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Airline failure and distress prediction: a comparison ofquantitative and qualitative models

Sveinn Vidar Gudmundsson 1

Faculty of Economics and Business Administration, University of Maastricht, Postbus 616, Maastricht, 6200 MD,

The Netherlands

Received 1 April 1998; received in revised form 17 November 1998; accepted 29 December 1998

Abstract

In this paper, an exploratory study of failure and distress prediction models is presented based on aqualitative survey among new-entrant airline managers and a quantitative data source containing ®nancialand tra�c data of new-entrant airlines. Using logistic regression, it became evident that qualitative vari-ables are a viable source for model construction. They contribute to a deeper understanding of the un-derlying distress and failure process. A number of signi®cant predictor variables are discussed in the contextof managing new-entrant airlines. Ó 1999 Elsevier Science Ltd. All rights reserved.

Keywords: New-entrant airlines; Airline failures; Failure prediction; Deregulation

1. Introduction

When deregulation of air transportation materialized in the US in 1978, it was believed that thethreat of entry by a new airline with new ideas and/or low cost structure was the foundation ofe�ective competition in a deregulated market. New-entrants started to appear shortly after de-regulation, but it soon became apparent that the new-entrants had problems surviving as the post-deregulation years passed. In fact, by 1990 few ®rst wave 2 new-entrants (note that only scheduledpassenger new entrants are covered in this research) had survived. The survivors as of 1993 wereSouthwest Airlines, Midwest Express, Tower Air and Carnival (America West reemerged fromChapter 11, but is counted as failed in this research) that was merged later into the re-establishedPan Am that failed shortly thereafter.

Transportation Research Part E 35 (1999) 155±182www.elsevier.com/locate/tre

1 Tel.: +31-43-388-3442; fax: +31-43-325-4566; e-mail: [email protected] First wave new-entrants being intrastate, charter, cargo, regional or new airlines that took advantage of deregulation

before 1990, while second wave new-entrants are carriers that started operations thereafter.

1366-5545/99/$ ± see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.

PII: S1366-5545(99)00004-6

There have been numerous explanations of this failure phenomena (see Gudmundsson, 1998b)provided by industry analysts and ex-managers of new-entrant airlines, explanations such as: (i)poor management; (ii) unfavorable regulatory environment (route rights, etc.); (iii) inadequateinfrastructure (slots, etc.); (iv) poor ®nancial foundation; (v) overexpansion; (vi) poor servicestandards; (vii) inadequate distribution systems; (viii) poor marketing strategy; (ix) lack of pro-tection against predatory pricing; (x) brand image con¯ict due to evolutionary change in theproduct or strategy of the new-entrants (the problems of growth, etc.); (xi) conspiracy to un-dermine the new-entrant by incumbents; and (xii) unfavorable economic climate (recession, etc.).Recognizing that there is an element of complexity in these business failures, this research at-tempted to come up with qualitative failure models that would identify variables that are goodpredictors of ®nancial distress and failure but at the same time provide a platform for furtherresearch into the variables' impact on the distress and failure process.

2. Distress and failure prediction

Distress and failure prediction has been of substantial interest to the ®nancial community sincethe beginning of this century although it did not take-o� until the advent of powerful computers inthe late 1960s. Various ®nancial indicators are monitored to see if ®nancial distress and failure canbe predicted. The methodologies applied have been numerous: ratio analysis (Hickman, 1958),univariate ratio analysis (Beaver, 1967), multiple discrimination analysis (Altman, 1968; Deakin,1972; Edminster, 1972; Blum, 1974; Libby, 1975), logit/probit and logistic regression models(Santomero and Vinso, 1977; Ohlson, 1980; Scott, 1981), theoretical gambler's ruin models(Wilcox, 1971) and neural networks (Tam and Kiang, 1992).

Non-®nancial ratios have been used in a number of failure prediction models (Keasey andWatson, 1987, 1988) but are usually pseudo-®nancial or accounting procedure linked. The non-®nancial variables employed have usually not addressed the underlying causes of failure but ratherthe symptoms. Argenti (1976) stated in his work related to causes and symptoms of failure, thatan econometric model containing only ®nancial statement information would not provide accu-rate explanation as to why the companies were likely to succeed or fail. This raises interest inwhether or not failure prediction models could be used on qualitative data that provide moreinformation for theory building on the causes of business failure. Regardless of the shortcomingsof failure prediction models, they have shown high prediction accuracy up to three-years-prior tofailure, a fact that cannot be ignored in spite of lack of underlying theory and causal relationshipof model predictors. 3

Contrary to popular belief there is no scienti®cally well-grounded general theory available onbusiness failure or success. Nevertheless, there is wide conformity in the literature that the maincause of business failures is the management of the ®rm (Argenti, 1976; Slatter, 1984; Clutterbuckand Kernaghan, 1990). As a result, it is possible that the importance placed on various factors bythe management could in fact uncover a measurement device (questionnaire) accurate enough tobe used in a qualitative failure or distress prediction. This would provide more information than

3 This applies to MDA, but not to LRA if coe�cients are signi®cant.

156 S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182

quantitative models about the model predictor's relationships with failure and distress. Thequestionnaire research was also made on the presumption that there is a di�erence between themanagement as cause and other possible causes of failure such as the environment. Given ade-quate repetition of such surveys to standardize the management constructs, it is possible toprovide for a new dimension in failure prediction models if stability in variables or trends areunveiled. This study compares the accuracy of such qualitative models with the more traditionalquantitative models in order to see whether conformity would occur across models from variousdata sources. Such a comparison was accomplished by comparing the individual respondent'sprediction probability score using the qualitative models with the airline's probability score usingquantitative models. Furthermore, it was anticipated that through the comparison of variousmodels the study would allow exploration of the reasons behind misclassi®cation of cases as wellas to identify model predictors' association with distress and non-distress as mentioned earlier.

3. Methodology

This research was based on two basic data sources: (i) a questionnaire survey among seniorairline managers that was divided into two distinctive parts; and (ii) a database of airline databoth of ®nancial and tra�c orientation. In Part I of the questionnaire (see Appendix B, Table 9),respondents were asked to state their degree of agreement or disagreement with statements re-lating to various aspects of organization and management constructs on a ®ve-point scale. In PartII (see Appendix B, Table 10), the respondents were asked to indicate on a scale from 0� noimportance to 10� highly important, the importance placed on various factors: in the past, atpresent and as expected in the future. Furthermore, in Part II of the questionnaire the qualitativevariable set was divided into six groups with varying number of variables, shown in the brackets:environment (6); information (9); operations (14); management (18); marketing (18); and ®nancial(10). A separate model was constructed for each sub-group in the three time intervals past, presentand future. This was to account for the possible changes in management approach as managersmove along the learning-curve and encounter changes in the environment.

The grouping of factors to be assessed in Part II was partially based on empirically developedcategorization by Cowan (1990), while the statements in Part I were in the ®rst instance based onthe concepts of Hall (1992). During the course of the questionnaire development, these statementsunderwent changes and adaptation to the airline industry as such. The factors presented in Part IIwere developed on the basis of intuition, literature research and comments on the pilot ques-tionnaire.

The other data-source besides the questionnaire was ®nancial and tra�c data about new-en-trant airlines compiled from the data program (Form 41) at the US Department of Transpor-tation (DoT). To reduce data errors, the data were inserted directly from the actual ®lings fromindividual airlines at the premises of the DoT.

3.1. Population description

For the purposes of this research, new-entrant airlines were de®ned as jet operating airlinesestablished or expanding operations into interstate or international markets after deregulation of

S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182 157

a domestic market or bilateral route. The population of jet operating new-entrants 4 was dividedinto four population stratas (see Table 1): (i) 11 failed new-entrants, de®ned as an airline that hasfailed totally or ®led for Chapter XI 5; (ii) six recently established carriers, which have been op-erating for less than three years before the survey was conducted; and (iii) 23 established new-entrants, which had been operating for more than three years when the survey was conducted in1993. Furthermore, in order to contrast the airline managers' views it was found appropriate togather the views of 44 industry observers: journalists, academics or consultants associated withaviation in general and/or new-entrant airlines. These requirements led to four di�erent ques-tionnaire formats, designed speci®cally so that responses to all questions for all stratas could bemerged for data analysis. The only exception was that industry analysts did not receive Part I ofthe survey, which was the attitude section.

4 This criteria of jet operating new-entrants was to ensure comparability and to exclude plain feeder and commuter

airlines which operate under di�erent philosophy than new-entrants serving general markets. General markets are

interstate and/or international. The comparability requirement is necessary to exclude ®nancial structures and operating

characteristics of smaller turboprop carriers that are quite di�erent from jet carriers. First of all sector distances are

shorter for turboprop carriers, direct costs are higher, indirect costs are lower due to simpler overhead structure and ties

with trunk carriers are usually in e�ect through feeder agreements or ownership.

Table 1

Survey statistics

Population Population Number of

mailings

E�ective

mailings

Responses by

individuals

Number of

airlines

Responses by

airlines

Established

new-entrant

Every airline meeting

criteria

136 129 a 33 (26%) d 23 16 (70%)

Recent

new-entrant


criteria

30 25 b 8 (32%) f 6 5 (83%)

Failed

new-entrant


criteria h

32 Unknown c 5 (15%) 11 5 (45%)

Specialist All linked with

new-entrants

44 44 15 (34%) e ± ±

242 na 27.9% 40 67.7%

a One airline went bankrupt just before the questionnaire was sent out, unknown to the researcher.b Two airlines went bankrupt during the survey execution period.c Present residence of ex-managers of failed airlines was inferred from International Aerospace Directory. As it is

extremely hard to track these managers, poor results were anticipated.d One response was seriously positive biased and was rejected on the basis of two other responses from the same airline.e One response was incomplete and was rejected.f One airline went bankrupt in 1994.g The 59 e�ective responses were received after taking into account notes (cf. footnote d and e).h As expected it was very di�cult to reach ex-managers of failed airlines, causing major reduction in mailings compared

to the total population of managers with such experience.

5 In this study the following failure de®nition was adopted. An airline is considered failed if it has: (i) ®led for

bankruptcy under Chapter XI (US); (ii) been taken over by another airline due to ®nancial di�culties; (iii) ceased to

operate due to bankruptcy; and (iv) ceased to operate without bankruptcy proceedings.


New-entrant airlines included and the associated managers were derived from two mainsources, Air Transport World's World Airline Report and Flight International's World AerospaceDirectory. The selection of managers was guided by the preset requirement of every subject beinga member of the executive team of the airline. This led to the inclusion of all such managers listedin the named sources for the airlines being observed. As a result, the likelihood of receiving at leastone response from each airline was raised, because each airline received 3±16 mailings (mean� 7).The specialists were, however, selected on the basis of their association with issues relating to new-entrants, detected through articles or a listed specialization in the World Aerospace Directory. Thismethod resulted in a total of 242 mailings to individuals of which 12 were undelivered because ofthe bankruptcy of the carriers in question. Much emphasis was placed on con®dentiality of re-sponses and the reporting of results in the aggregate.

To build the quantitative models, all airlines were included that ®tted the criteria of being a jetoperating US new-entrant airline (see Appendix A) having operated for three years or more and®led Form 41 at the US Department of Transportation. This resulted in a total of 25 airlines ®lingduring the period 1978±1992. Through the Life-cycle Phase-In Algorithm (see Fig. 1) the samplewas boosted to 36, having 18 airlines or periods in each group.

3.2. Response statistics

The questionnaire response rate for all four population stratas by individuals was 25.2% or 59e�ective responses (61 in total) and 65.0% by airlines or 26 out of 40 in total. To gain fairly goodoverall response rate from airlines was actually one of the main objectives of the survey. Of these59 responses 10.2% were from top managers (chairmen, CEOs), 18.6% from marketing managers,15.3% from operations managers, 10.2% from ®nancial managers, 22.0% from other managersand 23.7% from specialists (consultants, journalists and academics). Geographical dispersion ofrespondents was 49% from USA and 51% from the European Union (EU).

There was a signi®cant positive correlation (p < 0.05, or better) between respondents from thesame individual airline for all airlines having two or more respondents, except two that wereslightly negatively correlated. In 55.0% of the cases for Part I, the positive correlation amongrespondents is r > 0.5; in 82.0% of cases it is r > 0.3. For Part II, the correlation for all valid cases(correlation could not be calculated if there were missing values) is r > 0.3 and for 38.0% of casesit is r > 0.5. The overall agreement of respondents from the same entity can therefore be judged tobe satisfactory.

Fig. 1. Life-cycle Phase-In Algorithm.


Table 2

Quantitative ratios and variables

n� 36 Mean SE SD

Financial

ADV_FIXE ADV/®xed assets a 0.33 0.10 0.62

ADV_WORK ADV/working capital ÿ1.92 2.44 14.64

CURRENT_ Current assets/total assets 0.25 0.03 0.15

CURRENT1 Current assets/total revenue 0.19 0.01 0.08

DEPRECI1 Depreciation/ADV 0.0038 0.00045 0.0027

EBIT_SAL EBIT/sales ÿ0.03 0.02 0.13

REVEX Revex 0.98 0.02 0.13

NET_INCO Net income/net worth 0.11 0.27 1.61

NET_WORT Net worth/total debt 0.26 0.10 0.58

LABOUR_C Labour cost/ADV 0.74 0.50 3.00

LONG_TER Long-term debt/total assets 0.50 0.06 0.34

TOTAL_DE Total debt/total assets 0.98 0.10 0.61

TOT._REV Total revenue 148,043 12,756 76,535

ADDED_VA Added value a 81,992,434 23,674,390 14,204,634

SCHED._R Total revenue scheduled ops. 95.81 11.74 70.46

Mixed

ASK_$100 ASK/$1000 operating exp. b 17,775 1430 8579

ASK_$101 ASK/$1000 wages 107,000 11,944 71,667

DEPRECIA Depreciation/ASK 0.10 0.18 1.09

DIRECT_O Direct op. Exp./ASK 0.0285 0.00396 0.0238

FUEL_ASK Fuel/ASK 0.0129 0.00190 0.0114

INDIRECT Indirect op. Exp./ASK 0.05 0.01 0.03

TOT.OP.E Total op. Exp./employee 114.84 20.13 120.79

TOT._OP. Tot.op.exp./ASK 0.08 0.01 0.05

PAX_REVE Pax revenues/RPK (pax yield) 0.11 0.02 0.09

OPERATIN Operating rev./employee ÿ0.0014 0.00145 0.00873

OPERATI1 Operating prof./ASK 145,027 12,765 76,591

YIELD Yield 0.075 0.0086 0.052

Non-®nancial

@_NON_SC Proportion non-scheduled 0.08 0.03 0.16

AVERAGE_ Total rev. hours/days 1117 198 1188

AVERAGE1 Tot. seats o�ered/total departures 113.59 12.19 73.16

AVERAGE2 Total passenger km/total passengers 6.32 0.36 2.17

AVERAGE3 Total aircraft km/departures 1496 319 1916

DISTANCE Total aircraft km/aircraft hours 597 22 135

AIRCRAF1 Aircraft dep./total personnel 34.54 2.14 12.85

AIRCRAFT Aircraft hours/total personnel 33.46 3.66 21.96

LOAD_FAC Load-factor 0.60 0.01 0.09

MAINTENA Maintenance/ASK 0.011 0.002 0.012

WEIGHT_L Weight load factor 0.52 0.01 0.09

PERCENT1 Change in GDP USA ÿ0.10 0.04 0.26

PERCENT_ Change in crude oil prices (US$/bar.) 0.0346 0.0017 0.0101

SPOT_FUE Spot fuel $ (Jet A/Jet A1) 61.00 2.39 14.34

INTEREST Interest rate USA (money market) 0.0736 0.0030 0.0178

CHANGE_1 Change in consumer prices USA ÿ0.09 0.03 0.21


3.3. Construction of quantitative ratios

From the airline database, 45 ratios and variables were constructed (see Table 2) in threedistinctive parts: (i) ®nancial; (ii) non-®nancial; and (iii) mixed, composed of one ®nancial and onenon-®nancial element. No adequate model containing mixed ratios appeared in the analysis.

As very little theoretical guidance is available in selecting the ratios (Zavgren, 1983, p. 32), anexploratory stance had to be taken in the research regarding the inclusion of variables, a stancethat was further supported by Menard (1995). However, the selection of ®nancial ratios was basedprimarily on previous studies of e�ective ratios for failure prediction (Chen and Shimerda, 1981)and measurement of corporate performance through the concept of added value (Bhattacharya,1987; Davis and Kay, 1990).

The non-®nancial ratios have no cost or revenue element associated, with the exception of %Non-scheduled, that is calculated on the basis of non-scheduled revenues as a percentage of totalrevenues. Non-®nancial ratios were divided into two parts, industry-speci®c and environment re-lated. The environment ratios/variables were based on a forecasting model of the in¯uences of theeconomic environment on air transport demand (Gudmundsson, 1986). Mixed ratios were basedpartially on the assumptions put forth by Doganis (1986), namely that a cost unit should be part

Table 2 (Continued)

n� 36 Mean SE SD

CHANGE_2 Change in interest rate USA ÿ0.14 0.03 0.18

CHANGE_I Change in spot fuel prices USA 0.0333 0.0018 0.0107

a Added value (ADV)� pro®t after tax and dividend + tax + dividend + interest + labour cost + depreciation (see

Bhattacharya, 1987, p. 139).b ASK� available seat kilometres.

Table 3

Signi®cant di�erences between failed and non-failed carriers

Ratio/variable Population Mean

NET_WORT Failed ÿ0.0056 ��

Non-failed 0.5719

EBIT_SAL Failed ÿ0.1082 ��

Non-failed 0.0558

TOT._OP Failed ÿ0.0067 ��

Non-failed 0.0036

REVEX Failed 0.9037

Non-failed 1.0486

TOTAL_DE Failed 1.2285 ��

Non-failed 0.6693

DEPRECIA Failed 0.0028 �

Non-failed 0.0044

LONG_TER Failed 0.5592 ��

Non-failed 0.3875

��p < 0.001; ��p < 0.01; �p < 0.05; ��p < 0.1.

Available ton-kilometers (ATK)� ¯ight payload capacity x stage distance ¯own; available seat-kilometers (ASK)�seats available on ¯ight x stage distance ¯own.


of a ratio supposed to measure e�ciency. In addition, average stage length and average aircraftsize were considered to be important (Gudmundsson, 1998b) and included as separate variables.

Of the 45 ratios and variables tested, six showed statistically signi®cant di�erence between failedand non-failed carriers and one showed weak signi®cance. All of these ratios (see Table 3) arehighly signi®cant (p < 0.001), with the exception of depreciation/ASK (p < 0.05) and long-termdebt/total assets which had weak signi®cance (p < 0.1). What is clearly apparent is that all of theseratios are of ®nancial orientation, while non-®nancial ratios showed non-signi®cant di�erences.This ®nding does neither pose problems in the construction of a failure or distress predictionmodel nor the interpretation of signi®cant predictor coe�cients in the models.

3.4. The application of logistic regression

Menard points out that stepwise methods can be used in exploratory analysis for theory de-velopment ``...when neither theory nor knowledge about correlates of the phenomenon is welldeveloped'' (cf. Menard, 1995, p. 38). He 6 also suggests that inclusion and removal of variablesunder such circumstances is similar as that of theory testing but less stringent. This account ex-plains well the exploratory stand taken in the approach to this research and supports the suit-ability of the stepwise Logistic Regression Analysis (LRA) methodology selected for the project.

In the project, LRA was applied using the maximum likelihood (Newton±Raphson) 7 methodfor variable selection, based on its good performance in previous failure prediction research(Cochran and Green, 1982; Zavgren, 1983) and also because in LRA the logistic coe�cients canbe interpreted, which is not possible in Multiple Discrimination Analysis (MDA). In LRA thelogistic coe�cient can be interpreted as a change in the log odds as the independent variablechanges by one unit. Thus, a coe�cient of 0.5 in a failure prediction model will, with one unitchange in the independent variable, increase the log odds of failure by 0.5. Therefore, the generalmodel for probability of failure can be stated as

Estimated probability �failure� � 1

1� eÿz;

where e is the natural logarithm and z the log-odds.In the model construction backward stepwise (BSTEP) was used in all instances except in the

qualitative model for Part I, where forward stepwise (FSTEP) was used because BSTEP resultedin a large set of non-signi®cant coe�cients. However, FSTEP resulted in the same signi®cantcoe�cients as BSTEP but much fewer non-signi®cant coe�cients and similar prediction results. InBSTEP, all variables are entered at the ®rst step and then proceed to eliminate variables from themodel. The signi®cance level for variable entry was set at p < 0.05 and for variable eliminationp > 0.1. A relaxation of these led to an increase in the inclusion of non-signi®cant coe�cients inthe models.

In LRA multicollinearity can be used to enhance the discriminatory power of the regressionfunction. Cochran and Green (1982) stated that variables that are unimportant when used alone

6 A very good account on the arguments with and against the use of stepwise methods for theory testing and

exploratory research is given by Menard (1995), pp. 54±58.7 For detailed discussion and full algorithm listing, refer to the SPSS Statistical Algorithms, 2nd ed.


may be very important in combination with other variables and enhance the classi®cation abilityof the model. Farrar and Glauber (cited in Zavgren, 1983) noted that if multicollinearity ispresent, another assumption must be added, namely that of a stability between intercorrelationsof the prediction group on which the model is based and the group on which the function will beused to predict a dichotomous outcome. If this latter assumption does not hold, the results will besample speci®c and predictability will be poorer than for the original group from which thefunction is derived. Earlier work by Edminster (1972) simply excluded variables that were cor-related with any other variable if the correlation was greater than 0.31, in an attempt to reduce thelikelihood of sample-speci®c results. This practice was not deemed necessary on the basis ofCochran's and Green's work but testing for multicollinearity was performed anyway to be on thesafe side. Hence, tolerance was calculated for each of the variables in the models showing that novariable posed serious problems allowing interpretation of individual coe�cients given the ac-ceptance of Edminster's requirement.

A technical note worth highlighting is that interpretation of coe�cients has to take into accountthat the representative variable may be highly emphasized or agreed on by both groups, regardlessof its classi®cation properties. Thus, one must not interpret a factor as a causal failure factor orsuccess factor but rather as critical factor, or that the intensity of its rating di�ers between the twogroups under observation.

Stability in variables across the three time dimensions was not assumed. This is not to say thatthe same or similar predictor variables will not appear in future research although dynamism inqualitative variables must be assumed. It was also deemed appropriate to divide the question setinto sub-groups in order to see if such sub-sets generated higher number of signi®cant modelpredictors than if one model was attempted from all questions. In fact it is highly likely that one ormore of the sub-models will contain the same model predictors as one overall model selected fromall questions (75 in Part II). This exploratory practice utilizes better the special feature of LRAthat enables interpretation of model predictors (see Menard, 1995) increasing information contentabout factors' association with distress and prediction capability of models based on such func-tional sub-groups (prediction ability was not improved by entering all questions at once).

Using the quantitative data, models were produced for each of the selected variable sets one-year-prior to bankruptcy and then tested on the data for the second- and third-years-prior tobankruptcy. Bankruptcy prediction models have sometimes been developed separately for eachyear prior to failure, often up to ®ve years. This practice is highly questionable (Keasey andMcGuinness, 1990; Robertson and Mills, 1991), since the practitioner cannot determine accuratelywhich model to use for prediction, the one year, the second year or the third year model if weassume a three-year data-set. Innes et al. (1991) concluded, however, that there is a di�erence if themodels are presented as base models, 8 whereas their predictability is then tested on the other years. 9

8 A base model means that the formula is based on that year data. This means that one can produce a base model

from data three-years-prior to failure and test its classi®cation ability on data one- and two-years-prior. This alternative

raises the practitioner ability to predict failure of companies that might not be classi®ed as prone to failure according to

a model based on ®rst-year-prior to failure. In fact one can suggest an algorithm where the practitioner starts out with a

model based on one-year-prior, then with a two-year-prior model and lastly with a three-year-prior model. If there is a

discrepancy between the three base models, there is a reason to research the company more thoroughly.9 In fact the practitioner cannot, based on the statistical model, infer how far from bankruptcy a given case is.


3.5. The Phase-In Algorithm for the small population case

The population of jet operating new-entrants posed a number of practical problems. One wasthe establishment of adequate number of carriers in the two groups of the dichotomous observedvariable, due to the small population and very high failure rate of new entrant airlines. One caninfer from observation that some jet operating new-entrants like People Express were quitesuccessful for a period in their life-cycle. Based on this assumption it was deemed appropriate todivide the life-cycle of a failed as well as non-failed carrier into phases of three years and handleeach phase as a separate module for entry either as failed or non-failed. The important empiricalsupport for this approach is the de facto that one can subjectively state, based on the data, that inthe long-term (10±15 years) most new entrants existing at a point in time will have failed or beenacquired. However, the important issue is not this long-term view but rather the short-term viewof ®nding out whether there is a probability of the airline failing in the next, let us say, two orthree years. This analogy supports the division of airlines' life-cycles 10 into non-failed versusfailed phases.

Fig. 1 shows the analogy of the Phase-In Algorithm developed and applied in the study. The ®rstphase is when the carrier has operated for three complete years, which was the minimum operatinglife for inclusion in the database. The ®rst year is therefore considered as slack. The second phaseincludes carriers that have operated for at least seven years. However, carriers failing anytimeduring the year t + 5 to t + 7 will be included only as failed, counting from the last full ®nancialyear and backward three years. For example, a carrier failing in the middle of year t + 6 is includedas failed and years t + 5, t + 4 and t + 3 are included as operating data of a failed carrier. If acarrier fails, however, in year t + 8, the carrier will be included twice, ®rst as non-failed by in-cluding year t + 2, t + 3 and t + 4 and then as failed counting year t + 5, t + 6 and t + 7. Yeart + 1 is then counted as slack for a partial year of operations. A carrier operating successfullythrough all four phases (operating between 1980±1992) will be included four times in the databaseas non-failed, providing a contrast to carriers that fail during each of its non-failed phases.

4. Results

4.1. Qualitative attitude mode

Model 1.1 (see Table 4) is based on the qualitative variables from Part I of the questionnaire.Using the logistic regression to classify the cases into two groups, distressed (D) airlines and non-distressed (ND), a 91.1% overall classi®cation was attained. The Type I error was 4.17%, while theType II error was 14.29%. The model chi-square was highly signi®cant (p < 0.01), and thepseudo-R2 (0.51) was satisfactory. 11

10 See discussion on airline life-cycles by Gudmundsson (1998a).11 The pseudo-R2 is an attempt to produce a measurement of the adequacy of the model, like that used in multiple

linear regression. The underlying principle is the `proportion of variation explained by the model' or R2 in multiple

linear regression, simulated as pseudo-R2 in logistic regression.


Two constructs were statistically signi®cant (p < 0.05), everyone in our airline understands ourlong-term aims and objectives (AIMS) and employees are rewarded for taking actions that bene®tour customers (REWA), while the coe�cient representing lack of capital will not limit our growth(CAPI) was highly signi®cant (p < 0.01). Other constructs were non-signi®cant, including theairlines' success is largely dependent on factors out of its control (SUCC), we ful®ll our customer'sneeds well (CUST), our marketing is aggressive (MARK) and group consensus is the usual way wemake decisions (CONS). The non-signi®cant predictor variables enhance the prediction accuracyof the model in association with the signi®cant predictor variables, but interpretation of thosevariables is not appropriate.

4.2. Qualitative emphasis models

Table 5 shows the resulting models for the past, present and future time dimensions. 12 Con-sistency in model predictors across time dimensions was limited as expected, re¯ecting the impactof the environment (economy, policy maker, etc.) and value systems. 13 Model construction wassuccessful for all sub-groups except ®nancial, that yielded no result, while the information andenvironment variable sub-sets produced poor models and were eliminated from the analysis. The

12 One model for each time period had to be constructed rather than to test a base model on the data sets of the other

periods. The reason is that one cannot assume that the same variables are as important in the future as in the past, due

to the inherent dynamism of the qualitative variables.13 What is considered e�ective to deal with day to day problems changes over time.

Table 4

Results of logistic regression for Part I

Model 1.1 Statement b

SUCC The airline's success is largely dependent on factors out of its control ÿ1.0158

CUST We ful®ll our customer' needs well 1.4569

MARK Our marketing is aggressive ÿ1.1002

CAPI Lack of capital will not limit our growth 1.7224 ��

AIMS Everyone in our airline understands our long-term aims and objectives 1.1385 �

REWA Employees are rewarded for taking actions that bene®t our customers ÿ1.4643 �

CONS Group consensus is the usual way we make decisions ÿ0.7493

Constant ÿ0.9600

ÿ2 log likelihood 30.159

Goodness of ®t 30.810

D 95.83

ND 85.71

Overall 91.11

Model chi-square 32.024 ��

Pseudo-R2 0.51

n D24 ND21

��p < 0.001; ��p < 0.01; �p < 0.05.

Factors were rated on a Likert scale from 1 (disagree strongly) to 5 (agree strongly).


Table 5

Results of logistic regression by variable sub-groups, Part II

Model Operations Management Marketing

(2.1) Past (2.2) Present (3.1) Past (3.2) Present (3.3) Future (4.1) Past (4.2) Present (4.3) Future

Past Hub and

spoke

operations

(HUB1)

Hub and

spoke

operations

(HUB2)

Decentralized

organization

structure

(ORGA)

Flexible job

descriptions

(FLEX)

Employees

productivity

(PROD)

Alliance with

the

incumbents

(ALLI)

Market

research

(MARK)

Frequent ¯yer

programmes

(FREQ)

ÿ0.8010 �� ÿ0.4684 � 0.5362 � 0.5311 � 1.0119 � ÿ0.4157 �� ÿ0.9361 � ÿ0.2585 �

Freight

operations

(OPS1)

Freight

operations

(OPS2)

Job rotation

(JOB1)

Job rotation

(JOB2)

Job rotation

(JOB3)

Market-share

(MASH)

Media

advertising

(MEAD)

Expansion into

new markets

(EXPA)

0.6450 � 0.4138 � ÿ0.7925 �� ÿ0.8461 �� ÿ0.7903 �� ÿ0.6874 � 0.8910 � 0.7555 ��

Aircraft

utilization

(UTIL)

0.5887 ��

Service quality

(QUAL)

1.0123 ��

Constant 0.5926 ÿ4.8343 �� 0.7047 ÿ0.1557 ÿ5.6825 ÿ2.6641 0.5289 ÿ4.4099 �

ÿ2 log

likelihood

25.122 35.083 33.104 31.194 40.250 24.114 32.907 37.506

Goodness

of ®t

25.266 30.697 39.612 28.297 35.080 27.902 48.363 37.637

D 83.33 78.95 78.95 80.00 71.43 88.24 70.59 73.68

ND 86.67 77.78 80.0 70.59 64.71 86.67 82.35 70.59

Overall 84.85 78.38 79.41 75.68 68.42 87.50 76.47 72.22

Model

chi-square

20.353 �� 16.183 �� 13.558 �� 19.855 �� 12.007 �� 20.122 �� 14.227 �� 12.289 ��

Pseudo-

R2

0.45 0.32 0.29 0.39 0.35 0.45 0.30 0.25

n D18 ND15 D19 ND18 D19 ND15 D20 ND17 D21 ND17 D17 NL15 D17 ND17 D19 ND17

��p < 0.001; ��p < 0.01; �p < 0.05; ��p < 0.1.

Factors were rated on a scale from 0 (no importance) to 10 (highly important).

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model chi-square was highly signi®cant (p < 0.01) for all models (see Table 5) through all timedimensions. It was a surprising result that the logistic regression on the qualitative ®nancialvariables produced no model given the same constraints on variable entry as that of the othermodels. As a result it appears that qualitative ®nancial factors are poorer predictors of ®nancialdistress than anticipated.

Examining the past model set, the best classifying result was obtained in the Marketing Model(4.1) or 87.5% overall classi®cation. The Type I error was 11.76% and Type II error 13.33%. Thesecond best performing model was that of Operations (2.1) with 84.85% overall classi®cationaccuracy, while the Management Model (3.1) produced 79.41% overall classi®cation. The pseudo-R2 ranged from 0.45 for the operations and Marketing Model down to 0.29 for the ManagementModel. All coe�cients were statistically signi®cant (p < 0.05) except alliance with the incumbents(ALLI), in the Marketing Model. Three coe�cients were highly signi®cant; hub and spoke oper-ations (HUB1, p < 0.01), in the Operations Model, job rotation (JOB1, p < 0.01) in the Man-agement Model and service quality (QUAL, p < 0.01) in the Marketing Model. ALLI hadsigni®cant di�erence of the means between US and EU respondents (mean EU� 3.9, USA� 2.4,p < 0.05).

Regression on the present part of the variable set resulted in three models, having similaroverall classi®cation accuracy. The pseudo-R2 was non-spectacular, ranging from 0.39 for theManagement Model (3.2) down to 0.30 for the Marketing Model (4.2). The Operations Model (2.2)outperformed the Marketing Model producing the highest overall classi®cation or 78.4%. TheType I and Type II errors were similar for the Operations Model but di�ered for the other twomodels. The Management Model had Type I error of 20.0% and the Marketing Model 29.41%,while Type II error was 29.41% for the Management Model and 17.65% for the Marketing Model.All the coe�cients are statistically signi®cant (p < 0.05), with the exception of aircraft utilization(UTIL, p < 0.1) in the Operations Model. The only highly signi®cant coe�cient was job rotation(JOB2, p < 0.01) in the Management Model. JOB2 had signi®cant di�erence of the means(p < 0.05) between US (mean� 5.4) and EU respondents (mean� 3.8) and so did MASH (meanEU� 6.2, USA� 4.5, p < 0.05) (see Gudmundsson, 1997). These variables have to be viewed withthis di�erence in mind although the validity of the models is still intact for both geographicallocations. The future part of the questionnaire survey produced no Operations Model. TheManagement Model (3.3) has Pseudo-R2 of 0.35, while the Marketing Model (4.3) had a Pseudo-R2 of 0.25. The Marketing Model reached 72.2% overall classi®cation, while the ManagementModel had 68.5%. The Management Model and the Marketing Model have lower Type I, com-pared to Type II errors, or 28.57% and 26.32% for Type I and 35.29% and 29.41% for Type IIerrors, respectively. All coe�cients are statistically signi®cant (p < 0.05), but the coe�cients forjob rotation (JOB3) and expansion into new markets (EXPA) are highly signi®cant (p < 0.01). Theoverall conclusion for the qualitative models is that the Operations, Marketing and ManagementModels yield the best results, 14 with the Marketing Model showing the best overall classi®cationaccuracy over the three time dimensions.

14 A `best' model in this context is a model producing predicted probabilities much to the expected extremes, 1 for

distressed (distressed) and 0 for non-loss making (non-distressed).


Table 6

Models for variable sets one to three-years-prior

Model Financial (5.1) Non-®nancial (6.1)

One year Two years Three years One year Two years Three years

Revexÿ13.8773 ÿ3.2898 2.5342 Average1 ÿ0.0396 � 0.0043 ÿ0.0143

ADV_Fixe ÿ8.7176 � ÿ0.0747 ÿ2.1062 � Aircraft ÿ0.3580 � ÿ0.1456 � ÿ0.1034 �

Total_De 21.4537 � 4.8552 11.6443 � Distance ÿ0.0231 �� ÿ0.0086 0.0002

Long_Ter ÿ22.4908 �� ÿ3.8757 ÿ11.819 � Aircraf1 0.2345 � 0.1419 �� 0.1297 �

Average3 0.0020 0.0003 0.0008

Load_Fac ÿ20.096 � ÿ8.1492 ÿ14.0717

@_Non_Sc ÿ12.9935 �� ÿ1.4555 1.1717

Change_2 ÿ9.1595 �� 4.5861 2.7637

Constant 10.7039 1.3234 ÿ4.5059 30.9817 � 9.8932 7.9386

-2 log likelihood 14.098 38.907 39.730 25.779 34.621 36.593

Goodness of Fit 20.428 30.848 32.026 25.846 30.552 31.440

Classi®cation

F 94.44 66.67 61.11 83.33 77.78 72.22

NF 94.44 72.22 72.22 83.33 72.22 72.22

Total 94.44 69.44 66.67 83.33 75.00 72.22

Model chi-square 35.809 � �� 11.00 � 10.176 � 24.128 � � 15.286 �� 13.314

Pseudo-R2 0.72 0.22 0.20 0.48 0.31 0.27

n, F18 NF18

�� p < 0.001; ��p < 0.01; �p < 0.05; ��p < 0.1.

Please refer to Table 2 for the key to the variable names.

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4.3. Quantitative models

Three types of ratios were used to produce quantitative prediction models: ®nancial, non-®-nancial and mixed. As mentioned before, the mixed ratios resulted in a poor model that wasdropped from further consideration, leaving a Financial (5.1) and Non-Financial Model (6.1) (seeTable 6).

Base models were speci®ed for one-year-prior to failure and then tested on two-years-prior andthree-years-prior data. Using one-year-prior data only, the Financial Model provides a highoverall classi®cation of 94.44%, while both have highly signi®cant chi-square (p < 0.01). Thepseudo-R2 was satisfactory (0.48) for the Non-Financial Model and good (0.74) for the FinancialModel. In the Financial Model two prediction coe�cients were signi®cant ADV_Fixe (p < 0.05),Total_De (p < 0.05), one had weak signi®cance Long_Ter (p < 0.1) and one was non-signi®cantRevex. In the Non-Financial Model, four predictor coe�cients were signi®cant (p < 0.05) Aver-age1, Aircraft, Aircraf1 and Load_Fac. While two had weak signi®cance (p < 0.1) @_Non_Sc andChange_2, and Average3 was non-signi®cant.

Two-years-prior to failure the overall classi®cation was reduced for all models, while the Fi-nancial and Non-Financial Models gave the best results, 69.4% and 75.0% overall classi®cation,respectively. 15 The type I error was quite di�erent between the two models, 33.3% and 22.2%,respectively, while the type II error was 27.8% for both models. The pseudo-R2 is adequate for theNon-Financial Model, but yields non-signi®cant model chi-square, while the Financial Model hadsigni®cant (p < 0.05) chi-square but low pseudo-R2.

Three-years-prior, the Non-Financial Model is providing better results than the Financial Model,producing 72.2% overall classi®cation compared to 66.67% for the latter model. The model chi-square for the Non-Financial Model was not signi®cant although the classi®cation result wassuperior to the statistically signi®cant Financial Model. The pseudo-R2 indicates that Non-Fi-nancial (0.27) is performing better than the Financial Model (0.20), although the values are notparticularly good as expected three-years-prior.

5. Comparison of models

The best performing models presented so far for the qualitative and quantitative sections weretested on those airlines that participated in the questionnaire survey and were included in thequantitative section, 13 airlines in total. The results are presented as predicted probabilities (seeTable 7). Perfect probability of p� 1 denotes failure, p� 0 non-failure, and p� 0.5 is the cut-o�point for the two states. On the basis of the cut-o� point the conformity of models was calculated,meaning that a probability of p > 0.5 renders the airline distressed or failed and p < 0.5 counts asnon-distressed or non-failed. So if an airline is considered distressed or failed in all four modelsand is in such a state (status) the conformity of models is 100%. If only three models return theactual status, the conformity is 75% and so forth. As can be seen from Table 7 the overall con-

15 Unlike the questionnaire based models, the variables obtained from the empirical selection into the one-year-prior

models are tested two- and three-years-prior, rather than having new variables entering.


formity of all four models presented was 86.5%. Conformity within individual models rangedfrom 70% to 100%, with the quantitative Non-Financial Model having low conformity, while thequalitative General Model (1.1) had 93%, the Marketing Model (4.1) 97% and the quantitativeFinancial Model (5.1) having perfect conformity of 100%.

Table 7

Comparison of predicted probabilities for qualitative and quantitative models

Airline #, status Case

#

General

qualitative

(1.1)

Marketing

qualitative

(4.1)

Financial

quantitative

(5.1)

Non-®nancial

quantitative

(6.1)

Conformity

of all four

models

109 Non-distressed Subject a 0.00 0.06 0.03 0.33 100%


118 Non-distressed Mean p� 0.37 Mean p� 0.00 0.00 0.20 100%

Subject a 0.02 0.00

Subject b 0.83 0.00

Subject c 0.27 0.00


122 Non-distressed Mean p� 0.53 Mean p� 0.03 0.01 0.99 50%

Subject a 0.80 0.00

Subject b 0.31 0.00

Subject c 0.49 0.08

104 Distressed Mean p� 0.71 Mean p� 0.99 0.99 1.00 100%

Subject a 0.70 0.99

Subject b 0.65 1.00

Subject c 0.90 0.98

Subject d 0.59 1.00

105 Distressed Mean p� 0.92 Mean p � 0.79 0.69 0.96 100%

Subject a 0.88 0.92

Subject b 1.00 0.63

Subject c 0.75 0.60

Subject d 0.99 0.82

Subject e 0.99 1.00

108 Distressed Mean p� 0.01 Mean p� 0.29 0.94 1.00 50%

Subject a 0.01 0.01

Subject b 0.03 0.87

Subject c 0.00 0.01

301 Failed Subject a 1.00 0.82 0.63 0.08 75%

304 Failed Subject a 1.00 1.00 1.00 0.28 75%

306 Failed Subject a 1.00 0.93 0.94 1.00 100%

307 Failed Subject a 0.93 0.99 1.00 1.00 100%

313 Failed Subject a 0.84 1.00 0.98 0.57 100%

Overall

classi®cation

93% 97% 100% 70% 86.5%

p�Probability of failure, where p� 1 (failure/distress) and p� 0 (non-failure/non-distress). The determination of the

dichotomous state distress/failure and non-distress/non-failure is based on ®nancial data until 1993. Financial status

applies to 1994 in the case of existing airlines. Italicized predicted probabilities can be regarded as extreme outliers.

Mean p is the calculated average of predicted probabilities shown below the number.


Examining individual respondents it is evident that there is general conformity between pre-dicted probabilities of individual respondents in the questionnaire survey and the predictedprobabilities for the associated airline derived from the database models. In the few cases wherethere is not conformity, such as airline 108, there is conformity, however, of the two quantitativemodels. In the cases where there is large discrepancy between the ®nancial and non-®nancialmodel, e.g., airlines 115, 122, 301 and 304, the explanation can be sought in the type of routestrategy adopted by the respective airlines. Airlines 301 and 304 had highly e�cient route systems,while airlines 115 and 122 had services heavily geared to business travelers. As a result, these willbe predicted as bankrupt if the Non-Financial Model (6.1) is considered in isolation despite theairline being pro®table. The reason for this predicament can be sought in the dramatically dif-ferent cost structure associated with the strategies selected and lower load factors. The model willas a result treat the high cost carrier as ine�cient although it is not in view of its strategy. Thesesort of discrepancies must be accounted for and eliminated in the model construction method-ology if misclassi®cation rates are to be reduced. That could be accomplished by segregatinggroups according to strategies adopted if di�erences are substantial such as observed in Table 7.

The predicted probabilities of ®nancial distress depicted for the General Model (1.1) at airline122 implies management con¯ict (the mean probability was calculated if there was more than onerespondent from the same airline) as there is not much agreement among its managers. A reversesituation occurs for airline 108, where predicted probabilities for the General Model and theMarketing Model (4.1) imply management characteristics and emphasis of a non-distressed ®rm,although both the Financial (5.1) and Non-Financial models classify the carrier as failed. In realitythis ®nding is con®rmed by observation, as the carrier was presenting poor ®nancial results at thetime and operating an ine�cient route system. Apparently, the management aspect of the airline isnon-characteristic of distressed ®rms, meaning that the ®rm is either exceptional or about to turn-around.

6. Discussion and implications

6.1. Interpretation of qualitative models

The signi®cant prediction coe�cients that appeared in the qualitative models can be viewed asimportant critical factors that need further discussion in a wider context. Starting with thequalitative General Model (1.1) of Part I, AIMS (see Table 4) association with non-distress impliesthe importance of an emphasis on communicating aims and objectives to employees. Whethersuch emphasis within the airline is a contributing factor to non-distress needs to be establishedthrough additional research. CAPI is also associated with non-distressed carriers, although bothgroups seem to have problems raising capital to a su�cient degree. Statement REWA has a highermean for distressed new-entrants, although one would believe on a priori that this should be areverse sign. The reason is likely an attempt to turnaround (related to the failure process), whichwould logically create increased focus on this aspect of the airline's activity.

The qualitative marketing models of Part II (4.1, 4.2 and 4.3) came up with seven mar-keting-related critical factors: ALLI, MASH, MARK, FREQ, QUAL, MEAD and EXPA.The ®rst four are related to distress but the remaining three are related to non-distress.


Although the ®rst four factors listed above are good in segregating the two groups of carriers,it does not indicate that these aspects should be de-emphasized by the airlines. Rather it is anindication that distressed carriers emphasize those factors more, implying association withdistress but not necessarily a causal relationship. This means that these factors are critical,but the questionnaire ®ndings support rather a failure process association than causal rela-tionship. MASH is important, but only on the micro-level, meaning that overall market-shareis not important, but market-share at speci®c airports and routes may be important (Gud-mundsson, 1998b).

The qualitative operations models (2.1, 2.2) of Part II came up with three critical factors UTIL,HUBi (i� 1, 2) and OPSi (i� 1, 2). Much emphasis on UTIL is characteristic of non-distressedcarriers, resulting in lower cost structure as high aircraft utilization a�ects costs in almost allaspects. Conversely, the non-distressed carriers emphasized HUBi to a much lesser degree thandistressed carriers. That ®nding reinforces the importance of aircraft utilization as hub and spokeoperations lead to less aircraft utilization than direct service as the success of Southwest Airlineshas shown. The models provided one more operations critical factor OPSi, which is positivelyrelated to non-distress, implying that freight operations are an operating feature of non-distressedcarriers. This implies that greater utilization of the aircraft by carrying freight as a byproduct is animportant revenue source for new-entrant airlines, reinforcing non-distress.

The qualitative management models (3.1, 3.2 and 3.3) of Part II highlighted four critical factorsORGA, FLEX, PROD and JOBi (i� 1, 2, 3). The ®rst three are positively related with non-distress while the last coe�cient, job rotation is negatively related. ORGA poses a di�culty ininterpretation as the variable is more highly rated by distressed carriers but has positive sign in theprediction model implying positive relationship with non-failure. What is more it has high cor-relation with JOB1 implying that job rotation and decentralized organization structure are likely togo hand in hand. JOBi on the other hand has negative sign in all models implying strong criticalcharacteristics. Too frequent changes in employees' job responsibilities creates di�culties for theairline due to lack of build up in specialization within operating areas. There is also anotherpossible explanation dealing with the failure process, namely that job rotation is used as one of thetools to turnaround the airline. If the latter explanation is accepted, one cannot establish whethersuch practice is working e�ectively or not. However, it is well known that in a turnaround situ-ation drastic reshu�ing of the management team is not uncommon. FLEX implies that ¯exiblejob descriptions are positively related with non-failure just as PROD, employees' productivity, butthese two variables are obviously highly related as ¯exible job descriptions has the precise aim ofimproving employee productivity.

6.2. Interpretation of quantitative models

Signi®cant ®nancial predictor variables that emerged were the following ratios: ADV_Fixe,Total_De and Long_ter. These results indicate that the proportional earnings capability of a new-entrant in relation to sales, expenses, ®xed assets and output is critical. However, such factorsshould be viewed as symptomatic rather than causal, making the non-®nancial variables of agreater interest to a manager of an airline looking for causal relationships. The general conclusionfor the ®nancial ratios is that gearing and revenue generated as proportion of resources applied arecritical indicative (symptomatic) factors.


The non-®nancial model came up with three signi®cant critical factors associated with mar-keting: Load_fac, @_Non_Sc and Average1 (average aircraft size). The Load_fac coe�cient had anegative sign meaning that there is negative relationship with non-failure. This is because many ofthe failed carriers had relatively high load-factors, achieved through low fares. The second factor,@_Non_Sc, is related to failure. This means that the less the carrier is involved in charter oper-ations the better. The relationship is only to an extent, as charter operations as pure by-productcannot harm the pro®tability of the carrier, but scheduled operations as a by-product, probablydoes harm the carrier involved because of possible incompatibilities of such operations (largeraverage equipment size and assigned priorities in the route network). The last factor Average1 isnegatively related, indicating that carriers operating smaller equipment fared better than thoseoperating larger equipment. The in¯uence of the former charter-based new-entrants is probablyre¯ected in this ®nding, due to their very poor results.

Signi®cant non-®nancial factors related to operations were: Aircraft, Aircraf1 and Distance.The ®rst two are e�ciency ratios of operations in terms of employee input. This aspect comes upagain and again showing that aircraft utilization is important in running a competitive airline.However, the previous ratio has negative sign indicating that a high ratio of aircraft departuresper employee is positively related to failure. Carriers having relatively many departures per em-ployee have therefore been more prone to bankruptcy compared with those having a lower ratio.It is possible that this inverse relationship is caused by poorer service quality of such carriers.However, further research must be carried out to establish such a relationship. The latter ratio,Aircraf1 has positive sign, that indicates, as expected, that an aircraft in the air is more productivethan an aircraft on the ramp. However, one can expect airlines having longer average sectorlengths to have more ¯ight hours per employee than short-haul carriers. There was only onesigni®cant environment factor derived from the database models: Change_2. The negative signshows that a rise in spot aviation fuel prices is associated with failure.

6.3. Comparison of qualitative and quantitative models

It has been demonstrated that model comparison (see Table 7) does provide increased infor-mation on the reasons behind misclassi®cation, but the question remains as to how this additionalinformation can be used in model construction that will be e�ective for a hold-out sample and inpractice. As a result, the researcher needs to identify why there is discrepancy between cases and®nd the common factors that distinguish between the misclassi®ed cases. This can be done visuallyor by using logistic regression dividing the cases into two groups according to the table above, i.e.cases that were classi®ed as non-failed by the Financial Model and failed by the Non-FinancialModel and so forth. Note that the sample size for such an analysis has to be large to begin with ifenough misclassi®ed cases are to be available for this post-analysis. In the above case it waspossible to establish through visual examination of the data that strategy applied by carriers wasinvolved. Thus a dummy variable representing strategy (i.e. 0� high service/high cost, 1� lowservice/low cost) would be necessary as a second step. Reduction of such a misclassi®cation forspeci®c industries or industry segments could be accomplished not only to account for discrep-ancy between di�erent quantitative models but also between quantitative and qualitative models.

In addition to what has been discussed so far, qualitative models can provide the practitionerwith information on possible con¯ict among top managers as can be seen from the lack of


conformity among respondents within airline 108 and 118. Such cases indicate highly di�eringviews on the airline's current situation and approach, necessitating further research into thepossible cause. If, for example, the manager is new to the airline, he may not have adequateinformation to assess the current situation, a factor that could be examined by asking the re-spondents to state the number of years/months they have been with the airline. If no such link canbe established, respondents showing deviation from colleagues must be eliminated from theanalysis as extreme outliers, given enough responses from a particular airline.

Table 8

Airlines included in the quantitative models

Airline a Status

1 Carnival 0

2 Air California 0

3 Air Wisconsin (1) 0


5 America West (1) 0

6 Horizon Air (1) 0

7 Horizon Air (2) 0

8 Midway Airlines (1) 0

9 Midwest Express 0

10 New York Air 0

11 PSA-Paci®c Southwest 0

12 People Express (1) 0

13 Southwest Airlines (1) 0



16 Tower Air (1) 0

17 Tower Air (2) 0

18 World Airways (1) 0

19 Air Atlanta 1

20 Air Florida 1


22 America West (2) 1

23 Arrow Airways 1

24 Aspen 1

25 Brani� II 1

26 Capitol Air 1

27 Empire Airlines 1

28 Florida Express 1

29 Jet America 1

30 Midway Airlines (2) 1

31 Muse Air 1

32 New York Air (2) 1

33 People Express (2) 1

34 Presidential Airways 1

35 Sunworld International Airways 1

36 World Airways (2) 1

a Airlines participating in the questionnaire survey cannot be named due to con®dentiality.

0�Non-failed, 1�Failed.


7. Summary

The study shows that all the sub-sets of variables provide di�erent classi®cation accuracy. Thisresult is valid for the past, present and future variable sub-sets of the questionnaire survey Part II.The same conclusion was obtained for the quantitative sub-sets tested. A qualitative predictionmodel based on marketing variables provided the best overall performance along three time di-mensions. It became also clear in this research that respondents of qualitative statements show lessvariation in views when asked to rate various factors into the future (projection) compared withpast (experience). The reason for this is reduced information and uncertainty associated with theunknown. The shift in emphasis among individual variables should, however, indicate futuretrends in management emphasis, given a large enough sample of respondents.

The results of qualitative model 1.1 indicates that managers of non-distressed new-entrantairlines feel that the airline's marketing is not aggressive, that capital is available to ®nancegrowth, that employees understand long-term aims and objectives, but at the same time they donot feel that employees are speci®cally rewarded for taking actions that bene®t the customers.

The results of qualitative models 2.1 and 2.2 indicate that at non-distressed new-entrant carriersless importance is placed on hub and spoke operations but more on freight operations and aircraftutilization than at distressed carriers. Qualitative models 3.1±3.3 indicate that less job rotationoccurs at non-distressed carriers than distressed carriers, and more importance is placed on de-centralized organization structure, ¯exible job descriptions and employees' productivity. Quali-tative models 4.1±4.3 indicate that at non-distressed carriers, less importance is placed on allianceswith the incumbents, market-share, market research and frequent ¯yer programmes, but moreimportance on service-quality, media advertising and expansion into new markets.

The results for quantitative model 5.1 shows that lower gearing and higher revenue generationas proportion of resources applied is characteristics of non-failed carriers. Turning to quantitativemodel 6.1, we see that during the non-failed phases of the carriers' life-cycles they were likely tohave had relatively lower load-factors, done less charter-operations, operated smaller equipment,had higher aircraft utilization and fewer departures but more aircraft hours per employee. Finally,failures were more likely to have occurred during increases in spot fuel prices.

The qualitative models reinforce the quantitative models, implying their viability for use fordistress and failure prediction and a research tool to identify variable relationships with failure ordistress and deeper understanding of the failure process. Such qualitative models upon adequatestandardization are especially viable as an internal or external monitoring device for businesses.

The main limitations of this research was the small population making a holdout sample im-possible in order to test the external validity of the models, a standard practice in this area. Inaddition, the management constructs in Part II of the questionnaire need to be developed furtheron the basis of relationships with failure and relative importance. A potentially valuable meth-odology in that regard would be conjoint analysis that would establish the importance of eachfactor more precisely. The research could not be extended to use the extra information content(use of dummy variables) that was achieved by combining the models due to the small populationsize, necessitating further research using this methodology on a larger population.

A further limitation is that there is no theoretical model available from which one can com-fortably select variables into the initial variable set. However, a repetition of studies like this onefor speci®c industries will improve our ability to identify stability in variables. The lack of un-


Table 9

Part I ± Descriptives

n� 45 95% CI

Questionnaire indicator Mean SD SE Lower Upper

Our organisational structure was decentralised 2.78 1.52 0.23 2.32 3.23

Our ®nancial control system was e�cient 3.96 1.19 0.18 3.60 4.31

The airline had a vision of the future shared by all the employees 3.53 1.31 0.19 3.14 3.93

We surrounded ourselves with sta� who promoted di�erent

rientations and points of view

3.80 1.06 0.16 3.48 4.12

Group consensus was the usual way we made decisions 3.29 1.38 0.21 2.88 3.70

The airline's success was largely dependent on factors out of its

control

2.44 1.29 0.19 2.06 2.83

The number of serious problems we were faced with increased

constantly

3.02 1.13 0.17 2.68 3.37

Our sta� was encouraged to have open discussion about the

airline's problems

4.16 1.04 0.16 3.84 4.47

We did detailed analysis before taking any major decision 3.89 1.34 0.20 3.49 4.29

We made changes in our service quite frequently 3.02 1.47 0.22 2.58 3.47

We usually had enough resources to plan for the future 3.02 1.41 0.21 2.60 3.44

Our customer loyalty was strong 4.07 1.18 0.18 3.71 4.42

We ful®lled our customer' needs well 4.42 0.58 0.08 4.25 4.60

We acted immediately upon customer complaints 4.00 1.09 0.16 3.67 4.33

Our marketing performance was good 3.93 0.96 0.14 3.64 4.22

Our long-term aims and objectives were easily achievable 2.66 1.20 0.18 2.29 3.02

Our marketing was aggressive 3.80 1.01 0.15 3.50 4.10

We usually received many useful suggestions from our employees 3.71 1.04 0.15 3.40 4.02

We were more e�cient than most of our competitors 4.31 1.10 0.16 3.98 4.64

We tried to avoid head to head competition with our larger

competitors

3.42 1.56 0.23 2.95 3.89

Our long-term aims and objectives guided our business

decisions

3.78 1.15 0.17 3.43 4.12

Our airline was ¯exible enough to respond immediately to

major opportunities

4.62 0.81 0.12 4.38 4.86

We were pleased with the performance of our distribution outlets 3.55 0.87 0.13 3.28 3.81

We were innovators in customer service compared to our

competitors

3.91 1.24 0.19 3.53 4.28

Lack of capital did not limit our growth 2.60 1.40 0.21 2.18 3.02

Everyone in our airline understood our long-term aims and

objectives

3.16 1.36 0.20 2.75 3.57

We were constantly identifying threats and opportunities to our

business

4.20 1.04 0.15 3.89 4.51

The improvement of the airline's market-share was our number

one priority

3.05 1.49 0.23 2.59 3.50

We were rarely taken by surprise by our business environment 3.25 1.22 0.18 2.88 3.62

We emphasised planning for the future 3.56 1.29 0.19 3.17 3.94

We grew by selling our services to more customers 4.38 0.91 0.14 4.10 4.65

We allocated major resources for diversi®cation into other

industries

1.39 0.87 0.13 1.12 1.65

We planned for and allocated su�cient resources to developing

new markets

3.33 1.40 0.21 2.91 3.75


derlying theory does, however, not render the results useless, because the search for good pre-dictors of distress and failure is important, what is more to link purely quantitative models withqualitative models may facilitate the emergence of an underlying theory (given enough repetitionof this method). A logical point is that `relationship' or `association' with failure does not nec-essarily imply `causal' relationship with failure. As a result, the qualitative model is build on thebasis of intensity of the rating meaning that both groups may be emphasizing a factor (customerservice) heavily although the failing group may do so to a greater extent. That in fact poses adi�culty for interpretation but can be considered either as an indicator of turnaround e�ort orextreme emphasis that can be harmful (see Miller, 1990).

To sum up, the research makes a number of contributions such as: (i) the small populationPhase-In Algorithm; (ii) it shows the viability of qualitative models and their performance inproviding indication of model predictor's relationship with distress; (iii) it shows that whenconstructing quantitative failure or distress prediction models for airlines, such models shouldtake into account strategies that have major impact on cost structures; and (iv) models based on

Table 9 (Continued)

n� 45 95% CI

Questionnaire indicator Mean SD SE Lower Upper

Long-term prospects in our primary markets were excellent 3.93 1.18 0.18 3.58 4.29

We had customer-oriented front-line people 4.36 0.98 0.15 4.06 4.65

Our service had a range of features that made it distinctive 4.11 1.11 0.17 3.78 4.45

Quality was our major competitive advantage 3.82 1.15 0.17 3.48 4.17

Everyone in our airline understood how they could improve quality 3.40 1.25 0.19 3.02 3.78

We were e�ective in monitoring our customers' expectation of quality 3.77 1.21 0.18 3.39 4.14

Our internal social and political system supported our business aims 3.50 1.21 0.18 3.13 3.87

We were good at changing our sta�'s beliefs and values 3.13 0.89 0.13 2.86 3.40

Our sta� provided us with a competitive advantage 4.18 0.89 0.13 3.91 4.44

We had incentives for our sta� that encouraged extra commitment 3.09 1.26 0.19 2.71 3.47

We had all the information we needed on our customers, markets

and opportunities

2.69 1.04 0.16 2.38 3.00

Employees were rewarded for taking actions that bene®ted our

customers

3.24 1.21 0.18 2.88 3.61

Our information system provided us with a clear competitive

advantage

2.60 1.05 0.16 2.28 2.92

We were constantly upgrading and improving our information

system

3.84 1.07 0.16 3.52 4.16

We would create the same organisation structure as we had, if given

the opportunity

3.38 1.40 0.21 2.96 3.80

We made e�ective cash-¯ow forecasts 4.29 1.01 0.15 3.98 4.59

We were good at stimulating demand for our services 4.14 0.88 0.13 3.87 4.40

We were e�ective in monitoring important cost areas 4.16 1.15 0.17 3.81 4.50

Important information was communicated to employees to enable

e�ective decision-making

3.82 1.15 0.17 3.48 4.17

The board of directors was highly involved in the airline's a�airs 3.89 1.32 0.20 3.49 4.28

The atmosphere among employees was very good 3.87 1.14 0.17 3.52 4.21

Our information systems provided quick, accurate and relevant

information

3.36 1.16 0.18 3.01 3.72


Table 10

Part II ± Descriptives

n� 43±51 95% CI

Construct Questionnaire indicator Mean SD SE Lower Upper

In¯uence of external

environment

Investors' attitudes towards the

airline (investment bankers,

stockholders, etc.)

6.02 3.42 0.48 5.05 6.99

Reduction of CRS bias a�ecting

the airline

6.01 2.28 0.32 5.37 6.65

Scale reliability� 0.76 In¯uencing government policy on

aviation

5.92 2.62 0.37 5.18 6.66

Forecasting adverse e�ects of the

economy on the airline

5.73 2.20 0.31 5.10 6.37

Favourable attitude of travel

agents

5.69 3.12 0.45 4.78 6.59

Competitor analysis 5.64 3.13 0.44 4.75 6.53

Information- and

communication system

Computer reservation system 6.16 2.21 0.31 5.53 6.78

Interdepartmental communication 5.88 2.28 0.32 5.24 6.52

Market-intelligent information-

and communication system

5.79 2.39 0.35 5.09 6.49

Control systems (monitoring the

work of the organisation)

5.70 2.38 0.34 5.02 6.38

Scale reliability� 0.85 Planning systems (integration of

information to prepare business

action plans)

5.69 2.54 0.36 4.96 6.42

Motivation systems (targets and

objectives for sta� involvement

and motivation)

5.56 2.57 0.36 4.83 6.29

Logistics systems (linking of

di�erent parts of the organisation)

5.16 2.00 0.29 4.59 5.74

Yield management system 5.02 3.19 0.45 4.12 5.92

Simpli®cation of information-

and communication systems

4.80 2.20 0.31 4.17 5.43

Operations management Aircraft utilisation 7.78 1.88 0.27 7.35 8.41

Matching of aircraft size with

route requirement

6.64 2.58 0.36 5.91 7.37

Homogeneous aircraft ¯eet 6.43 2.56 0.36 5.71 7.15

Scale reliability� 0.83 Frequency in served markets 6.35 3.05 0.43 5.49 7.21

Acquisition of airport slots 6.02 3.26 0.46 5.10 6.94

Acquisition of new aircraft 5.98 2.94 0.41 5.15 6.81

Quality of terminal space and

ground facilities

5.55 2.35 0.33 4.89 6.21

Operation on trunk routes 5.10 3.20 0.46 4.18 6.03

Interlining agreements 5.06 3.03 0.42 4.21 5.91


Table 10 (Continued)

n� 43±51 95% CI


Hub and spoke operations 3.98 3.54 0.51 2.96 5.00

Long haul routes 3.90 3.75 0.53 2.83 4.97

Freight operations 3.08 2.45 0.35 2.38 3.78

Feeder airline agreements 2.84 2.55 0.36 2.12 3.56

Code sharing 2.62 2.83 0.40 1.82 3.42

General management

and organisation

Employees' productivity 7.80 2.21 0.31 7.18 8.43

Employee relations 7.20 2.38 0.33 6.53 7.87

Flexible job descriptions 7.00 2.45 0.34 6.31 7.69

Operations without unionised

sta� (where possible)

6.90 3.19 0.46 5.98 7.81

Scale reliability� 0.84 Company culture (shared

attitudes, beliefs, norms)

6.80 2.73 0.38 6.04 7.57

Business strategy 6.66 2.50 0.35 5.95 7.37

Management's external contacts

(Government, etc.)

6.10 3.00 0.42 5.25 6.94

Shared company vision (the

future `we' want)

6.08 2.84 0.40 5.28 6.88

Company mission

(long-term aims and objectives)

5.76 2.93 0.41 4.93 6.59

Management teams

(interdepartmental teams)

5.53 2.40 0.34 4.84 6.22

Delegation 5.45 2.50 0.36 4.73 6.17

Employees' autonomy to take

decisions

5.32 2.27 0.32 4.67 5.97

Managers' incentive programme 5.18 3.09 0.43 4.31 6.05

Employees' incentive programme 5.00 3.16 0.44 4.11 5.89

Union relations 4.20 3.62 0.51 3.17 5.23

Job rotation 4.20 2.99 0.43 3.35 5.06

Sta� reduction 3.81 2.86 0.41 2.98 4.64

Decentralised organisation

structure

3.72 3.01 0.43 2.86 4.58

Financial management Cost control 7.73 2.41 0.34 7.05 8.40

Cost reduction 6.71 2.86 0.40 5.90 7.51

Fuel costs (fuel e�cient

aircraft. etc.)

6.68 2.20 0.31 6.06 7.30

Scale reliability� 0.76 Increase margins 6.59 2.49 0.35 5.89 7.29

Turnover growth 5.75 2.56 0.37 5.01 6.49

O� balance-sheet ®nancing

of aircraft

5.66 3.09 0.44 4.78 6.54

Achieving critical mass

(investment necessary before

pro®ts will be made)

5.56 3.23 0.46 4.64 6.48


industry-speci®c non-®nancial factors of airlines perform better further away from failure than®nancial factors. Finally, it is hoped that this research has raised questions with respect to thefailure process, causality and theory development that will stimulate research activity in thisimportant ®eld.

Acknowledgements

The author would like to thank Prof. Rigas Doganis and Dr. Fariba Alamdari, the editor Prof.W.G. Waters II and the two anonymous reviewers for helpful comments on earlier drafts of thiswork. Any remaining errors and omissions are the responsibility of the author.

Appendix A

In Table 8 we present airlines included in the quantitative models.

Appendix B

Tables 9 and 10 represent Parts I and II descriptives of the questionnaire survey among seniorairline managers, respectively.

Table 10 (Continued)

n� 43±51 95% CI


Long-term rather than short-term

pro®ts

5.49 3.06 0.43 4.63 6.35

Debt reduction 5.43 3.04 0.43 4.55 6.30

Reduction of labour costs 5.34 2.75 0.39 4.56 6.12

Marketing management Passenger load factors 7.06 2.12 0.30 6.46 7.66

Expansion into new markets 7.04 2.57 0.36 6.31 7.77

Price leadership in served markets 6.86 3.09 0.44 5.98 7.74

Scale reliability� 0.86 Service quality 6.73 2.68 0.38 5.97 7.48

Brand image 6.50 2.72 0.38 5.73 7.27

Media advertising 6.20 2.61 0.37 5.46 6.94

Promotion 6.12 2.30 0.33 5.47 6.77

Distribution network 5.76 2.27 0.32 5.10 6.41

Business passengers 5.75 3.19 0.45 4.85 6.64

Market research 5.74 2.40 0.34 5.06 6.42

Avoidance of price wars 5.00 3.22 0.46 4.08 5.92

Market share 4.94 3.04 0.43 4.09 5.80

Weight load factor 4.81 2.81 0.43 3.95 5.68

Commission overrides 4.43 2.52 0.36 3.70 5.14

Frequent ¯yer programmes 3.59 3.33 0.47 2.65 4.52

Alliance with the incumbents 3.17 2.63 0.38 2.40 3.93

Merger/acquisition to gain market

share

2.84 3.00 0.43 1.98 3.70

Diversi®cation into other industries 1.49 2.54 0.36 0.76 2.22


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