Directory UMM :Data Elmu:jurnal:T:Transportation Research_Logistics & Transportation Review:Vol35.Issue3.Sept1999:
Transportation Research Part E 35 (1999) 155±182
www.elsevier.com/locate/tre
Airline failure and distress prediction: a comparison of
quantitative 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 a
qualitative survey among new-entrant airline managers and a quantitative data source containing ®nancial
and trac data of new-entrant airlines. Using logistic regression, it became evident that qualitative variables are a viable source for model construction. They contribute to a deeper understanding of the underlying distress and failure process. A number of signi®cant predictor variables are discussed in the context
of 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 the
threat of entry by a new airline with new ideas and/or low cost structure was the foundation of
eective competition in a deregulated market. New-entrants started to appear shortly after deregulation, but it soon became apparent that the new-entrants had problems surviving as the postderegulation years passed. In fact, by 1990 few ®rst wave 2 new-entrants (note that only scheduled
passenger new entrants are covered in this research) had survived. The survivors as of 1993 were
Southwest Airlines, Midwest Express, Tower Air and Carnival (America West reemerged from
Chapter 11, but is counted as failed in this research) that was merged later into the re-established
Pan Am that failed shortly thereafter.
1
Tel.: +31-43-388-3442; fax: +31-43-325-4566; e-mail: s.gudmundsson@mw.unimass.nl
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.
2
1366-5545/99/$ ± see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 6 6 - 5 5 4 5 ( 9 9 ) 0 0 0 0 4 - 6
156
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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) inadequate
infrastructure (slots, etc.); (iv) poor ®nancial foundation; (v) overexpansion; (vi) poor service
standards; (vii) inadequate distribution systems; (viii) poor marketing strategy; (ix) lack of protection against predatory pricing; (x) brand image con¯ict due to evolutionary change in the
product or strategy of the new-entrants (the problems of growth, etc.); (xi) conspiracy to undermine 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 attempted to come up with qualitative failure models that would identify variables that are good
predictors of ®nancial distress and failure but at the same time provide a platform for further
research 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 since
the beginning of this century although it did not take-o until the advent of powerful computers in
the late 1960s. Various ®nancial indicators are monitored to see if ®nancial distress and failure can
be 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 and
Watson, 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 rather
the symptoms. Argenti (1976) stated in his work related to causes and symptoms of failure, that
an econometric model containing only ®nancial statement information would not provide accurate explanation as to why the companies were likely to succeed or fail. This raises interest in
whether or not failure prediction models could be used on qualitative data that provide more
information for theory building on the causes of business failure. Regardless of the shortcomings
of failure prediction models, they have shown high prediction accuracy up to three-years-prior to
failure, a fact that cannot be ignored in spite of lack of underlying theory and causal relationship
of model predictors. 3
Contrary to popular belief there is no scienti®cally well-grounded general theory available on
business failure or success. Nevertheless, there is wide conformity in the literature that the main
cause of business failures is the management of the ®rm (Argenti, 1976; Slatter, 1984; Clutterbuck
and Kernaghan, 1990). As a result, it is possible that the importance placed on various factors by
the management could in fact uncover a measurement device (questionnaire) accurate enough to
be used in a qualitative failure or distress prediction. This would provide more information than
3
This applies to MDA, but not to LRA if coecients are signi®cant.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
157
quantitative models about the model predictor's relationships with failure and distress. The
questionnaire research was also made on the presumption that there is a dierence between the
management as cause and other possible causes of failure such as the environment. Given adequate repetition of such surveys to standardize the management constructs, it is possible to
provide for a new dimension in failure prediction models if stability in variables or trends are
unveiled. This study compares the accuracy of such qualitative models with the more traditional
quantitative models in order to see whether conformity would occur across models from various
data sources. Such a comparison was accomplished by comparing the individual respondent's
prediction probability score using the qualitative models with the airline's probability score using
quantitative models. Furthermore, it was anticipated that through the comparison of various
models the study would allow exploration of the reasons behind misclassi®cation of cases as well
as 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 senior
airline managers that was divided into two distinctive parts; and (ii) a database of airline data
both of ®nancial and trac 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 relating to various aspects of organization and management constructs on a ®ve-point scale. In Part
II (see Appendix B, Table 10), the respondents were asked to indicate on a scale from 0 no
importance to 10 highly important, the importance placed on various factors: in the past, at
present and as expected in the future. Furthermore, in Part II of the questionnaire the qualitative
variable 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, present
and future. This was to account for the possible changes in management approach as managers
move 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 developed
categorization by Cowan (1990), while the statements in Part I were in the ®rst instance based on
the concepts of Hall (1992). During the course of the questionnaire development, these statements
underwent changes and adaptation to the airline industry as such. The factors presented in Part II
were developed on the basis of intuition, literature research and comments on the pilot questionnaire.
The other data-source besides the questionnaire was ®nancial and trac data about new-entrant airlines compiled from the data program (Form 41) at the US Department of Transportation (DoT). To reduce data errors, the data were inserted directly from the actual ®lings from
individual 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 airlines
established or expanding operations into interstate or international markets after deregulation of
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S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 1
Survey statistics
Population
Population
Number of
mailings
Eective
mailings
Responses by
individuals
Established
new-entrant
Recent
new-entrant
Failed
new-entrant
Specialist
Every airline meeting
criteria
Every airline meeting
criteria
Every airline meeting
criteria h
All linked with
new-entrants
136
129
30
25
32
Unknown
44
44
15 (34%)
242
na
27.9%
a
33 (26%)
b
8 (32%)
c
d
f
5 (15%)
e
Number of
airlines
Responses by
airlines
23
16 (70%)
6
5 (83%)
11
5 (45%)
±
40
±
67.7%
a
One airline went bankrupt just before the questionnaire was sent out, unknown to the researcher.
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 eective responses were received after taking into account notes (cf. footnote d and e).
h
As expected it was very dicult to reach ex-managers of failed airlines, causing major reduction in mailings compared
to the total population of managers with such experience.
b
a domestic market or bilateral route. The population of jet operating new-entrants 4 was divided
into four population stratas (see Table 1): (i) 11 failed new-entrants, de®ned as an airline that has
failed totally or ®led for Chapter XI 5; (ii) six recently established carriers, which have been operating for less than three years before the survey was conducted; and (iii) 23 established newentrants, which had been operating for more than three years when the survey was conducted in
1993. Furthermore, in order to contrast the airline managers' views it was found appropriate to
gather the views of 44 industry observers: journalists, academics or consultants associated with
aviation in general and/or new-entrant airlines. These requirements led to four dierent questionnaire formats, designed speci®cally so that responses to all questions for all stratas could be
merged for data analysis. The only exception was that industry analysts did not receive Part I of
the 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 dierent 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 dierent 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 eect through feeder agreements or ownership.
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 diculties; (iii) ceased to
operate due to bankruptcy; and (iv) ceased to operate without bankruptcy proceedings.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
159
Fig. 1. Life-cycle Phase-In Algorithm.
New-entrant airlines included and the associated managers were derived from two main
sources, Air Transport World's World Airline Report and Flight International's World Aerospace
Directory. The selection of managers was guided by the preset requirement of every subject being
a member of the executive team of the airline. This led to the inclusion of all such managers listed
in the named sources for the airlines being observed. As a result, the likelihood of receiving at least
one 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 newentrants, detected through articles or a listed specialization in the World Aerospace Directory. This
method resulted in a total of 242 mailings to individuals of which 12 were undelivered because of
the bankruptcy of the carriers in question. Much emphasis was placed on con®dentiality of responses and the reporting of results in the aggregate.
To build the quantitative models, all airlines were included that ®tted the criteria of being a jet
operating 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 ®ling
during the period 1978±1992. Through the Life-cycle Phase-In Algorithm (see Fig. 1) the sample
was 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 59
eective responses (61 in total) and 65.0% by airlines or 26 out of 40 in total. To gain fairly good
overall response rate from airlines was actually one of the main objectives of the survey. Of these
59 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 managers
and 23.7% from specialists (consultants, journalists and academics). Geographical dispersion of
respondents 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 the
same individual airline for all airlines having two or more respondents, except two that were
slightly negatively correlated. In 55.0% of the cases for Part I, the positive correlation among
respondents 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 cases
it is r > 0.5. The overall agreement of respondents from the same entity can therefore be judged to
be satisfactory.
160
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 2
Quantitative ratios and variables
n 36
Financial
ADV_FIXE
ADV_WORK
CURRENT_
CURRENT1
DEPRECI1
EBIT_SAL
REVEX
NET_INCO
NET_WORT
LABOUR_C
LONG_TER
TOTAL_DE
TOT._REV
ADDED_VA
SCHED._R
ADV/®xed assets a
ADV/working capital
Current assets/total assets
Current assets/total revenue
Depreciation/ADV
EBIT/sales
Revex
Net income/net worth
Net worth/total debt
Labour cost/ADV
Long-term debt/total assets
Total debt/total assets
Total revenue
Added value a
Total revenue scheduled ops.
Mixed
ASK_$100
ASK_$101
DEPRECIA
DIRECT_O
FUEL_ASK
INDIRECT
TOT.OP.E
TOT._OP.
PAX_REVE
OPERATIN
OPERATI1
YIELD
ASK/$1000 operating exp. b
ASK/$1000 wages
Depreciation/ASK
Direct op. Exp./ASK
Fuel/ASK
Indirect op. Exp./ASK
Total op. Exp./employee
Tot.op.exp./ASK
Pax revenues/RPK (pax yield)
Operating rev./employee
Operating prof./ASK
Yield
Non-®nancial
@_NON_SC
AVERAGE_
AVERAGE1
AVERAGE2
AVERAGE3
DISTANCE
AIRCRAF1
AIRCRAFT
LOAD_FAC
MAINTENA
WEIGHT_L
PERCENT1
PERCENT_
SPOT_FUE
INTEREST
CHANGE_1
Proportion non-scheduled
Total rev. hours/days
Tot. seats oered/total departures
Total passenger km/total passengers
Total aircraft km/departures
Total aircraft km/aircraft hours
Aircraft dep./total personnel
Aircraft hours/total personnel
Load-factor
Maintenance/ASK
Weight load factor
Change in GDP USA
Change in crude oil prices (US$/bar.)
Spot fuel $ (Jet A/Jet A1)
Interest rate USA (money market)
Change in consumer prices USA
Mean
SE
SD
0.33
ÿ1.92
0.25
0.19
0.0038
ÿ0.03
0.98
0.11
0.26
0.74
0.50
0.98
148,043
81,992,434
95.81
0.10
2.44
0.03
0.01
0.00045
0.02
0.02
0.27
0.10
0.50
0.06
0.10
12,756
23,674,390
11.74
0.62
14.64
0.15
0.08
0.0027
0.13
0.13
1.61
0.58
3.00
0.34
0.61
76,535
14,204,634
70.46
17,775
107,000
0.10
0.0285
0.0129
0.05
114.84
0.08
0.11
ÿ0.0014
145,027
0.075
1430
11,944
0.18
0.00396
0.00190
0.01
20.13
0.01
0.02
0.00145
12,765
0.0086
0.08
1117
113.59
6.32
1496
597
34.54
33.46
0.60
0.011
0.52
ÿ0.10
0.0346
61.00
0.0736
ÿ0.09
0.03
198
12.19
0.36
319
22
2.14
3.66
0.01
0.002
0.01
0.04
0.0017
2.39
0.0030
0.03
8579
71,667
1.09
0.0238
0.0114
0.03
120.79
0.05
0.09
0.00873
76,591
0.052
0.16
1188
73.16
2.17
1916
135
12.85
21.96
0.09
0.012
0.09
0.26
0.0101
14.34
0.0178
0.21
161
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 2 (Continued)
n 36
CHANGE_2
CHANGE_I
Mean
Change in interest rate USA
Change in spot fuel prices USA
ÿ0.14
0.0333
SE
SD
0.03
0.0018
0.18
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.
3.3. Construction of quantitative ratios
From the airline database, 45 ratios and variables were constructed (see Table 2) in three
distinctive parts: (i) ®nancial; (ii) non-®nancial; and (iii) mixed, composed of one ®nancial and one
non-®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), an
exploratory stance had to be taken in the research regarding the inclusion of variables, a stance
that was further supported by Menard (1995). However, the selection of ®nancial ratios was based
primarily on previous studies of eective 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 total
revenues. Non-®nancial ratios were divided into two parts, industry-speci®c and environment related. The environment ratios/variables were based on a forecasting model of the in¯uences of the
economic environment on air transport demand (Gudmundsson, 1986). Mixed ratios were based
partially on the assumptions put forth by Doganis (1986), namely that a cost unit should be part
Table 3
Signi®cant dierences between failed and non-failed carriers
Ratio/variable
Population
Mean
NET_WORT
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
ÿ0.0056
0.5719
ÿ0.1082
0.0558
ÿ0.0067
0.0036
0.9037
1.0486
1.2285
0.6693
0.0028
0.0044
0.5592
0.3875
EBIT_SAL
TOT._OP
REVEX
TOTAL_DE
DEPRECIA
LONG_TER
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.
162
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
of a ratio supposed to measure eciency. In addition, average stage length and average aircraft
size were considered to be important (Gudmundsson, 1998b) and included as separate variables.
Of the 45 ratios and variables tested, six showed statistically signi®cant dierence between failed
and non-failed carriers and one showed weak signi®cance. All of these ratios (see Table 3) are
highly signi®cant (p < 0.001), with the exception of depreciation/ASK (p < 0.05) and long-term
debt/total assets which had weak signi®cance (p < 0.1). What is clearly apparent is that all of these
ratios are of ®nancial orientation, while non-®nancial ratios showed non-signi®cant dierences.
This ®nding does neither pose problems in the construction of a failure or distress prediction
model nor the interpretation of signi®cant predictor coecients in the models.
3.4. The application of logistic regression
Menard points out that stepwise methods can be used in exploratory analysis for theory development ``...when neither theory nor knowledge about correlates of the phenomenon is well
developed'' (cf. Menard, 1995, p. 38). He 6 also suggests that inclusion and removal of variables
under such circumstances is similar as that of theory testing but less stringent. This account explains well the exploratory stand taken in the approach to this research and supports the suitability 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 method
for 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 coecients can
be interpreted, which is not possible in Multiple Discrimination Analysis (MDA). In LRA the
logistic coecient can be interpreted as a change in the log odds as the independent variable
changes by one unit. Thus, a coecient of 0.5 in a failure prediction model will, with one unit
change in the independent variable, increase the log odds of failure by 0.5. Therefore, the general
model for probability of failure can be stated as
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 resulted
in a large set of non-signi®cant coecients. However, FSTEP resulted in the same signi®cant
coecients as BSTEP but much fewer non-signi®cant coecients and similar prediction results. In
BSTEP, all variables are entered at the ®rst step and then proceed to eliminate variables from the
model. The signi®cance level for variable entry was set at p < 0.05 and for variable elimination
p > 0.1. A relaxation of these led to an increase in the inclusion of non-signi®cant coecients in
the models.
In LRA multicollinearity can be used to enhance the discriminatory power of the regression
function. Cochran and Green (1982) stated that variables that are unimportant when used alone
Estimated probability failure
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.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
163
may be very important in combination with other variables and enhance the classi®cation ability
of the model. Farrar and Glauber (cited in Zavgren, 1983) noted that if multicollinearity is
present, another assumption must be added, namely that of a stability between intercorrelations
of the prediction group on which the model is based and the group on which the function will be
used to predict a dichotomous outcome. If this latter assumption does not hold, the results will be
sample speci®c and predictability will be poorer than for the original group from which the
function is derived. Earlier work by Edminster (1972) simply excluded variables that were correlated with any other variable if the correlation was greater than 0.31, in an attempt to reduce the
likelihood of sample-speci®c results. This practice was not deemed necessary on the basis of
Cochran's and Green's work but testing for multicollinearity was performed anyway to be on the
safe side. Hence, tolerance was calculated for each of the variables in the models showing that no
variable posed serious problems allowing interpretation of individual coecients given the acceptance of Edminster's requirement.
A technical note worth highlighting is that interpretation of coecients has to take into account
that the representative variable may be highly emphasized or agreed on by both groups, regardless
of its classi®cation properties. Thus, one must not interpret a factor as a causal failure factor or
success factor but rather as critical factor, or that the intensity of its rating diers between the two
groups under observation.
Stability in variables across the three time dimensions was not assumed. This is not to say that
the same or similar predictor variables will not appear in future research although dynamism in
qualitative variables must be assumed. It was also deemed appropriate to divide the question set
into sub-groups in order to see if such sub-sets generated higher number of signi®cant model
predictors than if one model was attempted from all questions. In fact it is highly likely that one or
more of the sub-models will contain the same model predictors as one overall model selected from
all questions (75 in Part II). This exploratory practice utilizes better the special feature of LRA
that enables interpretation of model predictors (see Menard, 1995) increasing information content
about factors' association with distress and prediction capability of models based on such functional 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 oneyear-prior to bankruptcy and then tested on the data for the second- and third-years-prior to
bankruptcy. Bankruptcy prediction models have sometimes been developed separately for each
year prior to failure, often up to ®ve years. This practice is highly questionable (Keasey and
McGuinness, 1990; Robertson and Mills, 1991), since the practitioner cannot determine accurately
which model to use for prediction, the one year, the second year or the third year model if we
assume a three-year data-set. Innes et al. (1991) concluded, however, that there is a dierence if the
models 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.
164
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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 was
the establishment of adequate number of carriers in the two groups of the dichotomous observed
variable, due to the small population and very high failure rate of new entrant airlines. One can
infer from observation that some jet operating new-entrants like People Express were quite
successful for a period in their life-cycle. Based on this assumption it was deemed appropriate to
divide the life-cycle of a failed as well as non-failed carrier into phases of three years and handle
each phase as a separate module for entry either as failed or non-failed. The important empirical
support for this approach is the de facto that one can subjectively state, based on the data, that in
the long-term (10±15 years) most new entrants existing at a point in time will have failed or been
acquired. However, the important issue is not this long-term view but rather the short-term view
of ®nding out whether there is a probability of the airline failing in the next, let us say, two or
three years. This analogy supports the division of airlines' life-cycles 10 into non-failed versus
failed phases.
Fig. 1 shows the analogy of the Phase-In Algorithm developed and applied in the study. The ®rst
phase is when the carrier has operated for three complete years, which was the minimum operating
life for inclusion in the database. The ®rst year is therefore considered as slack. The second phase
includes carriers that have operated for at least seven years. However, carriers failing anytime
during the year t + 5 to t + 7 will be included only as failed, counting from the last full ®nancial
year and backward three years. For example, a carrier failing in the middle of year t + 6 is included
as failed and years t + 5, t + 4 and t + 3 are included as operating data of a failed carrier. If a
carrier fails, however, in year t + 8, the carrier will be included twice, ®rst as non-failed by including year t + 2, t + 3 and t + 4 and then as failed counting year t + 5, t + 6 and t + 7. Year
t + 1 is then counted as slack for a partial year of operations. A carrier operating successfully
through all four phases (operating between 1980±1992) will be included four times in the database
as 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 nondistressed (ND), a 91.1% overall classi®cation was attained. The Type I error was 4.17%, while the
Type II error was 14.29%. The model chi-square was highly signi®cant (p < 0.01), and the
pseudo-R2 (0.51) was satisfactory. 11
10
See discussion on airline life-cycles by Gudmundsson (1998a).
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.
11
165
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 4
Results of logistic regression for Part I
Model 1.1
Statement
b
SUCC
CUST
MARK
CAPI
AIMS
REWA
CONS
The airline's success is largely dependent on factors out of its control
We ful®ll our customer' needs well
Our marketing is aggressive
Lack of capital will not limit our growth
Everyone in our airline understands our long-term aims and objectives
Employees are rewarded for taking actions that bene®t our customers
Group consensus is the usual way we make decisions
Constant
ÿ1.0158
1.4569
ÿ1.1002
1.7224
1.1385
ÿ1.4643
ÿ0.7493
ÿ0.9600
ÿ2 log likelihood
Goodness of ®t
D
ND
Overall
Model chi-square
Pseudo-R2
n
30.159
30.810
95.83
85.71
91.11
32.024
0.51
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).
Two constructs were statistically signi®cant (p < 0.05), everyone in our airline understands our
long-term aims and objectives (AIMS) and employees are rewarded for taking actions that bene®t
our customers (REWA), while the coecient representing lack of capital will not limit our growth
(CAPI) was highly signi®cant (p < 0.01). Other constructs were non-signi®cant, including the
airlines' success is largely dependent on factors out of its control (SUCC), we ful®ll our customer's
needs well (CUST), our marketing is aggressive (MARK) and group consensus is the usual way we
make decisions (CONS). The non-signi®cant predictor variables enhance the prediction accuracy
of the model in association with the signi®cant predictor variables, but interpretation of those
variables is not appropriate.
4.2. Qualitative emphasis models
Table 5 shows the resulting models for the past, present and future time dimensions. 12 Consistency in model predictors across time dimensions was limited as expected, re¯ecting the impact
of the environment (economy, policy maker, etc.) and value systems. 13 Model construction was
successful for all sub-groups except ®nancial, that yielded no result, while the information and
environment 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 eective to deal with day to day problems changes over time.
166
Table 5
Results of logistic regression by variable sub-groups, Part II
Model
Constant
ÿ2 log
likelihood
Goodness
of ®t
D
ND
Overall
Model
chi-square
PseudoR2
n
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
Hub and
spoke
operations
(HUB1)
ÿ0.8010
Freight
operations
(OPS1)
0.6450
Hub and
spoke
operations
(HUB2)
ÿ0.4684
Freight
operations
(OPS2)
0.4138
Aircraft
utilization
(UTIL)
0.5887
Decentralized
organization
structure
(ORGA)
0.5362
Job rotation
(JOB1)
Flexible job
descriptions
(FLEX)
Employees
productivity
(PROD)
Market
research
(MARK)
Frequent ¯yer
programmes
(FREQ)
0.5311
Job rotation
(JOB2)
1.0119
Job rotation
(JOB3)
Alliance with
the
incumbents
(ALLI)
ÿ0.4157
Market-share
(MASH)
ÿ0.9361
Media
advertising
(MEAD)
0.8910
ÿ0.2585
Expansion into
new markets
(EXPA)
0.7555
0.5926
25.122
ÿ4.8343
35.083
25.266
83.33
86.67
84.85
20.353
ÿ0.7925
ÿ0.8461
ÿ0.7903
ÿ0.6874
Service quality
(QUAL)
1.0123
0.7047
33.104
ÿ0.1557
31.194
ÿ5.6825
40.250
ÿ2.6641
24.114
0.5289
32.907
ÿ4.4099
37.506
30.697
39.612
28.297
35.080
27.902
48.363
37.637
78.95
77.78
78.38
16.183
78.95
80.0
79.41
13.558
80.00
70.59
75.68
19.855
71.43
64.71
68.42
12.007
88.24
86.67
87.50
20.122
70.59
82.35
76.47
14.227
73.68
70.59
72.22
12.289
0.45
0.32
0.29
0.39
0.35
0.45
0.30
0.25
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).
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Past
Operations
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
167
model chi-square was highly signi®cant (p < 0.01) for all models (see Table 5) through all time
dimensions. It was a surprising result that the logistic regression on the qualitative ®nancial
variables produced no model given the same constraints on variable entry as that of the other
models. As a result it appears that qualitative ®nancial factors are poorer predictors of ®nancial
distress 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%. The
second best performing model was that of Operations (2.1) with 84.85% overall classi®cation
accuracy, while the Management Model (3.1) produced 79.41% overall classi®cation. The pseudoR2 ranged from 0.45 for the operations and Marketing Model down to 0.29 for the Management
Model. All coecients were statistically signi®cant (p < 0.05) except alliance with the incumbents
(ALLI), in the Marketing Model. Three coecients were highly signi®cant; hub and spoke operations (HUB1, p < 0.01), in the Operations Model, job rotation (JOB1, p < 0.01) in the Management Model and service quality (QUAL, p < 0.01) in the Marketing Model. ALLI had
signi®cant dierence 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 similar
overall classi®cation accuracy. The pseudo-R2 was non-spectacular, ranging from 0.39 for the
Management 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%. The
Type I and Type II errors were similar for the Operations Model but diered for the other two
models. 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 coecients 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 coecient was job rotation
(JOB2, p < 0.01) in the Management Model. JOB2 had signi®cant dierence of the means
(p < 0.05) between US (mean 5.4) and EU respondents (mean 3.8) and so did MASH (mean
EU 6.2, USA 4.5, p < 0.05) (see Gudmundsson, 1997). These variables have to be viewed with
this dierence in mind although the validity of the models is still intact for both geographical
locations. The future part of the questionnaire survey produced no Operations Model. The
Management Model (3.3) has Pseudo-R2 of 0.35, while the Marketing Model (4.3) had a PseudoR2 of 0.25. The Marketing Model reached 72.2% overall classi®cation, while the Management
Model had 68.5%. The Management Model and the Marketing Model have lower Type I, compared to Type II errors, or 28.57% and 26.32% for Type I and 35.29% and 29.41% for Type II
errors, respectively. All coecients are statistically signi®cant (p < 0.05), but the coecients for
job rotation (JOB3) and expansion into new markets (EXPA) are highly signi®cant (p < 0.01). The
overall conclusion for the qualitative models is that the Operations, Marketing and Management
Models yield the best results, 14 with the Marketing Model showing the best overall classi®cation
accuracy 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).
168
Model
Financial (5.1)
One year
Constant
-2 log likelihood
Goodness of Fit
Classi®cation
F
NF
Total
Model chi-square
Pseudo-R2
n, F18 NF18
Non-®nancial (6.1)
Two years
Three years
One year
Revexÿ13.8773
ADV_Fixe ÿ8.7176
Total_De 21.4537
Long_Ter ÿ22.4908
ÿ3.2898
ÿ0.0747
4.8552
ÿ3.8757
2.5342
ÿ2.1062
11.6443
ÿ11.819
10.7039
14.098
20.428
1.3234
38.907
30.848
ÿ4.5059
39.730
32.026
30.9817
25.779
25.846
94.44
94.44
94.44
35.809
0.72
66.67
72.22
69.44
11.00
0.22
61.11
72.22
66.67
10.176
0.20
83.33
83.33
83.33
24.128
0.48
p < 0.001; p < 0.01; p < 0.05; p < 0.1.
Please refer to Table 2 for the key to the variable names.
Average1 ÿ0.0396
Aircraft ÿ0.3580
Distance ÿ0.0231
Aircraf1 0.2345
Average3 0.0020
Load_Fac ÿ20.096
@_Non_Sc ÿ12.9935
Change_2 ÿ9.1595
Two years
Three years
0.0043
ÿ0.1456
ÿ0.0086
0.1419
0.0003
ÿ8.1492
ÿ1.4555
4.5861
ÿ0.0143
ÿ0.1034
0.0002
0.1297
0.0008
ÿ14.0717
1.1717
2.7637
9.8932
34.621
30.552
7.9386
36.593
31.440
77.78
72.22
75.00
15.286
0.31
72.22
72.22
72.22
13.314
0.27
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 6
Models for variable sets one to three-years-prior
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
169
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 was
dropped from further consideration, leaving a Financial (5.1) and Non-Financial Model (6.1) (see
Table 6).
Base models were speci®ed for one-year-prior to failure and then tested on two-years-prior and
three-years-prior data. Using one-year-prior data only, the Financial Model provides a high
overall classi®cation of 94.44%, while both have highly signi®cant chi-square (p < 0.01). The
pseudo-R2 was satisfactory (0.48) for the Non-Financial Model and good (0.74) for the Financial
Model. In the Financial Model two prediction coecients 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®cant
Revex. In the Non-Financial Model, four predictor coecients were signi®cant (p < 0.05) Average1, Aircraft, Aircraf1 and Load_Fac. While two had weak signi®cance (p < 0.1) @_Non_Sc and
Change_2, and Average3 was non-signi®cant.
Two-years-prior to failure the overall classi®cation was reduced for all models, while the Financial and Non-Financial Models gave the best results, 69.4% and 75.0% overall classi®cation,
respectively. 15 The type I error was quite dierent 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 the
Non-Financial Model, but yields non-signi®cant model chi-square, while the Financial Model had
signi®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 chisquare for the Non-Financial Model was not signi®cant although the classi®cation result was
superior to the statistically signi®cant Financial Model. The pseudo-R2 indicates that Non-Financial (0.27) is performing better than the Financial Model (0.20), although the values are not
particularly good as expected three-years-prior.
5. Comparison of models
The best performing models presented so far for the qualitative and quantitative sections were
tested on those airlines that participated in the questionnaire survey and were included in the
quantitative section, 13 airlines in total. The results are presented as predicted probabilities (see
Table 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 as
non-distressed or non-failed. So if an airline is considered distressed or failed in all four models
and is in such a state (status) the conformity of models is 100%. If only three models return the
actual 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.
170
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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
115 Non-distressed
118 Non-distressed
Subject a
Subject a
0.00
0.01
Mean
0.02
0.83
0.27
0.08
Mean
0.80
0.31
0.49
Mean
0.70
0.65
0.90
0.59
Mean
0.88
1.00
0.75
0.99
0.99
Mean
0.01
0.03
0.00
1.00
1.00
1.00
0.93
0.84
0.06
0.06
Mean
0.00
0.00
0.00
0.00
Mean
0.00
0.00
0.08
Mean
0.99
1.00
0.98
1.00
Mean
0.92
0.63
0.60
0.82
1.00
Mean
0.01
0.87
0.01
0.82
1.00
0.93
0.99
1.00
p 0.00
0.03
0.01
0.00
0.33
1.00
0.20
100%
75%
100%
p 0.03
0.19
0.01
0.01
0.99
100%
50%
p 0.99
0.99
1.00
100%
p 0.79
0.69
0.96
100%
p 0.29
0.94
1.00
50%
0.63
1.00
0.94
1.00
0.98
0.08
0.28
1.00
1.00
0.57
75%
75%
100%
100%
100%
100%
70%
86.5%
120 Non-distressed
122 Non-distressed
Subject
Subject
Subject
Subject
a
b
c
a
Subject a
Subject b
Subject c
104 Distressed
Subject
Subject
Subject
Subject
a
b
c
d
Subject
Subject
Subject
Subject
Subject
a
b
c
d
e
Subject
Subject
Subject
Subject
Subject
Subject
Subject
Subject
a
b
c
a
a
a
a
a
105 Distressed
108 Distressed
301
304
306
307
313
Failed
Failed
Failed
Failed
Failed
Overall
classi®cation
93%
p 0.37
p 0.53
p 0.71
p 0.92
p 0.01
97%
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.
formity of all four models presented was 86.5%. Conformity within individual models ranged
from 70% to 100%, with the quantitative Non-Financial Model having low conformity, while the
qualitative General Model (1.1) had 93%, the Marketing Model (4.1) 97% and the quantitative
Financial Model (5.1) having perfect conformity of 100%.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
171
Examining individual respondents it is evident that there is general conformity between predicted probabilities of individual respondents in the questionnaire survey and the predicted
probabilities for the associated airline derived from the database models. In the few cases where
there is not conformity, such as airline 108, there is conformity, however, of the two quantitative
models. In the cases where there is large discrepancy between the ®nancial and non-®nancial
model, e.g., airlines 115, 122, 301 and 304, the explanation can be sought in the type of route
strategy adopted by the respective airlines. Airlines 301 and 304 had highly ecient route systems,
while airlines 115 and 122 had services heavily geared to business travelers. As a result, these will
be predicted as bankrupt if the Non-Financial Model (6.1) is considered in isolation despite the
airline being pro®table. The reason for this predicament can be sought in the dramatically different cost structure associated with the strategies selected and lower load factors. The model will
as a result treat the high cost carrier as inecient although it is not in view of its strategy. These
sort of discrepancies must be accounted for and eliminated in the model construction methodology if misclassi®cation rates are to be reduced. That could be accomplished by segregating
groups according to strategies adopted if dierences are substantial such as observed in Table 7.
The predicted probabilities of ®nancial distress depicted for the General Model (1.1) at airline
122 implies management con¯ict (the mean probability was calculated if there was more than one
respondent from the same airline) as there is not much agreement among its managers. A reverse
situation occurs for airline 108, where predicted probabilities for the General Model and the
Marketing 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 reality
this ®nding is con®rmed by observation, as the carrier was presenting poor ®nancial results at the
time and operating an inecient route system. Apparently, the management aspect of the airline is
non-characteristic of distressed ®rms, meaning that the ®rm is either exceptional or about to turnaround.
6. Discussion and implications
6.1. Interpretation of qualitative models
The signi®cant prediction coecients that appeared in the qualitative models can be viewed as
important critical factors that need further discussion in a wider context. Starting with the
qualitative General Model (1.1) of Part I, AIMS (see Table 4) association with non-distress implies
the importance of an emphasis on communicating aims and objectives to employees. Whether
such emphasis within the airline is a contributing factor to non-distress needs to be established
through additional research. CAPI is also associated with non-distressed carriers, although both
groups seem to have problems raising capital to a sucient degree
www.elsevier.com/locate/tre
Airline failure and distress prediction: a comparison of
quantitative 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 a
qualitative survey among new-entrant airline managers and a quantitative data source containing ®nancial
and trac data of new-entrant airlines. Using logistic regression, it became evident that qualitative variables are a viable source for model construction. They contribute to a deeper understanding of the underlying distress and failure process. A number of signi®cant predictor variables are discussed in the context
of 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 the
threat of entry by a new airline with new ideas and/or low cost structure was the foundation of
eective competition in a deregulated market. New-entrants started to appear shortly after deregulation, but it soon became apparent that the new-entrants had problems surviving as the postderegulation years passed. In fact, by 1990 few ®rst wave 2 new-entrants (note that only scheduled
passenger new entrants are covered in this research) had survived. The survivors as of 1993 were
Southwest Airlines, Midwest Express, Tower Air and Carnival (America West reemerged from
Chapter 11, but is counted as failed in this research) that was merged later into the re-established
Pan Am that failed shortly thereafter.
1
Tel.: +31-43-388-3442; fax: +31-43-325-4566; e-mail: s.gudmundsson@mw.unimass.nl
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.
2
1366-5545/99/$ ± see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 6 6 - 5 5 4 5 ( 9 9 ) 0 0 0 0 4 - 6
156
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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) inadequate
infrastructure (slots, etc.); (iv) poor ®nancial foundation; (v) overexpansion; (vi) poor service
standards; (vii) inadequate distribution systems; (viii) poor marketing strategy; (ix) lack of protection against predatory pricing; (x) brand image con¯ict due to evolutionary change in the
product or strategy of the new-entrants (the problems of growth, etc.); (xi) conspiracy to undermine 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 attempted to come up with qualitative failure models that would identify variables that are good
predictors of ®nancial distress and failure but at the same time provide a platform for further
research 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 since
the beginning of this century although it did not take-o until the advent of powerful computers in
the late 1960s. Various ®nancial indicators are monitored to see if ®nancial distress and failure can
be 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 and
Watson, 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 rather
the symptoms. Argenti (1976) stated in his work related to causes and symptoms of failure, that
an econometric model containing only ®nancial statement information would not provide accurate explanation as to why the companies were likely to succeed or fail. This raises interest in
whether or not failure prediction models could be used on qualitative data that provide more
information for theory building on the causes of business failure. Regardless of the shortcomings
of failure prediction models, they have shown high prediction accuracy up to three-years-prior to
failure, a fact that cannot be ignored in spite of lack of underlying theory and causal relationship
of model predictors. 3
Contrary to popular belief there is no scienti®cally well-grounded general theory available on
business failure or success. Nevertheless, there is wide conformity in the literature that the main
cause of business failures is the management of the ®rm (Argenti, 1976; Slatter, 1984; Clutterbuck
and Kernaghan, 1990). As a result, it is possible that the importance placed on various factors by
the management could in fact uncover a measurement device (questionnaire) accurate enough to
be used in a qualitative failure or distress prediction. This would provide more information than
3
This applies to MDA, but not to LRA if coecients are signi®cant.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
157
quantitative models about the model predictor's relationships with failure and distress. The
questionnaire research was also made on the presumption that there is a dierence between the
management as cause and other possible causes of failure such as the environment. Given adequate repetition of such surveys to standardize the management constructs, it is possible to
provide for a new dimension in failure prediction models if stability in variables or trends are
unveiled. This study compares the accuracy of such qualitative models with the more traditional
quantitative models in order to see whether conformity would occur across models from various
data sources. Such a comparison was accomplished by comparing the individual respondent's
prediction probability score using the qualitative models with the airline's probability score using
quantitative models. Furthermore, it was anticipated that through the comparison of various
models the study would allow exploration of the reasons behind misclassi®cation of cases as well
as 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 senior
airline managers that was divided into two distinctive parts; and (ii) a database of airline data
both of ®nancial and trac 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 relating to various aspects of organization and management constructs on a ®ve-point scale. In Part
II (see Appendix B, Table 10), the respondents were asked to indicate on a scale from 0 no
importance to 10 highly important, the importance placed on various factors: in the past, at
present and as expected in the future. Furthermore, in Part II of the questionnaire the qualitative
variable 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, present
and future. This was to account for the possible changes in management approach as managers
move 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 developed
categorization by Cowan (1990), while the statements in Part I were in the ®rst instance based on
the concepts of Hall (1992). During the course of the questionnaire development, these statements
underwent changes and adaptation to the airline industry as such. The factors presented in Part II
were developed on the basis of intuition, literature research and comments on the pilot questionnaire.
The other data-source besides the questionnaire was ®nancial and trac data about new-entrant airlines compiled from the data program (Form 41) at the US Department of Transportation (DoT). To reduce data errors, the data were inserted directly from the actual ®lings from
individual 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 airlines
established or expanding operations into interstate or international markets after deregulation of
158
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 1
Survey statistics
Population
Population
Number of
mailings
Eective
mailings
Responses by
individuals
Established
new-entrant
Recent
new-entrant
Failed
new-entrant
Specialist
Every airline meeting
criteria
Every airline meeting
criteria
Every airline meeting
criteria h
All linked with
new-entrants
136
129
30
25
32
Unknown
44
44
15 (34%)
242
na
27.9%
a
33 (26%)
b
8 (32%)
c
d
f
5 (15%)
e
Number of
airlines
Responses by
airlines
23
16 (70%)
6
5 (83%)
11
5 (45%)
±
40
±
67.7%
a
One airline went bankrupt just before the questionnaire was sent out, unknown to the researcher.
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 eective responses were received after taking into account notes (cf. footnote d and e).
h
As expected it was very dicult to reach ex-managers of failed airlines, causing major reduction in mailings compared
to the total population of managers with such experience.
b
a domestic market or bilateral route. The population of jet operating new-entrants 4 was divided
into four population stratas (see Table 1): (i) 11 failed new-entrants, de®ned as an airline that has
failed totally or ®led for Chapter XI 5; (ii) six recently established carriers, which have been operating for less than three years before the survey was conducted; and (iii) 23 established newentrants, which had been operating for more than three years when the survey was conducted in
1993. Furthermore, in order to contrast the airline managers' views it was found appropriate to
gather the views of 44 industry observers: journalists, academics or consultants associated with
aviation in general and/or new-entrant airlines. These requirements led to four dierent questionnaire formats, designed speci®cally so that responses to all questions for all stratas could be
merged for data analysis. The only exception was that industry analysts did not receive Part I of
the 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 dierent 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 dierent 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 eect through feeder agreements or ownership.
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 diculties; (iii) ceased to
operate due to bankruptcy; and (iv) ceased to operate without bankruptcy proceedings.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
159
Fig. 1. Life-cycle Phase-In Algorithm.
New-entrant airlines included and the associated managers were derived from two main
sources, Air Transport World's World Airline Report and Flight International's World Aerospace
Directory. The selection of managers was guided by the preset requirement of every subject being
a member of the executive team of the airline. This led to the inclusion of all such managers listed
in the named sources for the airlines being observed. As a result, the likelihood of receiving at least
one 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 newentrants, detected through articles or a listed specialization in the World Aerospace Directory. This
method resulted in a total of 242 mailings to individuals of which 12 were undelivered because of
the bankruptcy of the carriers in question. Much emphasis was placed on con®dentiality of responses and the reporting of results in the aggregate.
To build the quantitative models, all airlines were included that ®tted the criteria of being a jet
operating 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 ®ling
during the period 1978±1992. Through the Life-cycle Phase-In Algorithm (see Fig. 1) the sample
was 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 59
eective responses (61 in total) and 65.0% by airlines or 26 out of 40 in total. To gain fairly good
overall response rate from airlines was actually one of the main objectives of the survey. Of these
59 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 managers
and 23.7% from specialists (consultants, journalists and academics). Geographical dispersion of
respondents 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 the
same individual airline for all airlines having two or more respondents, except two that were
slightly negatively correlated. In 55.0% of the cases for Part I, the positive correlation among
respondents 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 cases
it is r > 0.5. The overall agreement of respondents from the same entity can therefore be judged to
be satisfactory.
160
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 2
Quantitative ratios and variables
n 36
Financial
ADV_FIXE
ADV_WORK
CURRENT_
CURRENT1
DEPRECI1
EBIT_SAL
REVEX
NET_INCO
NET_WORT
LABOUR_C
LONG_TER
TOTAL_DE
TOT._REV
ADDED_VA
SCHED._R
ADV/®xed assets a
ADV/working capital
Current assets/total assets
Current assets/total revenue
Depreciation/ADV
EBIT/sales
Revex
Net income/net worth
Net worth/total debt
Labour cost/ADV
Long-term debt/total assets
Total debt/total assets
Total revenue
Added value a
Total revenue scheduled ops.
Mixed
ASK_$100
ASK_$101
DEPRECIA
DIRECT_O
FUEL_ASK
INDIRECT
TOT.OP.E
TOT._OP.
PAX_REVE
OPERATIN
OPERATI1
YIELD
ASK/$1000 operating exp. b
ASK/$1000 wages
Depreciation/ASK
Direct op. Exp./ASK
Fuel/ASK
Indirect op. Exp./ASK
Total op. Exp./employee
Tot.op.exp./ASK
Pax revenues/RPK (pax yield)
Operating rev./employee
Operating prof./ASK
Yield
Non-®nancial
@_NON_SC
AVERAGE_
AVERAGE1
AVERAGE2
AVERAGE3
DISTANCE
AIRCRAF1
AIRCRAFT
LOAD_FAC
MAINTENA
WEIGHT_L
PERCENT1
PERCENT_
SPOT_FUE
INTEREST
CHANGE_1
Proportion non-scheduled
Total rev. hours/days
Tot. seats oered/total departures
Total passenger km/total passengers
Total aircraft km/departures
Total aircraft km/aircraft hours
Aircraft dep./total personnel
Aircraft hours/total personnel
Load-factor
Maintenance/ASK
Weight load factor
Change in GDP USA
Change in crude oil prices (US$/bar.)
Spot fuel $ (Jet A/Jet A1)
Interest rate USA (money market)
Change in consumer prices USA
Mean
SE
SD
0.33
ÿ1.92
0.25
0.19
0.0038
ÿ0.03
0.98
0.11
0.26
0.74
0.50
0.98
148,043
81,992,434
95.81
0.10
2.44
0.03
0.01
0.00045
0.02
0.02
0.27
0.10
0.50
0.06
0.10
12,756
23,674,390
11.74
0.62
14.64
0.15
0.08
0.0027
0.13
0.13
1.61
0.58
3.00
0.34
0.61
76,535
14,204,634
70.46
17,775
107,000
0.10
0.0285
0.0129
0.05
114.84
0.08
0.11
ÿ0.0014
145,027
0.075
1430
11,944
0.18
0.00396
0.00190
0.01
20.13
0.01
0.02
0.00145
12,765
0.0086
0.08
1117
113.59
6.32
1496
597
34.54
33.46
0.60
0.011
0.52
ÿ0.10
0.0346
61.00
0.0736
ÿ0.09
0.03
198
12.19
0.36
319
22
2.14
3.66
0.01
0.002
0.01
0.04
0.0017
2.39
0.0030
0.03
8579
71,667
1.09
0.0238
0.0114
0.03
120.79
0.05
0.09
0.00873
76,591
0.052
0.16
1188
73.16
2.17
1916
135
12.85
21.96
0.09
0.012
0.09
0.26
0.0101
14.34
0.0178
0.21
161
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 2 (Continued)
n 36
CHANGE_2
CHANGE_I
Mean
Change in interest rate USA
Change in spot fuel prices USA
ÿ0.14
0.0333
SE
SD
0.03
0.0018
0.18
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.
3.3. Construction of quantitative ratios
From the airline database, 45 ratios and variables were constructed (see Table 2) in three
distinctive parts: (i) ®nancial; (ii) non-®nancial; and (iii) mixed, composed of one ®nancial and one
non-®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), an
exploratory stance had to be taken in the research regarding the inclusion of variables, a stance
that was further supported by Menard (1995). However, the selection of ®nancial ratios was based
primarily on previous studies of eective 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 total
revenues. Non-®nancial ratios were divided into two parts, industry-speci®c and environment related. The environment ratios/variables were based on a forecasting model of the in¯uences of the
economic environment on air transport demand (Gudmundsson, 1986). Mixed ratios were based
partially on the assumptions put forth by Doganis (1986), namely that a cost unit should be part
Table 3
Signi®cant dierences between failed and non-failed carriers
Ratio/variable
Population
Mean
NET_WORT
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
Failed
Non-failed
ÿ0.0056
0.5719
ÿ0.1082
0.0558
ÿ0.0067
0.0036
0.9037
1.0486
1.2285
0.6693
0.0028
0.0044
0.5592
0.3875
EBIT_SAL
TOT._OP
REVEX
TOTAL_DE
DEPRECIA
LONG_TER
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.
162
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
of a ratio supposed to measure eciency. In addition, average stage length and average aircraft
size were considered to be important (Gudmundsson, 1998b) and included as separate variables.
Of the 45 ratios and variables tested, six showed statistically signi®cant dierence between failed
and non-failed carriers and one showed weak signi®cance. All of these ratios (see Table 3) are
highly signi®cant (p < 0.001), with the exception of depreciation/ASK (p < 0.05) and long-term
debt/total assets which had weak signi®cance (p < 0.1). What is clearly apparent is that all of these
ratios are of ®nancial orientation, while non-®nancial ratios showed non-signi®cant dierences.
This ®nding does neither pose problems in the construction of a failure or distress prediction
model nor the interpretation of signi®cant predictor coecients in the models.
3.4. The application of logistic regression
Menard points out that stepwise methods can be used in exploratory analysis for theory development ``...when neither theory nor knowledge about correlates of the phenomenon is well
developed'' (cf. Menard, 1995, p. 38). He 6 also suggests that inclusion and removal of variables
under such circumstances is similar as that of theory testing but less stringent. This account explains well the exploratory stand taken in the approach to this research and supports the suitability 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 method
for 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 coecients can
be interpreted, which is not possible in Multiple Discrimination Analysis (MDA). In LRA the
logistic coecient can be interpreted as a change in the log odds as the independent variable
changes by one unit. Thus, a coecient of 0.5 in a failure prediction model will, with one unit
change in the independent variable, increase the log odds of failure by 0.5. Therefore, the general
model for probability of failure can be stated as
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 resulted
in a large set of non-signi®cant coecients. However, FSTEP resulted in the same signi®cant
coecients as BSTEP but much fewer non-signi®cant coecients and similar prediction results. In
BSTEP, all variables are entered at the ®rst step and then proceed to eliminate variables from the
model. The signi®cance level for variable entry was set at p < 0.05 and for variable elimination
p > 0.1. A relaxation of these led to an increase in the inclusion of non-signi®cant coecients in
the models.
In LRA multicollinearity can be used to enhance the discriminatory power of the regression
function. Cochran and Green (1982) stated that variables that are unimportant when used alone
Estimated probability failure
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.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
163
may be very important in combination with other variables and enhance the classi®cation ability
of the model. Farrar and Glauber (cited in Zavgren, 1983) noted that if multicollinearity is
present, another assumption must be added, namely that of a stability between intercorrelations
of the prediction group on which the model is based and the group on which the function will be
used to predict a dichotomous outcome. If this latter assumption does not hold, the results will be
sample speci®c and predictability will be poorer than for the original group from which the
function is derived. Earlier work by Edminster (1972) simply excluded variables that were correlated with any other variable if the correlation was greater than 0.31, in an attempt to reduce the
likelihood of sample-speci®c results. This practice was not deemed necessary on the basis of
Cochran's and Green's work but testing for multicollinearity was performed anyway to be on the
safe side. Hence, tolerance was calculated for each of the variables in the models showing that no
variable posed serious problems allowing interpretation of individual coecients given the acceptance of Edminster's requirement.
A technical note worth highlighting is that interpretation of coecients has to take into account
that the representative variable may be highly emphasized or agreed on by both groups, regardless
of its classi®cation properties. Thus, one must not interpret a factor as a causal failure factor or
success factor but rather as critical factor, or that the intensity of its rating diers between the two
groups under observation.
Stability in variables across the three time dimensions was not assumed. This is not to say that
the same or similar predictor variables will not appear in future research although dynamism in
qualitative variables must be assumed. It was also deemed appropriate to divide the question set
into sub-groups in order to see if such sub-sets generated higher number of signi®cant model
predictors than if one model was attempted from all questions. In fact it is highly likely that one or
more of the sub-models will contain the same model predictors as one overall model selected from
all questions (75 in Part II). This exploratory practice utilizes better the special feature of LRA
that enables interpretation of model predictors (see Menard, 1995) increasing information content
about factors' association with distress and prediction capability of models based on such functional 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 oneyear-prior to bankruptcy and then tested on the data for the second- and third-years-prior to
bankruptcy. Bankruptcy prediction models have sometimes been developed separately for each
year prior to failure, often up to ®ve years. This practice is highly questionable (Keasey and
McGuinness, 1990; Robertson and Mills, 1991), since the practitioner cannot determine accurately
which model to use for prediction, the one year, the second year or the third year model if we
assume a three-year data-set. Innes et al. (1991) concluded, however, that there is a dierence if the
models 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.
164
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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 was
the establishment of adequate number of carriers in the two groups of the dichotomous observed
variable, due to the small population and very high failure rate of new entrant airlines. One can
infer from observation that some jet operating new-entrants like People Express were quite
successful for a period in their life-cycle. Based on this assumption it was deemed appropriate to
divide the life-cycle of a failed as well as non-failed carrier into phases of three years and handle
each phase as a separate module for entry either as failed or non-failed. The important empirical
support for this approach is the de facto that one can subjectively state, based on the data, that in
the long-term (10±15 years) most new entrants existing at a point in time will have failed or been
acquired. However, the important issue is not this long-term view but rather the short-term view
of ®nding out whether there is a probability of the airline failing in the next, let us say, two or
three years. This analogy supports the division of airlines' life-cycles 10 into non-failed versus
failed phases.
Fig. 1 shows the analogy of the Phase-In Algorithm developed and applied in the study. The ®rst
phase is when the carrier has operated for three complete years, which was the minimum operating
life for inclusion in the database. The ®rst year is therefore considered as slack. The second phase
includes carriers that have operated for at least seven years. However, carriers failing anytime
during the year t + 5 to t + 7 will be included only as failed, counting from the last full ®nancial
year and backward three years. For example, a carrier failing in the middle of year t + 6 is included
as failed and years t + 5, t + 4 and t + 3 are included as operating data of a failed carrier. If a
carrier fails, however, in year t + 8, the carrier will be included twice, ®rst as non-failed by including year t + 2, t + 3 and t + 4 and then as failed counting year t + 5, t + 6 and t + 7. Year
t + 1 is then counted as slack for a partial year of operations. A carrier operating successfully
through all four phases (operating between 1980±1992) will be included four times in the database
as 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 nondistressed (ND), a 91.1% overall classi®cation was attained. The Type I error was 4.17%, while the
Type II error was 14.29%. The model chi-square was highly signi®cant (p < 0.01), and the
pseudo-R2 (0.51) was satisfactory. 11
10
See discussion on airline life-cycles by Gudmundsson (1998a).
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.
11
165
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 4
Results of logistic regression for Part I
Model 1.1
Statement
b
SUCC
CUST
MARK
CAPI
AIMS
REWA
CONS
The airline's success is largely dependent on factors out of its control
We ful®ll our customer' needs well
Our marketing is aggressive
Lack of capital will not limit our growth
Everyone in our airline understands our long-term aims and objectives
Employees are rewarded for taking actions that bene®t our customers
Group consensus is the usual way we make decisions
Constant
ÿ1.0158
1.4569
ÿ1.1002
1.7224
1.1385
ÿ1.4643
ÿ0.7493
ÿ0.9600
ÿ2 log likelihood
Goodness of ®t
D
ND
Overall
Model chi-square
Pseudo-R2
n
30.159
30.810
95.83
85.71
91.11
32.024
0.51
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).
Two constructs were statistically signi®cant (p < 0.05), everyone in our airline understands our
long-term aims and objectives (AIMS) and employees are rewarded for taking actions that bene®t
our customers (REWA), while the coecient representing lack of capital will not limit our growth
(CAPI) was highly signi®cant (p < 0.01). Other constructs were non-signi®cant, including the
airlines' success is largely dependent on factors out of its control (SUCC), we ful®ll our customer's
needs well (CUST), our marketing is aggressive (MARK) and group consensus is the usual way we
make decisions (CONS). The non-signi®cant predictor variables enhance the prediction accuracy
of the model in association with the signi®cant predictor variables, but interpretation of those
variables is not appropriate.
4.2. Qualitative emphasis models
Table 5 shows the resulting models for the past, present and future time dimensions. 12 Consistency in model predictors across time dimensions was limited as expected, re¯ecting the impact
of the environment (economy, policy maker, etc.) and value systems. 13 Model construction was
successful for all sub-groups except ®nancial, that yielded no result, while the information and
environment 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 eective to deal with day to day problems changes over time.
166
Table 5
Results of logistic regression by variable sub-groups, Part II
Model
Constant
ÿ2 log
likelihood
Goodness
of ®t
D
ND
Overall
Model
chi-square
PseudoR2
n
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
Hub and
spoke
operations
(HUB1)
ÿ0.8010
Freight
operations
(OPS1)
0.6450
Hub and
spoke
operations
(HUB2)
ÿ0.4684
Freight
operations
(OPS2)
0.4138
Aircraft
utilization
(UTIL)
0.5887
Decentralized
organization
structure
(ORGA)
0.5362
Job rotation
(JOB1)
Flexible job
descriptions
(FLEX)
Employees
productivity
(PROD)
Market
research
(MARK)
Frequent ¯yer
programmes
(FREQ)
0.5311
Job rotation
(JOB2)
1.0119
Job rotation
(JOB3)
Alliance with
the
incumbents
(ALLI)
ÿ0.4157
Market-share
(MASH)
ÿ0.9361
Media
advertising
(MEAD)
0.8910
ÿ0.2585
Expansion into
new markets
(EXPA)
0.7555
0.5926
25.122
ÿ4.8343
35.083
25.266
83.33
86.67
84.85
20.353
ÿ0.7925
ÿ0.8461
ÿ0.7903
ÿ0.6874
Service quality
(QUAL)
1.0123
0.7047
33.104
ÿ0.1557
31.194
ÿ5.6825
40.250
ÿ2.6641
24.114
0.5289
32.907
ÿ4.4099
37.506
30.697
39.612
28.297
35.080
27.902
48.363
37.637
78.95
77.78
78.38
16.183
78.95
80.0
79.41
13.558
80.00
70.59
75.68
19.855
71.43
64.71
68.42
12.007
88.24
86.67
87.50
20.122
70.59
82.35
76.47
14.227
73.68
70.59
72.22
12.289
0.45
0.32
0.29
0.39
0.35
0.45
0.30
0.25
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).
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Past
Operations
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
167
model chi-square was highly signi®cant (p < 0.01) for all models (see Table 5) through all time
dimensions. It was a surprising result that the logistic regression on the qualitative ®nancial
variables produced no model given the same constraints on variable entry as that of the other
models. As a result it appears that qualitative ®nancial factors are poorer predictors of ®nancial
distress 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%. The
second best performing model was that of Operations (2.1) with 84.85% overall classi®cation
accuracy, while the Management Model (3.1) produced 79.41% overall classi®cation. The pseudoR2 ranged from 0.45 for the operations and Marketing Model down to 0.29 for the Management
Model. All coecients were statistically signi®cant (p < 0.05) except alliance with the incumbents
(ALLI), in the Marketing Model. Three coecients were highly signi®cant; hub and spoke operations (HUB1, p < 0.01), in the Operations Model, job rotation (JOB1, p < 0.01) in the Management Model and service quality (QUAL, p < 0.01) in the Marketing Model. ALLI had
signi®cant dierence 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 similar
overall classi®cation accuracy. The pseudo-R2 was non-spectacular, ranging from 0.39 for the
Management 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%. The
Type I and Type II errors were similar for the Operations Model but diered for the other two
models. 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 coecients 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 coecient was job rotation
(JOB2, p < 0.01) in the Management Model. JOB2 had signi®cant dierence of the means
(p < 0.05) between US (mean 5.4) and EU respondents (mean 3.8) and so did MASH (mean
EU 6.2, USA 4.5, p < 0.05) (see Gudmundsson, 1997). These variables have to be viewed with
this dierence in mind although the validity of the models is still intact for both geographical
locations. The future part of the questionnaire survey produced no Operations Model. The
Management Model (3.3) has Pseudo-R2 of 0.35, while the Marketing Model (4.3) had a PseudoR2 of 0.25. The Marketing Model reached 72.2% overall classi®cation, while the Management
Model had 68.5%. The Management Model and the Marketing Model have lower Type I, compared to Type II errors, or 28.57% and 26.32% for Type I and 35.29% and 29.41% for Type II
errors, respectively. All coecients are statistically signi®cant (p < 0.05), but the coecients for
job rotation (JOB3) and expansion into new markets (EXPA) are highly signi®cant (p < 0.01). The
overall conclusion for the qualitative models is that the Operations, Marketing and Management
Models yield the best results, 14 with the Marketing Model showing the best overall classi®cation
accuracy 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).
168
Model
Financial (5.1)
One year
Constant
-2 log likelihood
Goodness of Fit
Classi®cation
F
NF
Total
Model chi-square
Pseudo-R2
n, F18 NF18
Non-®nancial (6.1)
Two years
Three years
One year
Revexÿ13.8773
ADV_Fixe ÿ8.7176
Total_De 21.4537
Long_Ter ÿ22.4908
ÿ3.2898
ÿ0.0747
4.8552
ÿ3.8757
2.5342
ÿ2.1062
11.6443
ÿ11.819
10.7039
14.098
20.428
1.3234
38.907
30.848
ÿ4.5059
39.730
32.026
30.9817
25.779
25.846
94.44
94.44
94.44
35.809
0.72
66.67
72.22
69.44
11.00
0.22
61.11
72.22
66.67
10.176
0.20
83.33
83.33
83.33
24.128
0.48
p < 0.001; p < 0.01; p < 0.05; p < 0.1.
Please refer to Table 2 for the key to the variable names.
Average1 ÿ0.0396
Aircraft ÿ0.3580
Distance ÿ0.0231
Aircraf1 0.2345
Average3 0.0020
Load_Fac ÿ20.096
@_Non_Sc ÿ12.9935
Change_2 ÿ9.1595
Two years
Three years
0.0043
ÿ0.1456
ÿ0.0086
0.1419
0.0003
ÿ8.1492
ÿ1.4555
4.5861
ÿ0.0143
ÿ0.1034
0.0002
0.1297
0.0008
ÿ14.0717
1.1717
2.7637
9.8932
34.621
30.552
7.9386
36.593
31.440
77.78
72.22
75.00
15.286
0.31
72.22
72.22
72.22
13.314
0.27
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
Table 6
Models for variable sets one to three-years-prior
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
169
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 was
dropped from further consideration, leaving a Financial (5.1) and Non-Financial Model (6.1) (see
Table 6).
Base models were speci®ed for one-year-prior to failure and then tested on two-years-prior and
three-years-prior data. Using one-year-prior data only, the Financial Model provides a high
overall classi®cation of 94.44%, while both have highly signi®cant chi-square (p < 0.01). The
pseudo-R2 was satisfactory (0.48) for the Non-Financial Model and good (0.74) for the Financial
Model. In the Financial Model two prediction coecients 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®cant
Revex. In the Non-Financial Model, four predictor coecients were signi®cant (p < 0.05) Average1, Aircraft, Aircraf1 and Load_Fac. While two had weak signi®cance (p < 0.1) @_Non_Sc and
Change_2, and Average3 was non-signi®cant.
Two-years-prior to failure the overall classi®cation was reduced for all models, while the Financial and Non-Financial Models gave the best results, 69.4% and 75.0% overall classi®cation,
respectively. 15 The type I error was quite dierent 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 the
Non-Financial Model, but yields non-signi®cant model chi-square, while the Financial Model had
signi®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 chisquare for the Non-Financial Model was not signi®cant although the classi®cation result was
superior to the statistically signi®cant Financial Model. The pseudo-R2 indicates that Non-Financial (0.27) is performing better than the Financial Model (0.20), although the values are not
particularly good as expected three-years-prior.
5. Comparison of models
The best performing models presented so far for the qualitative and quantitative sections were
tested on those airlines that participated in the questionnaire survey and were included in the
quantitative section, 13 airlines in total. The results are presented as predicted probabilities (see
Table 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 as
non-distressed or non-failed. So if an airline is considered distressed or failed in all four models
and is in such a state (status) the conformity of models is 100%. If only three models return the
actual 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.
170
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
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
115 Non-distressed
118 Non-distressed
Subject a
Subject a
0.00
0.01
Mean
0.02
0.83
0.27
0.08
Mean
0.80
0.31
0.49
Mean
0.70
0.65
0.90
0.59
Mean
0.88
1.00
0.75
0.99
0.99
Mean
0.01
0.03
0.00
1.00
1.00
1.00
0.93
0.84
0.06
0.06
Mean
0.00
0.00
0.00
0.00
Mean
0.00
0.00
0.08
Mean
0.99
1.00
0.98
1.00
Mean
0.92
0.63
0.60
0.82
1.00
Mean
0.01
0.87
0.01
0.82
1.00
0.93
0.99
1.00
p 0.00
0.03
0.01
0.00
0.33
1.00
0.20
100%
75%
100%
p 0.03
0.19
0.01
0.01
0.99
100%
50%
p 0.99
0.99
1.00
100%
p 0.79
0.69
0.96
100%
p 0.29
0.94
1.00
50%
0.63
1.00
0.94
1.00
0.98
0.08
0.28
1.00
1.00
0.57
75%
75%
100%
100%
100%
100%
70%
86.5%
120 Non-distressed
122 Non-distressed
Subject
Subject
Subject
Subject
a
b
c
a
Subject a
Subject b
Subject c
104 Distressed
Subject
Subject
Subject
Subject
a
b
c
d
Subject
Subject
Subject
Subject
Subject
a
b
c
d
e
Subject
Subject
Subject
Subject
Subject
Subject
Subject
Subject
a
b
c
a
a
a
a
a
105 Distressed
108 Distressed
301
304
306
307
313
Failed
Failed
Failed
Failed
Failed
Overall
classi®cation
93%
p 0.37
p 0.53
p 0.71
p 0.92
p 0.01
97%
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.
formity of all four models presented was 86.5%. Conformity within individual models ranged
from 70% to 100%, with the quantitative Non-Financial Model having low conformity, while the
qualitative General Model (1.1) had 93%, the Marketing Model (4.1) 97% and the quantitative
Financial Model (5.1) having perfect conformity of 100%.
S. Vidar Gudmundsson / Transportation Research Part E 35 (1999) 155±182
171
Examining individual respondents it is evident that there is general conformity between predicted probabilities of individual respondents in the questionnaire survey and the predicted
probabilities for the associated airline derived from the database models. In the few cases where
there is not conformity, such as airline 108, there is conformity, however, of the two quantitative
models. In the cases where there is large discrepancy between the ®nancial and non-®nancial
model, e.g., airlines 115, 122, 301 and 304, the explanation can be sought in the type of route
strategy adopted by the respective airlines. Airlines 301 and 304 had highly ecient route systems,
while airlines 115 and 122 had services heavily geared to business travelers. As a result, these will
be predicted as bankrupt if the Non-Financial Model (6.1) is considered in isolation despite the
airline being pro®table. The reason for this predicament can be sought in the dramatically different cost structure associated with the strategies selected and lower load factors. The model will
as a result treat the high cost carrier as inecient although it is not in view of its strategy. These
sort of discrepancies must be accounted for and eliminated in the model construction methodology if misclassi®cation rates are to be reduced. That could be accomplished by segregating
groups according to strategies adopted if dierences are substantial such as observed in Table 7.
The predicted probabilities of ®nancial distress depicted for the General Model (1.1) at airline
122 implies management con¯ict (the mean probability was calculated if there was more than one
respondent from the same airline) as there is not much agreement among its managers. A reverse
situation occurs for airline 108, where predicted probabilities for the General Model and the
Marketing 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 reality
this ®nding is con®rmed by observation, as the carrier was presenting poor ®nancial results at the
time and operating an inecient route system. Apparently, the management aspect of the airline is
non-characteristic of distressed ®rms, meaning that the ®rm is either exceptional or about to turnaround.
6. Discussion and implications
6.1. Interpretation of qualitative models
The signi®cant prediction coecients that appeared in the qualitative models can be viewed as
important critical factors that need further discussion in a wider context. Starting with the
qualitative General Model (1.1) of Part I, AIMS (see Table 4) association with non-distress implies
the importance of an emphasis on communicating aims and objectives to employees. Whether
such emphasis within the airline is a contributing factor to non-distress needs to be established
through additional research. CAPI is also associated with non-distressed carriers, although both
groups seem to have problems raising capital to a sucient degree