Airline failure and distress prediction: a comparison of
quantitative and qualitative models
Sveinn Vidar Gudmundsson
1Faculty 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 vari-ables are a viable source for model construction. They contribute to a deeper understanding of the un-derlying distress and failure process. A number of signi®cant predictor variables are discussed in the 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 de-regulation, but it soon became apparent that the new-entrants had problems surviving as the post-deregulation years passed. In fact, by 1990 few ®rst wave2new-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.
www.elsevier.com/locate/tre
1
Tel.: +31-43-388-3442; fax: +31-43-325-4566; e-mail: s.gudmundsson@mw.unimass.nl
2
First wave new-entrants being intrastate, charter, cargo, regional or new airlines that took advantage of deregulation before 1990, while second wave new-entrants are carriers that started operations thereafter.
1366-5545/99/$ ± see front matterÓ1999 Elsevier Science Ltd. All rights reserved.
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 pro-tection 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 un-dermine the new-entrant by incumbents; and (xii) unfavorable economic climate (recession, etc.). Recognizing that there is an element of complexity in these business failures, this research at-tempted to come up with qualitative failure models that would identify variables that are 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 accu-rate 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.
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 ade-quate 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 re-lating 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 0no importance to 10highly important, the importance placed on various factors: in the past, at
presentand as expected in thefuture. 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 intervalspast,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 ques-tionnaire.
The other data-source besides the questionnaire was ®nancial and trac data about new-en-trant airlines compiled from the data program (Form 41) at the US Department of Transpor-tation (DoT). To reduce data errors, the data were inserted directly from the actual ®lings from individual airlines at the premises of the DoT.
3.1. Population description
a domestic market or bilateral route. The population of jet operating new-entrants4was divided into four population stratas (see Table 1): (i) 11failednew-entrants, de®ned as an airline that has failed totally or ®led for Chapter XI 5; (ii) six recentlyestablished carriers, which have been op-erating for less than three years before the survey was conducted; and (iii) 23 established new-entrants, which had been operating for more than three years when the survey was conducted 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 ques-tionnaire 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.
4This 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.
Table 1
a One airline went bankrupt just before the questionnaire was sent out, unknown to the researcher. bTwo airlines went bankrupt during the survey execution period.
cPresent 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.
dOne response was seriously positive biased and was rejected on the basis of two other responses from the same airline. eOne 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).
hAs 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.
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.
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 (mean7). The specialists were, however, selected on the basis of their association with issues relating to new-entrants, detected through articles or a listed specialization in theWorld 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 re-sponses and the reporting of results in the aggregate.
To build the quantitative models, all airlines were included that ®tted the criteria of being a 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 isr > 0.5; in 82.0% of cases it isr> 0.3. For Part II, the correlation for all valid cases (correlation could not be calculated if there were missing values) isr > 0.3 and for 38.0% of cases it isr> 0.5. The overall agreement of respondents from the same entity can therefore be judged to be satisfactory.
Table 2
Quantitative ratios and variables
n36 Mean SE SD
Financial
ADV_FIXE ADV/®xed assetsa 0.33 0.10 0.62
ADV_WORK ADV/working capital ÿ1.92 2.44 14.64
CURRENT_ Current assets/total assets 0.25 0.03 0.15
CURRENT1 Current assets/total revenue 0.19 0.01 0.08
DEPRECI1 Depreciation/ADV 0.0038 0.00045 0.0027
EBIT_SAL EBIT/sales ÿ0.03 0.02 0.13
REVEX Revex 0.98 0.02 0.13
NET_INCO Net income/net worth 0.11 0.27 1.61
NET_WORT Net worth/total debt 0.26 0.10 0.58
LABOUR_C Labour cost/ADV 0.74 0.50 3.00
LONG_TER Long-term debt/total assets 0.50 0.06 0.34
TOTAL_DE Total debt/total assets 0.98 0.10 0.61
TOT._REV Total revenue 148,043 12,756 76,535
ADDED_VA Added valuea 81,992,434 23,674,390 14,204,634
SCHED._R Total revenue scheduled ops. 95.81 11.74 70.46
Mixed
ASK_$100 ASK/$1000 operating exp.b 17,775 1430 8579
ASK_$101 ASK/$1000 wages 107,000 11,944 71,667
DEPRECIA Depreciation/ASK 0.10 0.18 1.09
DIRECT_O Direct op. Exp./ASK 0.0285 0.00396 0.0238
FUEL_ASK Fuel/ASK 0.0129 0.00190 0.0114
INDIRECT Indirect op. Exp./ASK 0.05 0.01 0.03
TOT.OP.E Total op. Exp./employee 114.84 20.13 120.79
TOT._OP. Tot.op.exp./ASK 0.08 0.01 0.05
PAX_REVE Pax revenues/RPK (pax yield) 0.11 0.02 0.09
OPERATIN Operating rev./employee ÿ0.0014 0.00145 0.00873
OPERATI1 Operating prof./ASK 145,027 12,765 76,591
YIELD Yield 0.075 0.0086 0.052
Non-®nancial
@_NON_SC Proportion non-scheduled 0.08 0.03 0.16
AVERAGE_ Total rev. hours/days 1117 198 1188
AVERAGE1 Tot. seats oered/total departures 113.59 12.19 73.16
AVERAGE2 Total passenger km/total passengers 6.32 0.36 2.17
AVERAGE3 Total aircraft km/departures 1496 319 1916
DISTANCE Total aircraft km/aircraft hours 597 22 135
AIRCRAF1 Aircraft dep./total personnel 34.54 2.14 12.85
AIRCRAFT Aircraft hours/total personnel 33.46 3.66 21.96
LOAD_FAC Load-factor 0.60 0.01 0.09
MAINTENA Maintenance/ASK 0.011 0.002 0.012
WEIGHT_L Weight load factor 0.52 0.01 0.09
PERCENT1 Change in GDP USA ÿ0.10 0.04 0.26
PERCENT_ Change in crude oil prices (US$/bar.) 0.0346 0.0017 0.0101
SPOT_FUE Spot fuel $ (Jet A/Jet A1) 61.00 2.39 14.34
INTEREST Interest rate USA (money market) 0.0736 0.0030 0.0178
CHANGE_1 Change in consumer prices USA ÿ0.09 0.03 0.21
3.3. Construction of quantitative ratios
From the airline database, 45 ratios and variables were constructed (see Table 2) in 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®cand environment re-lated. 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 2(Continued)
n36 Mean SE SD
CHANGE_2 Change in interest rate USA ÿ0.14 0.03 0.18
CHANGE_I Change in spot fuel prices USA 0.0333 0.0018 0.0107
aAdded value (ADV)pro®t after tax and dividend + tax + dividend + interest + labour cost + depreciation (see
Bhattacharya, 1987, p. 139).
bASKavailable seat kilometres.
Table 3
Signi®cant dierences between failed and non-failed carriers
Ratio/variable Population Mean
NET_WORT Failed ÿ0.0056
Non-failed 0.5719
EBIT_SAL Failed ÿ0.1082
Non-failed 0.0558
TOT._OP Failed ÿ0.0067
Non-failed 0.0036
REVEX Failed 0.9037
Non-failed 1.0486
TOTAL_DE Failed 1.2285
Non-failed 0.6693
DEPRECIA Failed 0.0028
Non-failed 0.0044
LONG_TER Failed 0.5592
Non-failed 0.3875
p<0.001;p<0.01;p<0.05;p<0.1.
Available ton-kilometers (ATK)¯ight payload capacityx stage distance ¯own; available seat-kilometers (ASK)
of a ratio supposed to measure eciency. In addition, average stage length and average aircraft sizewere 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 assetswhich 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 de-velopment ``...when neither theory nor knowledge about correlates of the phenomenon is well developed'' (cf. Menard, 1995, p. 38). He6also suggests that inclusion and removal of variables under such circumstances is similar as that of theory testing but less stringent. This account ex-plains well the exploratory stand taken in the approach to this research and supports the suit-ability of the stepwise Logistic Regression Analysis (LRA) methodology selected for the project. In the project, LRA was applied using the maximum likelihood (Newton±Raphson)7method 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
Estimated probability failure 1
1eÿz; where e is the natural logarithm and zthe 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
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
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 cor-related 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 ac-ceptance 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 causalfailure factoror
success factorbut rather ascritical 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 func-tional sub-groups (prediction ability was not improved by entering all questions at once).
Using the quantitative data, models were produced for each of the selected variable sets one-year-prior to bankruptcy and then tested on the data for the second- and third-years-prior 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,8whereas their predictability is then tested on the other years.9
8A 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
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 asfailedor 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 thePhase-In Algorithmdeveloped 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 yeart + 5 tot + 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 yeart + 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 in-cluding yeart + 2,t + 3 and t + 4 and then as failed counting yeart + 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 non-distressed (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).
11
The pseudo-R2is 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 R2in multiple linear regression, simulated as pseudo-R2in logistic regression.
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 representinglack 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) andgroup 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, presentand future time dimensions. 12 Con-sistency 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
environmentvariable 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. Table 4
Results of logistic regression for Part I
Model 1.1 Statement b
SUCC The airline's success is largely dependent on factors out of its control ÿ1.0158
CUST We ful®ll our customer' needs well 1.4569
MARK Our marketing is aggressive ÿ1.1002
CAPI Lack of capital will not limit our growth 1.7224
AIMS Everyone in our airline understands our long-term aims and objectives 1.1385 REWA Employees are rewarded for taking actions that bene®t our customers ÿ1.4643
CONS Group consensus is the usual way we make decisions ÿ0.7493
Constant ÿ0.9600
ÿ2 log likelihood 30.159 Goodness of ®t 30.810
D 95.83
ND 85.71
Overall 91.11
Model chi-square 32.024
Pseudo-R2 0.51
n D24 ND21
p<0.001;p<0.01;p<0.05.
Table 5
Results of logistic regression by variable sub-groups, Part II
Model Operations Management Marketing
(2.1) Past (2.2) Present (3.1) Past (3.2) Present (3.3) Future (4.1) Past (4.2) Present (4.3) Future
Past Hub and
Constant 0.5926 ÿ4.8343 0.7047 ÿ0.1557 ÿ5.6825 ÿ2.6641 0.5289 ÿ4.4099
ÿ2 log likelihood
25.122 35.083 33.104 31.194 40.250 24.114 32.907 37.506
Goodness of ®t
25.266 30.697 39.612 28.297 35.080 27.902 48.363 37.637
D 83.33 78.95 78.95 80.00 71.43 88.24 70.59 73.68
ND 86.67 77.78 80.0 70.59 64.71 86.67 82.35 70.59
Overall 84.85 78.38 79.41 75.68 68.42 87.50 76.47 72.22
Model chi-square
20.353 16.183 13.558 19.855 12.007 20.122 14.227 12.289
Pseudo-R2
0.45 0.32 0.29 0.39 0.35 0.45 0.30 0.25
n D18 ND15 D19 ND18 D19 ND15 D20 ND17 D21 ND17 D17 NL15 D17 ND17 D19 ND17
p<0.001;p<0.01;p<0.05;p<0.1.
Factors were rated on a scale from 0 (no importance) to 10 (highly important).
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 thepastmodel set, the best classifying result was obtained in theMarketing 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 theManagement Model(3.1) produced 79.41% overall classi®cation. The pseudo-R2 ranged from 0.45 for theoperationsand Marketing Modeldown to 0.29 for theManagement
Model. All coecients were statistically signi®cant (p <0.05) exceptalliance with the incumbents (ALLI), in the Marketing Model. Three coecients were highly signi®cant;hub and spoke oper-ations (HUB1, p<0.01), in the Operations Model, job rotation (JOB1, p<0.01) in the Man-agement Model and service quality (QUAL, p <0.01) in the Marketing Model. ALLI had signi®cant dierence of the means between US and EU respondents (mean EU3.9, USA2.4,
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 theMarketing Model(4.2). TheOperations 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 theManagement Modeland 17.65% for theMarketing Model. All the coecients are statistically signi®cant (p<0.05), with the exception ofaircraft 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 (mean5.4) and EU respondents (mean3.8) and so did MASH (mean EU6.2, USA4.5,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 theMarketing Model (4.3) had a Pseudo-R2 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, com-pared 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) andexpansion into new markets(EXPA) are highly signi®cant (p< 0.01). The overall conclusion for the qualitative models is that theOperations, MarketingandManagement Modelsyield the best results,14 with theMarketing Model showing the best overall classi®cation accuracy over the three time dimensions.
14
Table 6
Models for variable sets one to three-years-prior
Model Financial (5.1) Non-®nancial (6.1)
One year Two years Three years One year Two years Three years
Revexÿ13.8773 ÿ3.2898 2.5342 Average1ÿ0.0396 0.0043 ÿ0.0143
ADV_Fixeÿ8.7176 ÿ0.0747 ÿ2.1062 Aircraftÿ0.3580 ÿ0.1456 ÿ0.1034 Total_De21.4537 4.8552 11.6443 Distanceÿ0.0231 ÿ0.0086 0.0002 Long_Terÿ22.4908 ÿ3.8757 ÿ11.819 Aircraf10.2345 0.1419 0.1297
Average30.0020 0.0003 0.0008
Load_Facÿ20.096 ÿ8.1492 ÿ14.0717 @_Non_Scÿ12.9935 ÿ1.4555 1.1717 Change_2ÿ9.1595 4.5861 2.7637
Constant 10.7039 1.3234 ÿ4.5059 30.9817 9.8932 7.9386
-2 log likelihood 14.098 38.907 39.730 25.779 34.621 36.593
Goodness of Fit 20.428 30.848 32.026 25.846 30.552 31.440
Classi®cation
F 94.44 66.67 61.11 83.33 77.78 72.22
NF 94.44 72.22 72.22 83.33 72.22 72.22
Total 94.44 69.44 66.67 83.33 75.00 72.22
Model chi-square 35.809 11.00 10.176 24.128 15.286 13.314
Pseudo-R2 0.72 0.22 0.20 0.48 0.31 0.27
n, F18 NF18
p<0.001;p<0.01;p<0.05;p<0.1. Please refer to Table 2 for the key to the variable names.
168
S.
Vidar
Gudmundsson
/
Transportation
Research
Part
E
35
(1999)
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 aFinancial (5.1) andNon-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 theNon-Financial Modeland good (0.74) for the Financial
Model. In the Financial Modeltwo prediction coecients were signi®cantADV_Fixe(p <0.05), Total_De(p< 0.05), one had weak signi®canceLong_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) Aver-age1,Aircraft,Aircraf1andLoad_Fac. While two had weak signi®cance (p< 0.1) @_Non_Scand Change_2, and Average3was non-signi®cant.
Two-years-prior to failure the overall classi®cation was reduced for all models, while the Fi-nancial and Non-Financial Models gave the best results, 69.4% and 75.0% overall classi®cation, respectively.15 The type I error was quite dierent between the two models, 33.3% and 22.2%, respectively, while the type II error was 27.8% for both models. The pseudo-R2is adequate for the
Non-Financial Model, but yields non-signi®cant model chi-square, while theFinancial Modelhad signi®cant (p <0.05) chi-square but low pseudo-R2.
Three-years-prior, theNon-Financial Modelis providing better results than theFinancial Model, producing 72.2% overall classi®cation compared to 66.67% for the latter model. The model chi-square for the Non-Financial Model was not signi®cant although the classi®cation result was superior to the statistically signi®cant Financial Model. The pseudo-R2 indicates that
Non-Fi-nancial (0.27) is performing better than theFinancial 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 ofp1 denotes failure,p0 non-failure, andp0.5 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 ofp > 0.5 renders the airline distressed or failed andp <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
formity of all four models presented was 86.5%. Conformity within individual models ranged from 70% to 100%, with the quantitative Non-Financial Modelhaving 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%. Table 7
Comparison of predicted probabilities for qualitative and quantitative models
Airline #, status Case
109 Non-distressed Subject a 0.00 0.06 0.03 0.33 100%
115 Non-distressed Subject a 0.01 0.06 0.01 1.00 75%
118 Non-distressed Meanp0.37 Meanp0.00 0.00 0.20 100%
Subject a 0.02 0.00
Subject b 0.83 0.00
Subject c 0.27 0.00
120 Non-distressed Subject a 0.08 0.00 0.19 0.01 100%
122 Non-distressed Meanp0.53 Meanp0.03 0.01 0.99 50%
Subject a 0.80 0.00
301 Failed Subject a 1.00 0.82 0.63 0.08 75%
304 Failed Subject a 1.00 1.00 1.00 0.28 75%
306 Failed Subject a 1.00 0.93 0.94 1.00 100%
307 Failed Subject a 0.93 0.99 1.00 1.00 100%
313 Failed Subject a 0.84 1.00 0.98 0.57 100%
Overall classi®cation
93% 97% 100% 70% 86.5%
pProbability of failure, wherep1 (failure/distress) andp0 (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. Meanpis the calculated average of predicted probabilities shown below the number.
Examining individual respondents it is evident that there is general conformity between pre-dicted probabilities of individual respondents in the questionnaire survey and the prepre-dicted 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 dif-ferent 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 method-ology 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 theGeneral 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 theFinancial(5.1) andNon-Financialmodels 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 turn-around.
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 qualitativeGeneral 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. Statement REWA has a higher mean for distressed new-entrants, although one would believe on a priori that this should be a reverse sign. The reason is likely an attempt to turnaround (related to the failure process), which would logically create increased focus on this aspect of the airline's activity.
Although the ®rst four factors listed above are good in segregating the two groups of carriers, it does not indicate that these aspects should be de-emphasized by the airlines. Rather it is an indication that distressed carriers emphasize those factors more, implying association with distress but not necessarily a causal relationship. This means that these factors are critical, but the questionnaire ®ndings support rather a failure process association than causal rela-tionship. MASH is important, but only on the micro-level, meaning that overall market-share is not important, but market-share at speci®c airports and routes may be important (Gud-mundsson, 1998b).
The qualitative operations models (2.1, 2.2) of Part II came up with three critical factors UTIL, HUBi (i1, 2) and OPSi (i1, 2). Much emphasis on UTIL is characteristic of non-distressed carriers, resulting in lower cost structure as high aircraft utilization aects costs in almost all aspects. Conversely, the non-distressed carriers emphasized HUBi to a much lesser degree than distressed carriers. That ®nding reinforces the importance of aircraft utilization as hub and spoke operations lead to less aircraft utilization than direct service as the success of Southwest Airlines has shown. The models provided one more operations critical factor OPSi, which is positively related to non-distress, implying that freight operations are an operating feature of non-distressed carriers. This implies that greater utilization of the aircraft by carrying freight as a byproduct is an important revenue source for new-entrant airlines, reinforcing non-distress.
The qualitative management models (3.1, 3.2 and 3.3) of Part II highlighted four critical factors ORGA, FLEX, PROD and JOBi (i1, 2, 3). The ®rst three are positively related with non-distress while the last coecient, job rotation is negatively related. ORGA poses a diculty in interpretation as the variable is more highly rated by distressed carriers but has positive sign in the prediction model implying positive relationship with non-failure. What is more it has high cor-relation with JOB1 implying thatjob rotationanddecentralized organization structureare likely to go hand in hand. JOBi on the other hand has negative sign in all models implying strong critical characteristics. Too frequent changes in employees' job responsibilities creates diculties for the airline due to lack of build up in specialization within operating areas. There is also another possible explanation dealing with the failure process, namely that job rotation is used as one of the tools to turnaround the airline. If the latter explanation is accepted, one cannot establish whether such practice is working eectively or not. However, it is well known that in a turnaround situ-ation drastic reshuing of the management team is not uncommon. FLEX implies that ¯exible job descriptionsare positively related with non-failure just as PROD,employees' productivity, but these two variables are obviously highly related as ¯exible job descriptions has the precise aim of improving employee productivity.
6.2. Interpretation of quantitative models
Signi®cant ®nancial predictor variables that emerged were the following ratios: ADV_Fixe,
Total_DeandLong_ter. These results indicate that the proportional earnings capability of a new-entrant in relation to sales, expenses, ®xed assets and output is critical. However, such factors should be viewed as symptomatic rather than causal, making the non-®nancial variables of a greater interest to a manager of an airline looking for causal relationships. The general conclusion for the ®nancial ratios is thatgearingandrevenue generated as proportion of resources appliedare critical indicative (symptomatic) factors.
The non-®nancial model came up with three signi®cant critical factors associated with mar-keting:Load_fac, @_Non_ScandAverage1(average aircraft size). TheLoad_faccoecient had a negative sign meaning that there is negative relationship with non-failure. This is because many of the failed carriers had relatively high load-factors, achieved through low fares. The second factor, @_Non_Sc, is related to failure. This means that the less the carrier is involved in charter oper-ations the better. The reloper-ationship is only to an extent, as charter operoper-ations as pure by-product cannot harm the pro®tability of the carrier, but scheduled operations as a by-product, probably does harm the carrier involved because of possible incompatibilities of such operations (larger average equipment size and assigned priorities in the route network). The last factorAverage1is negatively related, indicating that carriers operating smaller equipment fared better than those operating larger equipment. The in¯uence of the former charter-based new-entrants is probably re¯ected in this ®nding, due to their very poor results.
Signi®cant non-®nancial factors related to operations were: Aircraft, Aircraf1 and Distance. The ®rst two are eciency ratios of operations in terms of employee input. This aspect comes up again and again showing that aircraft utilization is important in running a competitive airline. However, the previous ratio has negative sign indicating that a high ratio of aircraft departures per employee is positively related to failure. Carriers having relatively many departures per em-ployee have therefore been more prone to bankruptcy compared with those having a lower ratio. It is possible that this inverse relationship is caused by poorer service quality of such carriers. However, further research must be carried out to establish such a relationship. The latter ratio,
Aircraf1has positive sign, that indicates, as expected, that an aircraft in the air is more productive than an aircraft on the ramp. However, one can expect airlines having longer average sector lengths to have more ¯ight hours per employee than short-haul carriers. There was only one signi®cant environment factor derived from the database models: Change_2. The negative sign shows that a rise in spot aviation fuel prices is associated with failure.
6.3. Comparison of qualitative and quantitative models
conformity among respondents within airline 108 and 118. Such cases indicate highly diering views on the airline's current situation and approach, necessitating further research into the possible cause. If, for example, the manager is new to the airline, he may not have adequate information to assess the current situation, a factor that could be examined by asking the re-spondents to state the number of years/months they have been with the airline. If no such link can be established, respondents showing deviation from colleagues must be eliminated from the analysis as extreme outliers, given enough responses from a particular airline.
Table 8
Airlines included in the quantitative models
Airlinea Status
1 Carnival 0
2 Air California 0
3 Air Wisconsin (1) 0
4 Air Wisconsin (2) 0
5 America West (1) 0
6 Horizon Air (1) 0
7 Horizon Air (2) 0
8 Midway Airlines (1) 0
9 Midwest Express 0
10 New York Air 0
11 PSA-Paci®c Southwest 0
12 People Express (1) 0
13 Southwest Airlines (1) 0
14 Southwest Airlines (2) 0
15 Southwest Airlines (3) 0
16 Tower Air (1) 0
17 Tower Air (2) 0
18 World Airways (1) 0
19 Air Atlanta 1
20 Air Florida 1
21 Air Wisconsin (3) 1
22 America West (2) 1
23 Arrow Airways 1
24 Aspen 1
25 Brani II 1
26 Capitol Air 1
27 Empire Airlines 1
28 Florida Express 1
29 Jet America 1
30 Midway Airlines (2) 1
31 Muse Air 1
32 New York Air (2) 1
33 People Express (2) 1
34 Presidential Airways 1
35 Sunworld International Airways 1
36 World Airways (2) 1
a Airlines participating in the questionnaire survey cannot be named due to con®dentiality.
0Non-failed, 1Failed.
7. Summary
The study shows that all the sub-sets of variables provide dierent classi®cation accuracy. This result is valid for thepast,presentandfuturevariable sub-sets of the questionnaire survey Part II. The same conclusion was obtained for the quantitative sub-sets tested. A qualitative prediction model based on marketing variables provided the best overall performance along three time di-mensions. It became also clear in this research that respondents of qualitative statements show less variation in views when asked to rate various factors into the future (projection) compared with past (experience). The reason for this is reduced information and uncertainty associated with the unknown. The shift in emphasis among individual variables should, however, indicate future trends in management emphasis, given a large enough sample of respondents.
The results of qualitative model 1.1 indicates that managers of non-distressed new-entrant airlines feel that the airline's marketing is not aggressive, that capital is available to ®nance growth, that employees understand long-term aims and objectives, but at the same time they do not feel that employees are speci®cally rewarded for taking actions that bene®t the customers.
The results of qualitative models 2.1 and 2.2 indicate that at non-distressed new-entrant carriers less importance is placed on hub and spoke operations but more on freight operations and aircraft utilization than at distressed carriers. Qualitative models 3.1±3.3 indicate that less job rotation occurs at non-distressed carriers than distressed carriers, and more importance is placed on de-centralized organization structure, ¯exible job descriptions and employees' productivity. Quali-tative models 4.1±4.3 indicate that at non-distressed carriers, less importance is placed on alliances with the incumbents, market-share, market research and frequent ¯yer programmes, but more importance on service-quality, media advertising and expansion into new markets.
The results for quantitative model 5.1 shows that lower gearing and higher revenue generation as proportion of resources applied is characteristics of non-failed carriers. Turning to quantitative model 6.1, we see that during the non-failed phases of the carriers' life-cycles they were likely to have had relatively lower load-factors, done less charter-operations, operated smaller equipment, had higher aircraft utilization and fewer departures but more aircraft hours per employee. Finally, failures were more likely to have occurred during increases in spot fuel prices.
The qualitative models reinforce the quantitative models, implying their viability for use for distress and failure prediction and a research tool to identify variable relationships with failure or distress and deeper understanding of the failure process. Such qualitative models upon adequate standardization are especially viable as an internal or external monitoring device for businesses. The main limitations of this research was the small population making a holdout sample im-possible in order to test the external validity of the models, a standard practice in this area. In addition, the management constructs in Part II of the questionnaire need to be developed further on the basis of relationships with failure and relative importance. A potentially valuable meth-odology in that regard would be conjoint analysis that would establish the importance of each factor more precisely. The research could not be extended to use the extra information content (use of dummy variables) that was achieved by combining the models due to the small population size, necessitating further research using this methodology on a larger population.
un-Table 9
Part I ± Descriptives
n45 95% CI
Questionnaire indicator Mean SD SE Lower Upper
Our organisational structure was decentralised 2.78 1.52 0.23 2.32 3.23
Our ®nancial control system was ecient 3.96 1.19 0.18 3.60 4.31
The airline had a vision of the future shared by all the employees 3.53 1.31 0.19 3.14 3.93 We surrounded ourselves with sta who promoted dierent
rientations and points of view
3.80 1.06 0.16 3.48 4.12
Group consensus was the usual way we made decisions 3.29 1.38 0.21 2.88 3.70 The airline's success was largely dependent on factors out of its
control
2.44 1.29 0.19 2.06 2.83
The number of serious problems we were faced with increased constantly
3.02 1.13 0.17 2.68 3.37
Our sta was encouraged to have open discussion about the airline's problems
4.16 1.04 0.16 3.84 4.47
We did detailed analysis before taking any major decision 3.89 1.34 0.20 3.49 4.29 We made changes in our service quite frequently 3.02 1.47 0.22 2.58 3.47 We usually had enough resources to plan for the future 3.02 1.41 0.21 2.60 3.44
Our customer loyalty was strong 4.07 1.18 0.18 3.71 4.42
We ful®lled our customer' needs well 4.42 0.58 0.08 4.25 4.60
We acted immediately upon customer complaints 4.00 1.09 0.16 3.67 4.33
Our marketing performance was good 3.93 0.96 0.14 3.64 4.22
Our long-term aims and objectives were easily achievable 2.66 1.20 0.18 2.29 3.02
Our marketing was aggressive 3.80 1.01 0.15 3.50 4.10
We usually received many useful suggestions from our employees 3.71 1.04 0.15 3.40 4.02 We were more ecient than most of our competitors 4.31 1.10 0.16 3.98 4.64 We tried to avoid head to head competition with our larger
competitors
3.42 1.56 0.23 2.95 3.89
Our long-term aims and objectives guided our business decisions
3.78 1.15 0.17 3.43 4.12
Our airline was ¯exible enough to respond immediately to major opportunities
4.62 0.81 0.12 4.38 4.86
We were pleased with the performance of our distribution outlets 3.55 0.87 0.13 3.28 3.81 We were innovators in customer service compared to our
competitors
3.91 1.24 0.19 3.53 4.28
Lack of capital did not limit our growth 2.60 1.40 0.21 2.18 3.02 Everyone in our airline understood our long-term aims and
objectives
3.16 1.36 0.20 2.75 3.57
We were constantly identifying threats and opportunities to our business
4.20 1.04 0.15 3.89 4.51
The improvement of the airline's market-share was our number one priority
3.05 1.49 0.23 2.59 3.50
We were rarely taken by surprise by our business environment 3.25 1.22 0.18 2.88 3.62
We emphasised planning for the future 3.56 1.29 0.19 3.17 3.94
We grew by selling our services to more customers 4.38 0.91 0.14 4.10 4.65 We allocated major resources for diversi®cation into other
industries
1.39 0.87 0.13 1.12 1.65
We planned for and allocated sucient resources to developing new markets
derlying theory does, however, not render the results useless, because the search for good pre-dictors of distress and failure is important, what is more to link purely quantitative models with qualitative models may facilitate the emergence of an underlying theory (given enough repetition of this method). A logical point is that `relationship' or `association' with failure does not nec-essarily imply `causal' relationship with failure. As a result, the qualitative model is build on the basis of intensity of the rating meaning that both groups may be emphasizing a factor (customer service) heavily although the failing group may do so to a greater extent. That in fact poses a diculty for interpretation but can be considered either as an indicator of turnaround eort or extreme emphasis that can be harmful (see Miller, 1990).
To sum up, the research makes a number of contributions such as: (i) the small population
Phase-In Algorithm; (ii) it shows the viability of qualitative models and their performance in providing indication of model predictor's relationship with distress; (iii) it shows that when constructing quantitative failure or distress prediction models for airlines, such models should take into account strategies that have major impact on cost structures; and (iv) models based on Table 9(Continued)
n45 95% CI
Questionnaire indicator Mean SD SE Lower Upper
Long-term prospects in our primary markets were excellent 3.93 1.18 0.18 3.58 4.29 We had customer-oriented front-line people 4.36 0.98 0.15 4.06 4.65 Our service had a range of features that made it distinctive 4.11 1.11 0.17 3.78 4.45 Quality was our major competitive advantage 3.82 1.15 0.17 3.48 4.17 Everyone in our airline understood how they could improve quality 3.40 1.25 0.19 3.02 3.78 We were eective in monitoring our customers' expectation of quality 3.77 1.21 0.18 3.39 4.14 Our internal social and political system supported our business aims 3.50 1.21 0.18 3.13 3.87 We were good at changing our sta's beliefs and values 3.13 0.89 0.13 2.86 3.40 Our sta provided us with a competitive advantage 4.18 0.89 0.13 3.91 4.44 We had incentives for our sta that encouraged extra commitment 3.09 1.26 0.19 2.71 3.47 We had all the information we needed on our customers, markets
and opportunities
2.69 1.04 0.16 2.38 3.00
Employees were rewarded for taking actions that bene®ted our customers
3.24 1.21 0.18 2.88 3.61
Our information system provided us with a clear competitive advantage
2.60 1.05 0.16 2.28 2.92
We were constantly upgrading and improving our information system
3.84 1.07 0.16 3.52 4.16
We would create the same organisation structure as we had, if given the opportunity
3.38 1.40 0.21 2.96 3.80
We made eective cash-¯ow forecasts 4.29 1.01 0.15 3.98 4.59
We were good at stimulating demand for our services 4.14 0.88 0.13 3.87 4.40 We were eective in monitoring important cost areas 4.16 1.15 0.17 3.81 4.50 Important information was communicated to employees to enable
eective decision-making
3.82 1.15 0.17 3.48 4.17
The board of directors was highly involved in the airline's aairs 3.89 1.32 0.20 3.49 4.28 The atmosphere among employees was very good 3.87 1.14 0.17 3.52 4.21 Our information systems provided quick, accurate and relevant
information
Table 10
Part II ± Descriptives
n43±51 95% CI
Construct Questionnaire indicator Mean SD SE Lower Upper
In¯uence of external
Scale reliability0.76 In¯uencing government policy on aviation
Competitor analysis 5.64 3.13 0.44 4.75 6.53
Information- and communication system
Computer reservation system 6.16 2.21 0.31 5.53 6.78
Interdepartmental communication 5.88 2.28 0.32 5.24 6.52 Market-intelligent
information-Scale reliability0.85 Planning systems (integration of information to prepare business
Yield management system 5.02 3.19 0.45 4.12 5.92
Simpli®cation of information-and communication systems
4.80 2.20 0.31 4.17 5.43
Operations management Aircraft utilisation 7.78 1.88 0.27 7.35 8.41 Matching of aircraft size with
route requirement
6.64 2.58 0.36 5.91 7.37
Homogeneous aircraft ¯eet 6.43 2.56 0.36 5.71 7.15
Scale reliability0.83 Frequency in served markets 6.35 3.05 0.43 5.49 7.21 Acquisition of airport slots 6.02 3.26 0.46 5.10 6.94 Acquisition of new aircraft 5.98 2.94 0.41 5.15 6.81 Quality of terminal space and
ground facilities
5.55 2.35 0.33 4.89 6.21
Operation on trunk routes 5.10 3.20 0.46 4.18 6.03
Interlining agreements 5.06 3.03 0.42 4.21 5.91
Table 10(Continued)
n43±51 95% CI
Construct Questionnaire indicator Mean SD SE Lower Upper
Hub and spoke operations 3.98 3.54 0.51 2.96 5.00
Long haul routes 3.90 3.75 0.53 2.83 4.97
Freight operations 3.08 2.45 0.35 2.38 3.78
Feeder airline agreements 2.84 2.55 0.36 2.12 3.56
Code sharing 2.62 2.83 0.40 1.82 3.42
General management and organisation
Employees' productivity 7.80 2.21 0.31 7.18 8.43
Employee relations 7.20 2.38 0.33 6.53 7.87
Flexible job descriptions 7.00 2.45 0.34 6.31 7.69 Operations without unionised
sta (where possible)
6.90 3.19 0.46 5.98 7.81
Scale reliability0.84 Company culture (shared attitudes, beliefs, norms)
6.80 2.73 0.38 6.04 7.57
Business strategy 6.66 2.50 0.35 5.95 7.37
Management's external contacts
Delegation 5.45 2.50 0.36 4.73 6.17
Employees' autonomy to take decisions
5.32 2.27 0.32 4.67 5.97
Managers' incentive programme 5.18 3.09 0.43 4.31 6.05 Employees' incentive programme 5.00 3.16 0.44 4.11 5.89
Union relations 4.20 3.62 0.51 3.17 5.23
Job rotation 4.20 2.99 0.43 3.35 5.06
Sta reduction 3.81 2.86 0.41 2.98 4.64
Decentralised organisation structure
3.72 3.01 0.43 2.86 4.58
Financial management Cost control 7.73 2.41 0.34 7.05 8.40
Cost reduction 6.71 2.86 0.40 5.90 7.51
Fuel costs (fuel ecient aircraft. etc.)
6.68 2.20 0.31 6.06 7.30
Scale reliability0.76 Increase margins 6.59 2.49 0.35 5.89 7.29
Turnover growth 5.75 2.56 0.37 5.01 6.49
industry-speci®c non-®nancial factors of airlines perform better further away from failure than ®nancial factors. Finally, it is hoped that this research has raised questions with respect to the failure process, causality and theory development that will stimulate research activity in this important ®eld.
Acknowledgements
The author would like to thank Prof. Rigas Doganis and Dr. Fariba Alamdari, the editor Prof. W.G. Waters II and the two anonymous reviewers for helpful comments on earlier drafts of this work. Any remaining errors and omissions are the responsibility of the author.
Appendix A
In Table 8 we present airlines included in the quantitative models.
Appendix B
Tables 9 and 10 represent Parts I and II descriptives of the questionnaire survey among senior airline managers, respectively.
Table 10(Continued)
n43±51 95% CI
Construct Questionnaire indicator Mean SD SE Lower Upper
Long-term rather than short-term pro®ts
5.49 3.06 0.43 4.63 6.35
Debt reduction 5.43 3.04 0.43 4.55 6.30
Reduction of labour costs 5.34 2.75 0.39 4.56 6.12
Marketing management Passenger load factors 7.06 2.12 0.30 6.46 7.66 Expansion into new markets 7.04 2.57 0.36 6.31 7.77 Price leadership in served markets 6.86 3.09 0.44 5.98 7.74
Scale reliability0.86 Service quality 6.73 2.68 0.38 5.97 7.48
Brand image 6.50 2.72 0.38 5.73 7.27
Media advertising 6.20 2.61 0.37 5.46 6.94
Promotion 6.12 2.30 0.33 5.47 6.77
Distribution network 5.76 2.27 0.32 5.10 6.41
Business passengers 5.75 3.19 0.45 4.85 6.64
Market research 5.74 2.40 0.34 5.06 6.42
Avoidance of price wars 5.00 3.22 0.46 4.08 5.92
Market share 4.94 3.04 0.43 4.09 5.80
Weight load factor 4.81 2.81 0.43 3.95 5.68
Commission overrides 4.43 2.52 0.36 3.70 5.14
Frequent ¯yer programmes 3.59 3.33 0.47 2.65 4.52
Alliance with the incumbents 3.17 2.63 0.38 2.40 3.93 Merger/acquisition to gain market
share
2.84 3.00 0.43 1.98 3.70
