordination and teamwork, focused air-lines should be able fo learn more from customerdissatisfaction caused by errors such as mishandledbaggage, denied boarding, and late arrivals. Con-sequently, focused airlines are likely to learn fasterto reduce customer dissatisfaction than full-serviceairlines.

HYPOTHESIS 2. Focused airlines learn faster to reducecustomer dissatisfaction than full-service airlines.

Prior learning-curve studies have typically as-sumed identical learning rates across firms. Duttonand Thomas (1984) compared over 200 learning-curvestudies to conclude that a learning rate should notbe viewed as a given constant but as a dependentvariable to be influenced by management. A fewstudies in manufacturing settings have demonstratedthat learning rates show considerable variation withinindustries, within firms, and even within plants (Levy1965, Lapre and Van Wassenhove 2001). A notable

exception in learning-curve research in service set-tings is Pisano et al. (2001). The authors study learn-ing curves for surgery times at 16 different hospitalsallowing for a different learning rate for each hospital.The 16 hospitals were the first to implement a newtechnology for minimally invasive cardiac surgery.Hence, learning rates can differ significantly withinthe sanie industry, be it in manufacturing or servicesettings.

The organizational learning literature provides arationale for heterogeneous learning rates. Cohen andLevinthal (1990) define absorptive capacity as "theability to recognize the value of new information,assimilate it, and apply it to commercial ends." Justlike people, organizations have different absorptivecapacities. Consequently, organizations will differ intheir ability to reflect on new experience and takeappropriate actions based on new experience, result-ing in heterogeneous learning rates. There are at leasttwo organizational characteristics that might causeheterogeneity in learning curves across airlines.

First, if organizations are faced with problems thatare inherently cross-functional, differences in cross-functional absorptive capacity can contribute substan-tively to heterogeneity in learning rates (Lapre andVan Wassenhove 2001). Gitfell (2003, p. 20) identi-fies 12 distinct functions in the flight departure pro-cess. Functions such as pilots, flight attendants, gateagents, fuelers, and baggage transfer agents performa complex set of tasks. Yet, communication betweenfunctions differs greatly between airlines. Because theflight departure process is complex, many things cango wrong, including late departures, mishandled bag-gage, and boarding problems. Any of these prob-lems can lead to customer dissatisfaction. Southwestassigns each individual flight its own on-site operat-ing agent to facilitate cross-functional communicationand coordination. At American, on the other hand,operations agents are located off-site. At any pointin time, an operations agent might be involved with15 different flights. As a consequence, communicationacross functional boundaries is impaired and jointproblem solving and learning suffer. A customer ser-vice supervisor at American exemplifies this (Gittell2003, p. 131):

Here you don't communicate. And sometimes youend up not knowing things [...] Everyone says weneed effective communication. But it's a low prior-ity in action. On the gates, I can't tell you the num-ber of times you get the wrong information from ops[...] We call it the creeping delay. The hardest thing atthe gates with off-schedule operations is to get infor-mation. They are leery to say the magnitude of theproblem.

Second, Edmondson (1999) conceptualizes learn-ing as "an ongoing process of reflection and action,

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Management Science 52(3), pp. 352-366, ©2006 INFC^RMS

characterized by asking questions, seeking feedback,experimenting, reflecting on results, and discussingerrors or unexpected outcomes of actions" (p. 353).Discussing errors and learning from errors requireswhat Edmondson (1999) calls psychological safety"a shared belief that the team is safe for interper-sonal risk taking" (p. 354). Psychological safety alsodiffers greatly between airlines. Gittell (2003) foundthat American Airlines' employees tended to hideinformation to avoid blame for a delay. An Americangate agent stated, "Unfortunately, in this companywhen something goes wrong, they need to be ableto pin it on someone. You should hear them fightover whose department gets charged for the delay"(p. 28). Tn the early 1990s, Southwest realized thatfinger pointing and blame avoidance impeded learn-ing (Gittell 2003). Southwest instituted a "team delay"which allowed less precise reporting of the cause ofdelays, with the goal of diffusing blame and encour-aging learning. A station manager at Southwest stated(Gittell 2003, p. 141):

IW]e track the source of delays. Usually it's a situa-tion rather than a person who is at fault. We take adelay when the delay warrants it [...] We try to figureout what caused a delay, but we don't do much fin-ger pointing. We find that the more you point fingers,the more problems go underground rather than gettingsolved.

Because of variation in cross-functional communi-cation and psychological safety across airlines, weexpect variation in learning curves across airlines.

HYPOTHESIS 3. Organizational learning curves for cus-tomer dissatisfaction are heterogeneous across airlines.

3. Learning-Curve ModelThe functional form most commonly used in learning-curve research is the power form. The power formspecifies that the logarithm of unit cost decreases asa linear function of the logarithm of cumulative pro-duction volume. Despite its frequent use, Lapre et al.(2000) note fundamental shortcomings of the powerform. Arguably the biggest shortcoming is that thepower form is an empirical observation lacking anytheoretical underpinnings.

An alternative functional form for the learningcurve is the exponential form, first introduced byLevy (1965) as the "adaptation curve." The adapta-tion curve follows from the assumption that the rateof improvement of a process is proportional to theamount a process can improve. Lapre et al. (2000) pro-vide a theoretical foundation for Levy's assumptiongrounded in the organizational learning literature onperformance gaps. In the context of complaint rates,let £ be a measure for operating experience, CR(E) thecomplaint rate after the organization has accumulated

£ experience, /x the learning rate, and CR* the opti-mal target level for the complaint rate. According toLapre et al. (2000, p. 600):

The performance gap [CR{E) - CR'] induces an orga-nization to search for alternatives to reduce this gap.A larger discrepancy spurs the organization to exertmore effort in searching for better knowledge (e.g.,March and Simon 1958). The effectiveness of acquiringnew knowledge is determined by the learning rate fi.Consequently, we can model the rate of improvementas the product of the learning rate and the perfor-mance gap.

dCR(E)/dE=fi[CR{E) - CR'].

A natural target level for CR' is 0 because we are look-ing at complaints filed with a third party—the gov-ernment. The solution for this differential equation isthe exponential form

or, for estimation purposes

\n(CR(E)) = a + fxE.

Given the theoretical interpretation of the exponentialfornri rooted in the organizational learning literature,we will employ the exponential form in this paper.

Unrelated to the theoretical justification for the ex-ponential form, there is a second benefit of using theexponential form regarding the inclusion or omissionof prior experience. For the power form, accountingfor prior experience is a major concern in learning-curve estimations. If organizations operated before theavailability of data on the outcome measure, omis-sion of prior experience will bias learning-rate esti-mates. Formally, let PEQ represent Prior Experience upto period 0, and let £, be Cumulative Experience fromperiod 1 through period t. Estimating a learning rateusing ln(£,) will yield a different coefficient than usingln(P£,, -I- E,). Loosely put, with the power form, omis-sion of prior experience estimates a different part ofthe learning curve. With the exponential form, on theother hand, estimating a coefficient with E, or PE^ -\- E,will yield the same learning rate. In other words, forthe exponential form, accounting for prior experienceis a nonissue—omission of prior experience will notbias learning-rate estimates. In the next section, wedescribe our data and research methodology.

4. Data and Method4.1. Consumer ComplaintsWe use consumer complaints filed with DOT as ourmeasure for customer dissatisfaction (part of this sec-tion draws on Lapre and Scudder (2004)). From 1987to 1998, passengers could file complaints with DOT

Lapre and Tsikriktsis: Organizational Learning Cunvs for Customer DissatisfactionManagement Science 52(3), pp. 352-366, ©2006 INHORMS 357

in writing, by telephone, or in person. Complaintcategories included flight problems, oversales, reser-vations/ticketing/boarding, fares, refunds, baggage,customer service, smoking, advertising, credit, tours,and other. Several factors led to a surge of complaintsagainst airlines in 1987: In August 1987 complaintswere up by almost 500% over January 1987.

First, in early 1987, airlines' performance as wellas DOT'S consumer phone number and addresswere given widespread publicity, which in turn ledto increased consumer awareness concerning airlinequality and the means to file complaints.

Second, in May 1987, the Secretary of Transporta-tion, Elizabeth Dole, sent a letter to all major airlinesconcerning consumer dissatisfaction with the airlineindustry. She asked airlines to consider several stepsincluding reeducation and training of employees,assessment of resources allocated to various sourcesfor dissatisfaction, such as processing refunds andbaggage claims, and review of complaint trends andprocessing times to resolve complaints.

Third, starting October 1987, DOT required majorairlines to report mishandled baggage, involuntarydenied boarding, and on-time arrival statistics. DOT,subsequently, started to publish these statistics alongwith complaints per 100,000 passengers (the broad-est measure of quality) in DOT Air Travel Con-sumer Reports. In 1999, DOT introduced an e-mailaddress as an additional channel for filing consumercomplaints.

To measure customer dissatisfaction, we use quar-terly data on consumer complaints filed with DOTfrom the fourth quarter of 1987 through the fourthquarter of 1998 for the following reasons:

• Consumer complaints filed with DOT clearlycapture customer dissatisfaction: These customerswere so dissatisfied that they wanted to tell the gov-ernment about their service interactions.

• During the 1987-1998 period, consumers couldonly file complaints with DOT in writing, by tele-phone, or in person. No other channels were addedor deleted during this time frame. Restricting ourdata set to 1987-1998 controls for ease of report-ing complaints. By starting in the fourth quarter of1987 as opposed to the first quarter of 1987, we alsocontrol for increased awareness of filing complaintswith DOT.

• We study consumer complaints filed directlywith DOT. Airlines were not involved in collectingand reporting these complaints. So, our analysis is notconfounded by any changes in reporting patterns byairlines.

Let Complaint Rate,, denote the number of con-sumer complaints per 100,000 passengers against air-line i in quarter t. Figure 1 shows the complaintrate evolutions for the 10 major airlines on individual

scales. Figure 2 shows the same complaint rate evo-lutions on a common scale. Consequently, Figure 1allows us to assess individual learning-curve patterns,while Figure 2 allows us to compare complaint ratesacross airlines.

4.2. Independent VariablesWe control for factors that provide more opportunitiesfor service failures: long flights and connections.Flight Length,, is the average flight length for airline /in quarter t; Connections,; is the percentage of pas-sengers connecting to another flight for airline / inquarter t. Both variables are constructed using dataon travel statistics reported to DOT. The impact offactors such as weather and holidays on complaintreasons can exhibit seasonal trends. We control forany seasonality with dummy variables. We defineQuarter2, as 1, if t is the second quarter of a calendaryear, 0 otherwise. Similarly, we define Quarter3, andQuarter4,.

To measure operating experience, learrung-curvescholars typically use cumulative production volume(Argote 1999). As airlines transport "batches" of pas-sengers on flights, airlines need to master all sortsof operations pertaining to flights including turningaround a plane for its next flight. We use cumula-tive number of flights to measure experience. For-mally, Experience,,. I denotes the cumulative numberof flights operated by airline / from October 1987 untilthe previous quarter {t — 1). To construct this experi-ence variable, we obtained flight data from Form 4 1 ^which airlines must file with DOT. Later in the paper,we assess the robustness of our findings if we use adifferent experience variable.

4.3. Two Subgroups of AirlinesDOT classifies an airline as major if the airline hasat least 1% of total U.S. domestic passenger rev-enues. Our data set includes the 10 major airlines forthe entire 1987-1998 period: Alaska, America West,American, Continental, Delta, Northwest, Southwest,TWA, United, and US Airways. The only other majorairlines operating during the 1987-1998 period ceasedoperations well before 1998: Eastern in 1990 andPan Am in 1991. Combined, the major airlines accountfor more than 93% of revenue passenger miles for allU.S. airlines. (One revenue passenger mile is trans-porting one passenger over one mile in revenueservice.)

Alaska, America West, and Southwest focused onroutes in North America only. We will refer to thesethree airlines as "focused airlines." The other sevenmajor airlines offered both continental and intercon-tinental services. For example, all seven operatedtransatlantic routes. We will refer to these seven air-

358Lapre and Tsikriktsis: Organizational Learning Curves for Customer Dissatisfaction

Management Science !i2(3), pp. 352-366, ©2006 INFORMS

Figure 1 Customer Complaints per 100,000 Passengers—Individuai Scaies

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lines as "full-service airlines." Haunschild and Sulli-van (2002) used variation in types of aircraft to con-struct a measure of specialization. We collected dataon the number of types of aircraft in an airline's fleetfor each airline for each quarter. The summary statis-

tics showed that the median and the mode for thethree focused airlines were 3 or 4. The median andthe mode for the full-service airlines ranged between9 and 14. Furthermore, the variation between the twosubgroups was significant, while the variation within

Lapre and Tsikriklsis: Organizational Learning Cun>es for Customer DissatisfactionManagement Science 52(3), pp. 352-366, ®21K)6 INFORMS 359

Figure 2 Customer Complaints per 100,000 Passengers—Common Scales

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US Airways

- •»•*»*» ***•87.4 88.4 89.4 90.4 91.4 92.4 93.4 94.4 95.4 96.4 974 98.4

Quarter87.4 88.4 89.4 90.4 91 4 92 4 934 94.4 95.4 96.4 974 98.4

Quarter

subgroups was not significant.' These differences intypes of aircraft lend further support to the way we

' Southwest's fleet is often described as consisting of a single planetype. During the 1987-1998 period, Southwest operated Boeing737-100/200, Boeing 737-300/700, and Boeing 737-500. DOT consid-ers these types of Boeing 737 as different and collects traffic statis-

group airlines. Market focus and fleet focus simplifyoperations. Simplified operations make it easier toachieve high levels of coordination and teamwork

tics for these different categories. One dimension on wKich these737 types differ is flying range.

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in turning around aircraft. Fast turnaround timesincrease fleet utilization. Lapre and Scudder (2004)showed that the three focused airlines were able tosignificantly outperform the seven full-service airlinesin fleet utilization.

4.4. MethodOur base model is the exponential form without anyexperience variables. The base model includes con-trol variables for factors that provide more opportu-nities for service failures, seasonal dummy variables,and dummy variables for individual airlines (Dy — 1if / = j , 0 otherwise). As the intercept is a linear com-bination of all ten airline dun:\my variables, we needto drop one airline dummy variable. Without loss ofgenerality, we do not include a dummy variable forSouthwest:

ln (Complaint

FIight Length j + /^jConnections,,

-I- ^3

(1)

In Model (1), /3 , captures how much the averagelogarithm of the complaint rate for airline ; deviatesfrom the average logarithm of Southwest's complaintrate [SQ, holding other variables constant. To test thefirst part of Hypothesis 1 (airlines learn to reducecomplaints), we include Experience,,_i:

ln(Complaint Rate.,)

= (S|, + )Q I Flight Length.J + ^2 Connections,,

(2)n + i3,Experience,,_j -\- u,,.

A negative estimate for ^y would imply that airlinesdid learn to reduce complaints. Note that the inter-pretation of (3(,j changes from Model (1) to Model (2).In Model (2), f,, captures how much the intercept,or starting point, of airline j's learning curve devi-ates from Southwest's intercept. The second partof Hypothesis 1 stated that a learning-curve effectfor customer dissatisfaction is U-shaped. FollowingIngram and Baum (1997), we include the square ofour experience variable (Experience,,,])^:

ln(Complaint Rate,,)

— /3n -I- j8i Flight Length., + jSjConnections,,

-I- /33Quarter2, + jS4Quarter3, -I- /3,Quarter4,

(3)

A negative estimate for jSy and a positive estimatefor f( would support Hypothesis 1—a U-shaped rela-tionship between customer dissatisfaction and oper-ating experience. Models (2) and (3) assume identicallearning rates across all airlines. Next, we intro-duce a dummy variable Full Service, — 1 if / isa full-service airline (American, Continental, Delta,Northwest, TWA, United, or US Airways), 0 oth-erwise. To test Hypothesis 2 (focused service firmsreduce customer dissatisfaction faster), we includeExperience,,. 1 x Full Service^ and (Experience,,_i)' xFull Service,:

ln(CompIaint

= JSQ -I- ^1 Flight Lengthy, + /

-h/3g(Experience,,_j)^-I-/3

X Full Service,-t-y3io

X Full Service, -I- w,,. (4)

Estimates for (3^ and for ^ ^ capture how a U-shapedlearning curve for full-service airlines differs from theU-shaped learning curve for focused airlines. Lastly,in the full model we allow for each airline to notonly have a different intercept, but also a differentU-shaped learning curve (Hypothesis 3):

ln(Con^plaint Rate.,

= (^Q + jQ|Flight j + ;32Connections,,

(Experience.,,, ) ) -f ^3^Experience,,.

,,_^)^ + u,,.

(5)

Significant estimates for (87, and 3 , would sup-port Hypothesis 3 that organizational learning curvesfor customer dissatisfaction are heterogeneous acrossairlines.

For Models (1) through (5), ; - 1,...,A/; t ^ 1,... ,T. We have N — 10 airlines, and T = 45 quar-ters. Hence, we have time-series cross-section (TSCS)data, not to be confused with "panel" data (Beck2001). In panel data, the units are sampled andtypically observed only a few times. Specific unitsare of no interest. The asymptotics are in N. Con-versely, in TSCS data, units are fixed; there is nosampling, and we are interested in specific units("how does Southwest compare with American?").The asymptotics are in T. Consequently, there arethree important considerations for the error term u ,:

Lapre and Tsikriktsis: Orgatiizational Learning Curves for Customer DissatisfactionManagement Science 52(3), pp, 352-366, ©2006 INFORMS 361

heteroscedasticity, contemporaneous correlation, andautocorrelation. To address these three considerations.Parks (1967) developed a generalized least squaresmethod for TSCS data. However, Beck and Katz(1995) showed that the Parks' method produces stan-dard errors that lead to extreme overconfidence evenfor data sets with N = 10 and T = 40—very similarto our data set. Instead, to deal with heteroscedas-ticity and contemporaneous correlation. Beck andKatz (1995) developed a superior estimation proce-dure that uses ordinary least square (OLS) parame-ter estimates, but replaces the OLS standard errorswith panel-corrected standard errors (PCSE). Lastly, ifautocorrelation is a concern {typical in learning-curveresearch) one should use Prais-Winsten estimatorswith panel-corrected standard errors (Beck and Katz1995). Unlike the Parks' method. Beck and Katz (1995)make a case against panel-specific autocorrelation,and recommend a single first-order autoregressiveprocess for the error term: u,, = pWy,_i -|-e,,. Therefore,our estimation approach for Models (1) through (5)is Prais-Winsten regression with panel-corrected stan-dard errors and a single first-order autoregressiveprocess common to all airlines (implemented with theprocedure "xtpcse" in Stata).

5. Empirical ResultsTable 1 shows the regression results for Models (1)through (5). The base model explains 33% of the vari-ation. All nine airline dummy variables are positiveand significant indicating that the average complaintrate for each of these airlines was significantly higherthan Southwest's average complaint rate.

The estimates for Model (2) support the first part ofHypothesis 1—airlines learned from operating expe-rience to reduce customer complaints. Inclusion of asingle experience variable common across all airlines(Experience) explains an additional 3% of the varia-tion. The f statistic for the increase in R is 22.48,which is significant at 0.001. The negative estimate forExperience and the positive estimate for (Experience)^in Model (3) support the second part of Hypothesis 1:Complaints show a U-shaped relationship with expe-rience. The F statistic for the increase in R^ of 18% dueto the inclusion of (Experience)^ is 170.54, which issignificant at 0.001. So, the U-shaped learning curve inModel (3) explains 21% of the variation (compared tothe base model), whereas a single experience variablein Model (2) explains only 3% of the variation. Theseestimates provide strong support for Hypothesis 1.

^We ran additional analyses to test if the U-shaped effect is causedby historical factors associated with the passage of time rather thanoperating experience. First, we introduced dummy variables foreach caleridar year in Model (3). All 95% confidence intervals for

The estimates for Model (4) show that the U-shapedlearning curve for full-service airlines does not differfrom the U-shaped learning curve for focused airlines.Neither Experience x Full Service nor (Experience)^ xEull Service has a significant impact on complaintrate. Consequently, Hypothesis 2 is not supported.

In contrast, estimates for Model (5) show a signif-icant increase in R^ of 20%. The F statistic for theincrease in R~ is 21.20, which is significant at 0.001.Several airlines have significantly different slope pa-rameters for Experience and/or (Experience)' . Hence,Hypothesis 3 is supported—organizational learningcurves for customer dissatisfaction are heterogeneousacross airlines.

Every service encounter with a customer is a mo-ment of truth. Every service encounter offers a firman opportunity to learn about customer expectationsand how to serve customers better. We use cumulativenumber of passengers as another measure of expe-rience to capture "learning by serving customers."Estimating Models (2) through (5) with cumulativepassengers yields similar R^ values for each modelcompared to Table 1. Moreover, Hypothesis 1 andHypothesis 3 are supported, while Hypothesis 2 isrejected. In addition, the increase in R^ going fromModel (2) to Model (3) is larger than going fromModel (1) to Model (2). Hence, none of our conclu-sions would change.

Why was Hypothesis 2 not supported? We can usethe estimates for Model (5) to further explore learn-ing curves for full service versus focused airlines.Figure 3 shows estimated U-shaped learning curvesfor selected airlines. In order to compare differentlearning-curve patterns without making Figure 3 toocrowded, we excluded the following airlines:

• United and US Airways, which like Americanhave similar comparisons with Southwest: a higherintercept (Z , > 0), a slower learning rate (^7, > 0),and a slower "relapse rate" (/3gy < 0).

the 1988-1998 year dummies overlapped, while the estimates forExperience and (Experience)^ did not change. So, the U-shapedlearning curve as a function of experience remained, even if weincluded year fixed effects. Second, instead of year fixed effects,we introduced calendar time in Model (3), Calendar time was notsignificant. Third, we introduced other time-varying measures thatmay affect customer dissatisfaction: load factor and real prices.Load factor, a standard utilization measure in the airline industry,is calculated as revenue passenger miles divided by available seatmiles for each airline, for each quarter, (One revenue passengermile is transporting one passenger over one mile in revenue service;one available seat mile is flying one seat over one mile availablefor revenue service.) To calculate real prices, we expressed aver-age one-way fares for each airline, for each quarter, in 1987 dollarsusing the Airline Composition Cost Index from the Air Transporta-tion Association (Lapre and Scudder 2004), Inclusion of either loadfactor or real prices in Mode! (3) did not change the results—neithervariable was significant. Finally, if we included all three variables(calendar year, load factor, and real prices) in Model (3), none ofthe three variables were significant.

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Management Science 52(3), pp. 352-366, ©2006 INFORMS

Trtle 1 Complaint Rale Learning-Curve Estimates

Constant

Flight length

Connections

Quai1er2

Quarter3

Ouarter4

American

Alasi<a

America West

Continental

D«ita

Northwest

TWA

United

US Airways

Experience

Experience^

Experience x Fuii Service

Experience^ x Fuii Service

PNxT

(1)

-0.155

(0.355)-0 .239"

(0.080)1,034-

(0.499)-0.185'-

(0.062)-0.026(0.074)

-0 .159 "

(0.061)

2 .556-(0.505)1.046"

(0.358)2.022"-

(0.292)

2.596"'(0.398)1.353-

(0.317}

2 .043-(0.426)2 .702-

(0.399)

2.476"'(0.498)1,255-

(0,223)

0.3250.726

450

(2)

-0.588(0.318)

-0.022

(0.072)0.634

(0.479)- 0 . 1 9 1 "

(0.061)

-0.043(0.073)

-0 .160"(0.060)

1.618"'(0.444)

0.266(0,347)1.310-

(0.273)1.692"'

(0.364)1.040"-

(0.293)1.312-

(0.397)

1.753"'(0.385)1,476-'-

(0.448)

1.295"-(0.221)

- 0 . 1 4 6 ' "(0.039)

0.3590.717

450

(3)

-0.156(0.266)0,054

(0.060)0.342

(0.422)- 0 . 1 8 9 -

(0.051)-0.045(0.061)

- 0 . 1 7 1 ' "

(0.050)

1.065"(0.351)

-0.310(0.268)0.912'"

(0.215)1.474'"

(0.297)0.553-

(0,218)1.152"'

(0,320)1.448"-

(0.317)1.107"

(0.360)1 .101 -

(0.164)

- 0 . 6 7 7 " '(0.069)0,062"'

(0,007)

0.5400.620

450

Model

(4)

-0.290(0.286)0.076

(0.059)0.247

(0.420)

- 0 . 1 8 9 ' "(0.050)

-0.045(0.060)

-0 .173"-(0.049)

1.096"(0.378)

-0.241(0,278)0 .990-

(0.218)1.517""

(0.335)0.648'

(0.263)1.223"-

(0.365)1.488'"

(0.355)1.144"

(0,392)

1.233"'(0.223)

- 0 . 8 0 1 -(0.181)0.103'"

(0.031)0.091

(0.162)

-0.039(0.030)

0,5500.613

450

Dummy

1,148-(0.357)0.303

(0.335)0.928'"

(0.246)2.878'"

(0.444)0.474

(0.274)2.452'"

(0.394)2 .790-

(0.478)

1.805"-(0.420)0.857"-

(0.223)

(5)

0.802-(0.347)

-0.130

(0.080)0.024

(0.488)-0 .179- "

(0.050)-0,012(0,062)

- 0 - 1 8 1 -(0.049)

Experience

X Dummy

0.795-"(0.157)

-1.786(0.966)

-0.236(0.461)

-0.312(0.212)0.716'"

(0.136)-0.508'(0.203)

-0.144(0.266)0.461"

(0,146)0,594-"

(0.159)

-1 .105" -(0.131)0.157"-

(0.023)

0.7530.429

450

Experience^

X Dummy

-0 .124- "(0.025)1.787"

(0.661)0,469-

(0.189)0.032

(0,039)- 0 . 1 2 0 -(0.023)0.086-

(0.034)

-0.011(0.076)

- 0 . 0 8 7 -(0.024)

-0 .110" -(0.025)

Note. Dependent variabie ln(Complaint Rate), Panel-corrected standard errors in parentiieses.'Significant at 0.05, "a t 0.01, and ' " a t 0.001.

• Continental and TWA, which have a similarU-shaped learning curve as Southwest except for ahigher intercept ( (,, > 0).

While the learning curves for focused airlinesSouthwest and Alaska outperform the learning curvesfor all full-service airlines, the learning curve forfocused airline America West is in the range of thefull-service airline learning curves. So, in Model (4),the learning curve for the average focused airline did

not outperform the learning curve for the average full-service airline. As Figure 3 shows, there is significantoverlap between the two groups of airlines. In fact,one full-service airline (Delta) even outperforms onefocused airline (America West).

Figure 3 demonstrates that organizations need tomanage two elements of U-shaped complaint learn-ing curves: the learning rate associated with Experi-ence d^j+P-^i in Model (5)) as well as the relapse rate

Lapre and Tsikriktsis: Organizational Learning Curves for Customer DissatisfactionManagement Science 52(3), pp. 352-366, ©2006 INFORMS 363

Figure 3 Selected Estimated U-shaped Learning Curves

Time

Note. The estimated U-shaped learning curves were calculated with the esti-mates from Table 1 and operating experience measured by cumulative num-ber ot tlights. Estimated learning curves are piotted over time as opposedto operating experience, because operating experience by the end of 1998differs by a (actor 10 between the smallest and the largest airiine.

associated with (Experience)^ (^^ + 13^, in Model (5)).Northwest was the only airline with a faster learn-ing rate than Southwest (/37| < 0 for Northwest), butNorthwest was not able to catch up with Southwestbecause of its faster relapse rate ( g, > 0 for North-west). Southwest and Alaska had similar interceptsand learning rates (neither ^^j nor ^y, were statisti-cally significant for Alaska). Yet, Alaska had a fasterrelapse rate than Southwest (jSg > 0 for Alaska),which allowed Southwest to reduce complaint ratesfor a longer period of time.

Figure 3 shows that by 1998 most airlines lostmost of their initial gains in reducing customer com-plaint rates. Most airlines appeared to have focusedon exploitation as opposed to exploration. On theother hand, Southwest had the superior learningcurve. In the 1990s, Southwest successfully exploredexpansion to the East Coast. The airline also success-fully explored extending its no-frills, point-to-pointmodel initially developed for short-haul routes tolong-haul routes. So, it seems that Southwest main-tained more of a balance between exploitation andexploration compared to the other airlines. Note thattechnology-driven innovations meant to improve cus-tomer experiences such as e-tickets, online ticketsales, self-check-in kiosks, and online check-in wereadopted on a large scale after the time period studied.

6. Discussion and ConclusionThe contributions of our study are threefold. First, weextend learning-curve analysis to a measure of cus-tomer dissatisfaction. Second, we provide evidence ofa U-shaped learning curve in the context of customerdissatisfaction. Third, we find heterogeneity in orga-nizational learning curves for custonier dissatisfactionacross airlines. For each contribution, we discuss ourresults, limitations, and questions for future research.

6.1. Extension of Learning-Curve Analysis toCustomer Dissatisfaction

Most learning-curve studies have focused on out-come measures evaluated inside organizations. Mostlearning-curve scholars have ignored organizationalperformance evaluated by customers. We estimatelearning curves for customer dissatisfaction. Becausecustomers can increase expectations over time, onecannot a priori expect a learning-curve pattern forcustomer dissatisfaction. Our study shows that air-lines did exhibit learning curves for complaint rates,suggesting that airlines did learn to reduce customerdissatisfaction, at least in the short run. Just like orga-nizations need to learn proactively about operationsto improve measures of internal capabilities such asefficiency, organizations need to engage in "continu-ous learning about markets" (Day 1994) to improvemeasures of external capabilities such as customerdissatisfaction. Day (1994, p. 115) gives an exampleof the continuous experimentation required to learnabout markets:

American Airlines found that customer perceptions ofon-time arrival performance improved markedly if theplane doors were opened less than 25 seconds aftergate arrival. The key to this insight was their ability tomeasure how quickly they opened the doors ar d theirfollow-up with telephone surveys of the passengers onthe flight. In effect, they took advantage of a series ofnatural experiments.

One limitation of our study is the focus on an out-come measure of customer dissatisfaction withoutaddressing the underlying gap between expectationsand perceptions. Future research is needed to disen-tangle ex ante expectations and ex post perceptions.Which way is most effective to manage customerdissatisfaction learning curves—manage expectations,perceptions, or both?

A related limitation of our study concerns ourtraditional interpretation of customer dissatisfaction:ex post perceptions falling short of ex ante expec-tations about a single transaction with a customer.Recently, marketing scholars have started to explorealternative theories for customer (dis)satisfaction.Fournier and Mick (1999) suggest that transaction-specific assessments of (dis)satisfaction could be in-complete. The authors find that (dis)satisfaction withnew product technologies is a dynamic process, oftenhas a social dimension, and is affected by emotion.Longitudinal dissatisfaction data on individual cus-tomers would enrich our understanding of dissatis-faction learning curves. It should be fruitful, for exam-ple, to study how airlines manage dissatisfaction ofits frequent fliers.

A promising avenue for future research would beto study organizational learning curves for servicefailure and recovery efforts. Although organizations

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Management Science ?i2(3), pp, 332-366, ©2006 INFORMS

should avoid failures when possible, failures containmany informative lessons. However, in most organi-zations "there are few incentives to study failures care-fully" (Day 1994). According to Tax and Brown (1998),the majority of customers are dissatisfied with the waycompanies resolve customer complaints. Because cus-tomer (dis)satisfaction is an important driver of cus-tomer loyalty and profitability, it is imperative forcompanies to not only recover but also learn from ser-vice failures {Tax and Brown 1998).

6.2. U-shaped Learning Curves for CustomerDissatisfaction

We found strong evidence that complaint rates fol-lowed a U-shaped function of experience. Thus, wecontribute to the literature on U-shaped learningcurves documented for organizational survival rates(Ingram and Baum 1997, Baum and Ingram 1998) andprofitability (Ingram and Simons 2002). Firms who areserious about learning from service failures shouldencourage dissatisfied customers to voice their com-plaints, so that firms can actually learn from servicefailures (Tax and Brown 1998). A U-shaped effect forcomplaints received directly by a firm is not nec-essarily undesirable, because customers are at leastincreasingly informing a firm about service failure.It is therefore important to note that our measure ofcustomer dissatisfaction does not concern complaintsfiled with airlines, but complaints filed with a thirdparty—the government. Moreover, DOT uses theseconsumer complaints to rank individual airlines andpublishes these rankings every month.

One implication of the U-shaped function is thateventually complaint rates would continue to getworse. We had to limit the study period to 1987-1998to create a controlled experiment as much as possible.DOT'S introduction of its e-mail address as well asposting Preformatted complaint forms on its websitein 1999 hugely facilitated customers to file complaintswith DOT. There is clearly a need to study longerperiods to research which organizations are able toimprove again and under what conditions.

Future research on organizational learning curvesfor service failure and recovery efforts could furthertease out our U-shaped learning-curve finding. Ina study of bank customers who complained twicewith the same bank within 20 months, Maxham andNetemeyer {2002) found that customers reporting twofailures have higher recovery expectations for thesecond failure than for the first failure. Hence, it ispossible that customers' raising the bar for recoveryexpectations contributes more to the U-shaped effectthan service failure does.

6.3. Heterogeneity in Learning Curves forCustomer Dissatisfaction

We did not find support for Hypothesis 2—the aver-age focused airline did not learn faster than the aver-age full-service airline. However, we did find thatthe best focused airline learned faster than the bestfull-service airline. While there are no guarantees fora focused airline to outperform full-service airlines,focus provides a potential benefit. Heterogeneity inorganizational learning curves across airlines impliesthat it depends on effective management of organiza-tional learning curves to realize the potential benefitof focus. Lapre and Van Wassenhove (2003) arguedthat successful management of productivity learn-ing curves in factories requires careful managementof knowledge creation and transfer. Similarly, in thestrategy field, the knowledge-based view argues thatknowledge is the most critical resource of the firm(Grant 1996). Consequently, heterogeneity in knowl-edge resources across firms is a key determinantof superior performance and sustained competitiveadvantage. We found strong support for heterogene-ity in complaint learning curves {Hypothesis 3). Wealso found superior complaint performance and sus-tained competitive advantage in the area of customerdissatisfaction for Southwest. As Figure 2 shows.Southwest has the best complaint performance amongthe major U.S. airlines: the lowest starting point in1987, the lowest ending point in 1998, and in betweenthe lowest complaint rate for more quarters than allother major airlines combined. Estimates in Table 1confirmed that, for 1987 through 1998, Southwest hasthe superior learning curve. Next, we explore some ofGittell's (2003) observations to extend a knowledge-based view of managing productivity learning curvesin factories to complaint learning curves in airlines.

First, Gitteil (2003) discusses how knowledge shar-ing by employees differs between American andSouthwest. At American, employees showed littleawareness of the overall process. They typicallyexplained their jobs without referring how their jobscontributed to the overall process.

By contrast, interviews with Southwest frontline em-ployees revealed that they understood the overall workprocess—and the links between their own jobs and thejobs performed by their counterparts in other func-tions. When asked to explain what they were doingand why, the answers were typically couched in ref-erence to the overall process. These descriptions bySouthwest employees typically took the form, "Thepilot has to do A, B, and C before he can take off, soI need to get this to him right away." Rather than justknowing what to do. Southwest employees knew why,based on shared knowledge of how the overall processworked. (Gitteil 2003, p. 32).

Lapre and Tsikriktsis: Organizational Learning Curves for Customer DissatisfactionManagement Science 52(3). pp. 352-366. ©2006 INFORMS 365

Just like in manufacturing, the mix of know-why, orcausal understanding, and know-how, or operationalskills is important for sharing knowledge (cf. Lapreand Van Wassenhove 2003).

Second, a critical activity in developing sharedknowledge is joint problem solving. At American,Gittell (2003) observed that employees focused onassigning blame and avoiding blame as opposed toworking together to solve problems. A ramp supervi-sor at American:

If you ask anyone here, what's the last thing you thinkof when there's a problem? I bet your bottoni dollarit's the customer. And these are guys who bust theirbutts every day. But they're thinking, how do 1 keepmy ass out of the sling? (Gittell 2003, p. 37)

In contrast. Southwest employees conduct root-causeanalysis together. A Southwest pilot:

We figure out the cause of the delay [,.,] It is a matterof working together. Figuring out what we can learn.Not finger pointing. (Gittell 2003, p. 37)

So, joint problem solving at Southwest is gearedtoward increasing the shared knowledge base.

Third, solving problems that cross departmentalboundaries can be a tough challenge. In factories,Lapre and Van Wassenhove (2003) found that sea-soned engineers with a rich diverse knowledge baseacross production departments could really acceleratelearning curves. At Southwest, Gittell (2003) observedthe critical role of "boundary sparmer" played bythe operations agent responsible for turning arounda flight. The boundary spanner communicated face toface with each function, thus facilitating joint prob-lem solving across functions. Moreover, boundaryspanners also reduced customer dissatisfaction. Yet,when other airlines heard about Southwest's staffinglevels for the operations agents, their response was"What a waste!" or "That is so inefficient!" (Gittell2003, p. 258). However, Southwest's superior perfor-mance in cost, customer complaints, and profitabilityshows there are clear payoffs to investing in boundaryspanners.

Gittell's (2003) study seems to support a knowl-edge-based view of managing learning curves. Thesame knowledge aspects that are important for man-aging productivity learning curves in factories seemto have contributed to Southwest's superior com-plaint learning curve. The observed differences inlearning rates, relapse rates, and starting points forcomplaint learning curves indicate that competitiveadvantages can accrue from managing complaintlearning curves better than others. Just like productiv-ity learning curves, a complaint learning curve is nota given; instead, it needs to be managed proactively.

A limitation of our study is the lack of longitudinalvariables describing learning processes or knowledge

creation processes. Future research that incorporateslearning or knowledge creation to "go behind thecomplaint learning curve" like Adler and Clark (1991)and Lapre et al. (2000) did for productivity and qual-ity measured inside organizations would be a signif-icant contribution. Future research is also needed toassess the generalizability of our findings obtained ina single industry in a single country. We hope thatresearch along these lines will advance our under-standing of how organizations can better managelearning curves.

AcknowledgmentsThe associate editor, two anonymous reviewers, partici-pants at tfie 2004 INFORMS meeting in Denver, seminarparticipants at Vanderbilt University, and Gary Scudderprovided very helpful comments. Support for this researchwas provided by the Dean's Fund for Faculty Research,Owen Graduate School of Management at VanderbiltUniversity.

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