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Tsikriktsis: The Effect of Operational Performance and Focus on Profitability508 Manufacturing & Service Operations Management 9(4), pp. 506517, 2007 INFORMS

research in a wholesale distribution service setting.

They found that productivity (measured by average

monthly sales documents over number of employ-

ees, average monthly sale line items over numberof employees, and yearly dollar sales per warehouse

square footage) was linked to financial performance

(measured as an adjusted profit after tax percentage).

A common thread between the two studies is that

they investigated the impact of productivity on prof-

itability without examining the potential role of qual-

ity. According to Schefczyk (1993), productivity alone

does not reflect overall performance. Specifically, pro-

ductivity does not consider the operational elements

that matter to the customer such as the flight being

on time, luggage not being lost or mishandled, etc.

One of the key frameworks in the area of ser-vice management which links (among others) quality,

productivity, and financial performance is the ser-

vice profit chain (Heskett et al. 1997). It synthesizes

research from various disciplines (such as human re-

source management, services marketing, and services

operations) and posits that certain human resource

practices lead to capable and satisfied employees

who, as a result, achieve higher productivity and

quality of service. This combination of quality and

productivity ultimately results in superior financial

performance (Loveman 1998, Heskett et al. 1994).

Finally, the service profit-chain framework is related

to the resource-based view, according to which the

resources and capabilities of an organization serve

as a foundation for sustained competitive advantage

(Barney 1991, 1995; Wright et al. 1994).

Important exceptions to the previous studies that

focused on either quality or productivity include

Roth and Jackson (1995) and Anderson et al. (1997).

Roth and Jackson (1995) empirically tested the

operational capabilities-service quality-performance

(C-SQ-P) framework in the banking industry using

exclusively perceptual measures. Anderson et al.

(1997) examined whether the relationship between

customer satisfaction, productivity, and profitability

was different between goods and services. Produc-

tivity was operationalized as sales per employee and

profitability was measured by return on investment

(ROI). Anderson et al. (1997) found that a trade-

off between customer satisfaction and productivity

was more likely when (a) customer satisfaction was

more dependent on customization as opposed to stan-

dardization, and (b) when it was costly to provide

high levels of both customization and standardiza-

tion simultaneously. Their analysis also showed thatfor manufacturing goods, only productivity enhanced

profitability, whereas for services both customer satis-

faction and productivity enhanced profitability.

Our study differs from the previous studies in sev-

eral ways. First, it differs from the studies that exam-

ined in isolation either quality (e.g., Nelson et al. 1992,

Fornell 1992, Anderson et al. 1994, Rust et al. 1995,Loveman 1998, Zhao et al. 2004, Voss et al. 2005) or

productivity (e.g., Schefczyk 1993, Smith and Reece

1999). It also differs from the study by Roth and

Jackson (1995) because they used perceptual mea-

sures of productivity, quality, and market perfor-mance, whereas we use exclusively objective data that

reduce (but by no means eliminate because there is

always a possibility of random noise in the data)

the threat of common method bias. Also, our study

differs from the Anderson et al. (1997) study in the

way we operationalize productivity. Their productiv-

ity measure is marketing oriented (sales productivity)

while our measures are operational because they cap-

ture capacity utilization. Finally, a key difference of

our study is that unlike the previous studies men-

tioned above, ours is based on longitudinal data. One

of the major advantages of a longitudinal study isthat it enables us to incorporate time lags between

variables and to move a step closer toward under-

standing cause and effect in empirical operations

management research.

According to DAveni (1989), improved utilization

of resources is necessary for increased profitability.

Hammesfahr et al. (1993) found that capacity affects

firm profitability while Banker et al. (1993) concludedthat capacity utilization is associated with changes in

overall profitability. Baltagi et al. (1998) found that

excess capacity is a fundamental reason for losses in

the U.S. airline industry. Based on these studies andalso the studies discussed in the previous paragraphs,

we posit:

Hypothesis 1A. Higher capacity utilization leads to

increased profitability in the U.S. airline industry.

There is a lot of evidence on the impact of quality

on profitability in services. Bad quality leads to dis-

satisfaction and dissatisfied customers tend to defect


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Tsikriktsis: The Effect of Operational Performance and Focus on ProfitabilityManufacturing & Service Operations Management 9(4), pp. 506517, 2007 INFORMS 509

and give bad word of mouth to a company, both of

which have a negative impact on profits (Heskett et al.

1997, Anderson et al. 1997). In addition, one would

expect that strong delivery reliability (flights beingon time) would lead to increased profitability. The

argument is very similar to that for the link between

quality and profitability often argued by quality theo-

rists (Deming 1982, Juran 1988, Garvin 1988). Reliable

deliveries, like good quality, may result in cost reduc-

tion (because there is no need for expediting and extra

labor) while, on the other hand, customers may be

willing to pay more to do business with a company

that has a better delivery record.

Based on these studies and also the studies dis-

cussed in the previous paragraphs that examined the

impact of quality on financial performance, we posit:

Hypothesis 1B. Higher quality leads to increased prof-

itability in the U.S. airline industry.

2.3. The Notion of Focus in Service

Operations Strategy

Skinner (1974) introduced the notion of a focused

factory. He suggested that a factory that focuses

on a narrow product mix for a particular market

niche would outperform a plant, which attempts to

achieve a broader mission. Heskett (1986), Swamidass

(1991), and more recently Roth and Menor (2003)have discussed the benefits of focus in the service

management literature. According to Heskett et al.

(1997), companies with operating focus (in the service

delivery system) achieve high profitability.

The notion of focus has received limited empiri-

cal testing in services. Huete and Roth (1988) showed

that focused banks (defined as those with a smaller

span, i.e., fewer delivery channels) had less man-

agerial complexity. More recently, Boyer et al. (2002)

examined the role of focus through the case study of

Sothebys. Our research attempts to extend this prior

empirical work; we investigate the role of focus on

financial performance, which had not been tested by

the previous two studies.

As described in 2.1, carriers in the U.S. airline

industry can be broken down to focused airlines

(such as Southwest, America West, and Alaska air-

lines) and full-service airlines (such as Continental,

Delta, and United). Focused airlines are known to

fly Boeing 737s from point to point in North Amer-

ica only and to have higher levels of coordinationand teamwork exemplified by fast turnaround times

(Gittell 2003). On the other hand, full-service air-lines operate several hubs and have many differenttypes of planes within their fleet (Lapr and Scudder

2004). Based on the arguments put forward by Skin-

ner (1974), Heskett et al. (1997), and Roth and Menor(2003), we posit:

Hypothesis 2. Focused airlines are more profitablethan full-service airlines.

3. Research Methods

3.1. Sample

We use data from the U.S. domestic airline industry toinvestigate the relationship between operational per-formance and profitability. Specifically, our study is

based on longitudinal data concerning the 10 major

airlines (Alaska Airlines, America West, American

Airlines, Continental, Delta, Northwest, Southwest,TWA, United, and USAir). The U.S. Department of

Transportation classifies an airline as major if the air-

line has at least 1% of total U.S. domestic passengerrevenues. The only other major airlines operating in

part of 19881998 ceased operations well before 1998:

Eastern in 1990 and Pan Am in 1991. Combined, the

major airlines account for more than 93% of revenuepassenger miles for all U.S. airlines. (One revenue

passenger mile is transporting one passenger over onemile in revenue service.)

Starting in September 1987, the U.S. Department

of Transportation introduced quarterly quality data

reports. Consequently, all major airlines were requiredto collect and report data among others on on-time

performance and lost baggage. Besides these objective

indicators of quality, the data also include objectivemeasures of capacity utilization and financial perfor-

mance. The data cover the period from the fourth

quarter of 1987 through the second quarter of 1998(43 quarters), resulting in a sample of 430 observa-

tions (i.e., there are no missing data for any of the

variables).Investigating business performance in terms of

both financial and operational performance with ob-

jective data from secondary sources is especially

appropriate for single industry studies (Venkatramanand Ramanujam 1986). In addition, a single industry


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study enables researchers to obtain a deeper under-

standing of an industry and its processes and prac-

tices, and allows for a direct comparison between

firms because the determinants of superior per-formance can be precisely identified (Garvin 1988).

Thus, our data seem appropriate for studying our

research questions.

3.2. Measures

We use two different measures of capacity utiliza-

tion (see Table 1 for definitions). The traditional mea-

sure of capacity utilization in the industry is in terms

of passengers (CU_Passengers), which is similar to

passengers over available seats but also controls for

differences in flight length (see Table 1 for a more

detailed explanation). Measuring capacity utilizationin the airline industry is a complex problem. A car-

rier can have very high capacity utilization in terms

of passengers, but its fleet may spend a lot more time

on the ground (compared to being in the air) than the

fleet of another carrier. Therefore, we add a new mea-

sure: capacity utilization in terms of fleet (CU_Fleet).

We use both measures to capture capacity utilization

in the airline industry.

We use twoqualityindicators in our analysis. Specif-

ically, we use lost baggage as a measure of confor-

mance quality (Garvin 1988). We also use late arrivals

Table 1 Description of Measures and Airline Terminology

Late arrivals: A flight is counted as on-time if it operated less than 15

minutes after the scheduled time shown in the carriers computerized

reservation systems. Cancelled and diverted flights are counted as late.

Lost or mishandled baggage: The rate of mishandled baggage reports per

1,000 passengers. The rate is based on the total number of reports each

carrier receives from passengers concerning lost, damaged, delayed, or

pilfered baggage.

Available seat miles (ASM): The aircraft miles flown in each interairport

hop multiplied by the number of seats available on that hop for revenue

passenger use.

Revenue passenger mile (RPM): One revenue passenger transported one

mile in revenue service. Revenue passenger miles are computed by

summation of the products of the revenue aircraft miles flown on each

interairport hop multiplied by the number of revenue passengers carried

on that hop.

Capacity utilization for passengers (CU_Passengers): RPM/ASM. It is also

known as load factor.

Capacity utilization for fleet (CU_Fleet): Airborne hours/(Airborne hours+

on-ground hours).

Operating profit over operating revenue (OPOR): Operating

profit/Operating revenues.

as a measure of on-time performance. On a theoret-

ical basis, late arrivals have a dual meaning. In the

field of service operations strategy, on-time perfor-

mance is considered to be an indicator of deliveryreliability (Fitzsimmons and Fitzsimmons 2000) while

from a service quality standpoint, late arrivals could

be thought of as an internal measure of service qual-

ity, similar to lost baggage.

Traditional measures offinancial performanceinclude

ROI, return on sales (ROS), and return on assets

(ROA). In this study, we cannot measure ROI and

ROA because airlines only report their systemwide

balance sheets (including both domestic and interna-

tional operations), while service quality data are only

reported for domestic operations. However, airlines

report separate income statements for domestic and

international operations. Therefore, we can measure

return on sales (ROS). One of the key methodological

considerations in using financial data from secondary

sources is to assess differences in accounting

policies (Venkatraman and Ramanujam 1986). We

use operating profit as opposed to net profit because

it is not confounded by differences in accounting

practices concerning owning versus leasing airplanes,

interest on loans, etc. Hence, we operationalize prof-

itability as operating profit over operating revenue

(OPOR). Given that our operationalization gives apercentage rather than an actual amount, OPOR is

a measure of relative rather than absolute profitabil-

ity. Hence, when we use the term profitability in the

remaining of the paper, we mean relativeprofitability.

We use two types of control variables in our study.

Dummy variables for each airline control for differ-

ences among the 10 carriers not captured by the other

variables. For example, differences in pricing (price

level, yield management techniques, etc.), which are

expected to affect profitability, are not captured by our

variables. Airline dummy multiplied by calendar time

variables control for the fact that over time airlines

may change policies/characteristics not accounted for

by the other variables.1

1 For the sample, which includes all carriers, we use nine dum-

mies for the 10 carriers and 10 dummies operationalized as airline

dummy calendar time, where time ranges from 1 (fourth quarter

of 1987) to 43 (second quarter of 1998) for each airline.


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Figure 1 Empirical Model Relating Operational Performance to

Profitability in the U.S. Domestic Airline Industry

Quality

Late arrivals

Lost baggage

Productivity

Profitability(OPOR)

Airline typeFocused vs. full service

Airline

Airline*Time

Control variables

Primary variables

C.U. passengers

C.U. fleet

3.3. Model Estimation

Figure 1 shows the model that links operational per-

formance to profitability in the U.S. domestic airline

industry. The unit of analysis is a carriers domestic

operating unit.

In addition to testing the model shown in Figure 1,

we perform several analyses to assess the robustness

of our findings. First, to get a deeper understanding

of which operational performance measures have an

impact on profitability, we split our original data (all

10 carriers) into two subgroups: the seven full-service

airlines and the three focused airlines. We rerun our

analysis for the two subgroups. Finally, all analyses

described above will also be conducted by lagging the

independent variables up to four quarters.

Given the structure of our data (time-series cross

section), we will use the time-series cross section

regression (TSCSREG) procedure in SAS (SAS/ETS

1993). In this procedure, we use a method developed

by Parks (1967). Parks method allows for a first-

order autoregressive error structure with contempora-

neous correlation between cross sections. Specifically,

the random errors uit , i=

1 N , t=

1 T havethe structure:

uit = iui t1 +it (autocorrelation)

Eitjt =ij (contemporaneous correlation)

E2it= ii (heteroscedasticity)

whereNis the number of cross sections and T is the

length of the time series for each cross section.

Overall, Parks method is appropriate for time-

series cross section data because it allows for

autocorrelation, contemporaneous correlation, and

heteroscedasticity (SAS/ETS 1993). In addition,Parkss method has been used in previous studies

analyzing time-series airline data (Tsikriktsis and

Heineke 2004). Autocorrelation is to be expected

because we have time-series data. Contemporaneous

correlation between companies may be expected

because of potential relationships between firms

(alliances, common facilities, etc). Heteroscedasticity

can be expected because observations for airlines

operating at different scales could have different

variances.

4. Empirical ResultsAppendices A and B show descriptive statistics for

all measures. The average profitability (OPOR) for the

industry is 3.27% but, as shown in Figure 2, the indus-

try has suffered losses for many quarters. Toward the

end of our study period, though, it is doing bet-

ter, with an average OPOR of about 10%. The im-

provement in profitability is also witnessed by the

positive correlation between time and OPOR (see

Appendix B). We now turn to the analysis.

Column 1 in Table 2 shows the results of the econo-

metric analysis. Overall, the model explains 41.6% ofthe variation in profitability. The results provide sev-

eral interesting insights with regard to the impact of

the independent variables on profitability. They show

that both capacity utilization measures are related to

profitability. Interestingly, only one of the two quality

measures (late arrivals) has an impact on profitability.

By conducting a t-test we found that the focused

airlines were significantly better than the full-service

airlines at the 0.01 level in terms profitability. Hence,

Hypothesis 2 is supported. We also found that the

two groups differed in all measures of utilization

and quality, which supports the logic of analyzing

each subgroup separately (full-service versus focused

airlines).

Column 2 of Table 2 shows the results for each of

the groups. The model explains 45.7% of profitability

compared to 41.6% for the entire sample. The results

for full-service airlines are quite similar to those for

the entire industry with one exception: late arrivals


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Table 2 Results of Regression Analysis

Model 1 Model 2

Uns ta nd ard ized Uns ta nd ard ized

coefficients T-statistic coefficients T-statistic

American 437 138 415 132

Alaska 691 188 6299 067

America West 506 099 4975 052

Continental 515 140 583 158

Delta 153 045 098 029

Northwest 507 133 530 141

Southwest 536 145 6268 066

United 352 111 456 141

USAir 127 037 149 044

American time 015 182 020 216

Alaska time 025 179 019 122

America West time 013 084 025 150

Continental time 012 122 010 103

Delta time 006 055 004 038

Northwest time 037 337 035 333

Southwest time 001 021 003 041

TWA time 011 077 014 101

United time 003 043 005 065

USAir time 001 008 003 028

CU_Passengers 063 856

CU_Fleet 447 429

Lost baggage 021 176

Late arrivals 010 086

Focused CU_Passengers 062 567

Focused CU_Fleet 394 254

Focused lost baggage 025 052

Focused late arrivals 028 328

Full service CU_Passengers 061 720

Full service CU_Fleet 519 383

Full service lost baggage 038 139

Full service late arrivals 001 010

R2

0416 0

457

R2 0041

Sample size 430 430

Notes. Dependent variable: profitability (OPOR).Signifies significance at 0.10 in a two-tail test, at 0.05, at 0.01.

have no impact on profitability for full-service air-

lines.

The results for focused airlines can be summarized

in two key points. First, similar to full-service airlines,

both capacity utilization measures have an impact on

profitability. Second, unlike full-service airlines, late

arrivals have a significant impact on the dependent

variable for focused airlines.

An advantage of having time-series data is the

opportunity to test for potential lagged effects. All

analyses described above were also conducted by lag-

ging the independent variables up to four quarters.

Although the relationships were found to be in the

same direction, their statistical significance was lower

than the one obtained by conducting the analysis at

time t for all measures. Moreover, the models that

used lagged variables had lower explanatory power

compared to the models shown here.

5. DiscussionAs noted in the literature review, profitability stud-

ies in services have typically focused on the impact

of either productivity or quality. Our empirical find-

ings show that both can have explanatory power.

Consequently, neither driver should be ignored a pri-

ori. In fact, we found that a companys operating

model can play an important role in this relationship.

In the U.S. domestic airline industry, there are two

distinct operating models: full-service airlines and

focused airlines. In these two operating models, dif-

ferent dimensions of operational performance driveprofitability. It would be erroneous to conclude for

the entire industry that either productivity or quality

had no impact on profitability. It may be misleading

to lump all firms in a single industry analysis if firms

have different operating models.

To illustrate the importance of operating models,

consider our findings for late arrivals. Late arrivals

affect profitability for focused airlines, whereas they

do not affect profitability for full-service carriers (see

Model 2 in Table 2). This finding can be explained by

the zone of tolerance argument used in the service

quality literature (Parasuraman et al. 1990). Accord-

ing to this argument, the zone of tolerance is much

tighter for the service quality dimension that is most

critical to company success. In our case, companies

that have competitive strength on timeliness seem to

have a very narrow zone of tolerance for lateness, and

that is reflected in their financial performance. Specif-

ically, Figure 3 shows that focused airlines have a bet-

ter on-time performance record than the rest of the

industry. This is certainly true for the first 28 quarters

of our data. Recently, focused airlines have had more

late arrivals andfor some quarterseven more thanthe rest of the industry. As shown in Figure 2, in

the same period (the last 15 quarters) the profitabil-

ity gap between focused airlines and the rest of the

industry has narrowed. Figures 2 and 3 combined

with the results in Table 2 indicate that airlines that

have traditionally been the best on-time performers

are penalized financially for being late whereas the

others are not.


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Figure 2 Profitability (Operating Profit Over Operating Revenue) in the U.S. Domestic Airline Industry: Focused Airlines (DOM_3) vs. Full-Service

Airlines (INT_7)

20

15

10

5

0

5

10

15

20

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Percent

DOM_3

INTL_7

Quarter

Figure 3 Average Late Arrivals: Focused (DOM_3) vs. Full-Service Airlines (INT_7)

0

5

10

15

20

25

30

35

40

DOM_3

INTL_7

Percent

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Quarter

Based on a series of case studies, Gittell (2003)

concluded that one of the key benefits of focused

airlines is faster turnaround at the gate. Besides

the obvious reasons for this (focused carriers flyone type of plane and have no or limited food on

board), there are other organizational and human-

related factors. Specifically, Gittell found that cross-

trained employees and better coordination among

divisions also helped to quickly turn the airplane.

For example, at Southwest each flight has its own

onsite operating agent who is in charge of com-

munication and coordination across various depart-

ments/functions, while at American Airlines several

flights share the same operations agent who is actu-

ally located offsite. Full-service airlines have tried to

compete with the focused airlines but fail mainlydue to organizational differences. For example, full-

service airlines have to face strong unions that inhibit

cross-training and command that employees only

perform work that is strictly defined in their job

specifications.

One could attempt to explain these differences be-

tween the two operating models through the theoret-

ical lens of the service profit chain (Heskett et al.


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Figure 4 (a) Fleet Capacity Utilization (CU_Fleet): Focused (DOM_3) vs. Full-Service Airlines (INT_7); (b) Fleet Capacity Utilization (CU_Fleet) for

Full-Service Airlines: Maximum, Mean, and Minimum Values

44.5

45.0

45.5

46.0

46.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Quarter

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Quarter

Percent

43

44

45

46

47

Percent

DOM_3

INTL_7

(a)

(b)

1997). Specifically, the service profit chain proposesthat human resource management practices designedto both support and enable employees result in capa-

ble and satisfied employees. Consequently, increased

productivity, higher levels of customer service, andbetter financial performance are dependent upon thecontribution of employees of the organization (Hes-

kett et al. 1994). It should come as no surprise, then,that Southwest is consistently voted as one of the bestemployers in the United States (despite the fact thatits employees are paid less than the industry average)

and the company has the best record of on-time per-formance and profitability in the United States for thelast 20 years.


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Our empirical results also have implications for

managers. Specifically, by looking at the coefficient

of capacity utilization for passengers (mean 63.41%,

standard deviation 5.51%), we see that 1% increasein CU_Passengers would result in 0.63 percentage

points increase in OPOR. Also, 1% increase in fleet

capacity utilization (mean 45.25%, standard devia-

tion 0.42%) would result in 4.47 percentage points

increase in OPOR. Given that the average OPOR is

3.27% (standard deviation 8.10%), one can appreci-

ate the magnitude of potential benefits for airlines.

The impact of fleet capacity utilization on profitabil-

ity is even more significant for the seven full-service

airlines. Specifically, 1% increase in CU_Fleet (mean

45.12%, standard deviation 0.36%) would result in

an increase of 5.19 percentage points in profitability.Given that the average OPOR for these carriers is

2.28% (standard deviation 7.77%), this would actually

mean an increase of more than 200% in their prof-

itability. As shown in Figure 4, the full-service carriers

have reached their limit with regard to CU_Fleet, at

a level of 45%46%. Although the range between the

best and worst performing carrier at any given point

in time is less than two percentage points, surpris-

ingly enough even such a small difference has a huge

impact on profitability.

Overall, the managerial implications of our find-ings are twofold. First, they show managers where (on

which operational measures) to improve. Second, they

provide guidelines on how to quantify the benefits

of those improvements, which in turn enables man-

agers to conduct a cost/benefit analysis of potential

improvement programs.

6. ConclusionsOur analysis shows that operational performance

has a significant impact on profitability. When we

look at the industry as a whole, both productiv-

ity and quality affect profitability. Interestingly, the

relationship between operational performance and

profitability is contingent on a companys operating

model. Focused airlines show a link between late

arrivals and profitability, while full-service airlines do

not. Also, capacity utilization is a stronger driver of

profitability for full-service airlines than for focused

airlines.

Overall, when we look at the entire industry,

we find support for both Hypothesis 1A (the link

between capacity utilization and profitability) and

Hypothesis 1B (the link between quality and prof-itability). However, when we analyze the two groups

separately, we only find partial support for Hypoth-

esis 1B (it is only supported for focused airlines and

not for full-service ones).

We also found that focused airlines outperform the

rest of the industry in terms of profitability, which

confirms Hypothesis 2 and provides empirical sup-

port to the proposition put forward by Skinner (1974)

three decades ago. Finally, our research found empir-

ical support for the zone of tolerance argument

(Parasuraman et al. 1990). Companies with superior

performance on on-time delivery (carriers with onlydomestic routes)cannot tolerate lateness as much as the

other carriers, and this is reflected in their profitability.

The contribution of our study is twofold. First, it

contributes to the operations strategy literature. It is

the first study to empirically investigate and demon-

strate the link between focus (Skinner 1974) and

profitability in services, and it also shows that the rela-

tionship between operational performance and prof-

itability is contingent upon a companys operating

model. The second contribution of our study to the

field of empirical operations management research

is that it uses objective, longitudinal data to exam-

ine how both productivity and quality affect prof-

itability in a service industry. Empirical operations

management (OM) research could benefit from more

longitudinal studies, which would enable us to test

rigorously OM theories and to move closer toward

causality (Flynn et al. 1990).

Finally, the study is subject to a few limitations.

First, we are missing information that could have

helped us understand in more depth the drivers of

financial performance in the airline industry. Specif-

ically, regarding the first hypothesis (the impact of

operational performance on profitability), one would

expect variables such as ticket price and fuel cost to

affect operational and financial performance. Also, the

type of airport (hub versus nonhub, etc.) could play

an important role (Sarkis 2000). The impact of these

factors could be addressed in future studies.

Second, regarding our investigation of the impact

of focus on profitability, one could also consider


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the concept of fit (defined as the degree to which a

firms operational elements match its business strat-

egy) (Venkatraman 1989). The concept of fit (Skinner

1969) has also received very limited empirical inves-tigation in service operations (Smith and Reece 1999

provide an interesting discussion of the effect of fit on

service performance).

Third, future studies could investigate the relation-

ship between quality and productivity in services sim-

ilar to the work of Krishnan et al. (2000), who based

on a study of new product development in the soft-

ware industry suggest a conceptual model linking

quality and productivity.

Finally, we realize that our findings were obtained

in a single industry. Schmenner (1986) classified air-

lines as service factories because (a) they offer astandardized service (limited customization), (b) there

is relatively low interaction with the customer, and

(c) they are more equipment intensive as opposed

to labor intensive. This implies that one has to be

careful when attempting to generalize these find-

ings, especially to professional services, which have

opposite characteristics. Future research should revisit

our study in service settings that allow for more

customization. However, single industry studies are

highly beneficial under certain circumstances (Heskett

1990) and this is particularly true in the airline

industry, where it is extremely important to address

context-specific issues such as capacity utilization of

fleet versus that of passengers. We hope that answers

to these questions will help firms to better manage

service operational performance.

Appendix A. Descriptive Statistics

Full-servic e F ocused

All airlines airlines airlines

(N= 430) (N= 301) (N= 129)

Variable Mean St. dev. Mean St. dev. Mean St. dev.

Profitability

Operating 327 8.10 228 7.77 559 8.43

profit/revenue

Quality

Late a rrivals 2049 5.67 2143 5.07 1827 6.37

Lost baggage 591 1.83 618 1.80 528 1.75

Productivity

CU_Passengers 6341 5.51 6391 5.13 6227 6.18

CU_Fleet 4525 0.42 4512 0.36 4556 0.37

Appendix B1. Correlation Matrix (All Airlines)

Late Lost CU CU

Time arrivals baggage passe ngers fleet

OPOR 0.311 0094 0369 0517 0167

Time 0184 0491 0619 0197

Late arrivals 0401 0045 0075


CU_passengers 0096

Notes.Bold numbers are significant at the 0.01 level (two-tailed). Italic num-

bers are significant at the 0.05 level (two-tailed).

Appendix B2. Correlation Matrix (Full Service Airlines)

Late Lost CU CU

Time arrivals baggage passengers fleet

OPOR 0.332 0011 0290 0573 0112

Time 0104 0579 0623 0171

Late arrivals 0415 0008 0013


CU_Passengers 0147

Notes. Bold numbers are significant at the 0.01 level (two-tailed). Italic num-


Appendix B3. Correlation Matrix (Focused Airlines)

Late Lost CU CU

Time arrivals baggage passengers fleet

OPOR 0.286 0114 0458 0532 0041

Time 0355 0324 0635 0348

Late arrivals 0274 0038 0139Lost baggage 0451 0090

CU_Passengers 0260

Notes. Bold numbers are significant at the 0.01 level (two-tailed). Italic num-


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Documents

CRM and airlines in the US