ACCOUNTING AND FINANCE RESEARCH UNIT

An international survey of performance

measurement and benchmarking by airlines

Jackie Fry* Graham Francis+ Ian Humphreys♦

September 2004

04/05

ISBN 0 7492 01460 © Jackie Fry, Graham Francis and Ian Humphreys *Dr Jackie Fry, Open University Business School, Walton Hall, Milton Keynes, MK7 6AA Email: J.Fry@open.ac.uk Tel: 01908 659239 Fax: 01908 655898 +Graham Francis, Department of Accounting, Waikato Management School, Waikato University Private Bag 3105, Hamilton, New Zealand Email: GAJF@waikato.ac.nz ♦Dr Ian Humphreys, Transport Studies Group, Department of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK Acknowledgements We are grateful to all those airline managers who contributed to this study. We would also like to thank Jacky Holloway for her support.

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Abstract This working paper describes the nature and prevalence of the use of performance measurement techniques by airlines. The authors draw on evidence from an international survey of the largest 200 airlines in terms of total passengers carried. The results provide empirical insight into the nature and prevalence of performance measurement, benchmarking activities and other performance management techniques. The surveys revealed a very high utilization of benchmarking and quality management techniques by airlines, and evidence that certain measures are considered of more use than others by the airline managers. 1. Introduction

December 2003 marked the 100th anniversary of the first heavier-than-air controlled, powered flight by the Wright Brothers. Since this time there has been huge development and expansion of the aviation industry. However, despite the growth in their business, airlines face substantial commercial pressures. They face challenging, dynamic market environments that in the short term are extremely sensitive to the world economic and political situation. Long term growth of around 4.5 per cent per annum in air traffic has been forecast (ACI, 2003). However, events such as September 11th, the SARS outbreak and poor economic conditions of the early 2000’s have seen an overall stagnation and reduction of traffic during the period 2001 to 2003, although some market sectors have performed better. Historically airlines have made very low margins, 8 per cent on average. The pressures from competition, deregulated market forces, the decline of average yields per passenger and, in certain regions, the challenge from low cost airlines has presented management with the problem of how to improve airline economic performance. This paper seeks to identify the nature and prevalence of the performance improvement techniques adopted by airline managers in response to these pressures. This working paper has the following structure. In order to place our survey in context, the next section outlines the importance of performance measurement to airlines. This is followed by a section outlining the methods used to collect data and a section on the demographics and non-response bias of the survey. The results of the survey are then described and some conclusions drawn. 2. Performance measurement of airlines

The continuing speed of change and rapid growth have resulted in a complex array of challenges for managers including: increasing congestion of infrastructure, safety, sustainability, environmental and social opposition to aircraft operations, airport and air traffic privatization and commercialization, alliances and mergers between airlines, deregulation of markets, the operation of new larger aircraft and the continued rise of low cost carriers. Such pressures have led managers, planners and regulators to use a variety of performance management techniques to measure and manage performance.

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The importance of performance measurement to monitor operational, safety and financial aspects of performance has been long recognised. Performance data is required to evaluate customer response to services and to maintain management control of geographically disparate route networks (Doganis 2001; Shaw 1999; Hanlon, 1999). Performance measurement data is frequently collected electronically and managed via a series of databases. The range and volume of data has increased with much of it collected and collated electronically and fed into databases that are accessible to teams of analysts and decision makers. Many airlines are fed detailed information on the aircraft’s performance of each flight via each aircraft’s mandatory Flight Data Recorder (FDR). This operational information is downloaded and used by management to identify performance improvements that can be made and to highlight specific operational problems on certain sectors1. With the agreement of a ‘no blame’ regime, certain airlines use this data to identify safety issues and the need for pilot training/retraining. Operations data is an example of performance data that is often collected in real time, reviewed by an operations department and used to manage flight operations, but is also reviewed by network planning analysts to feed medium and long term planning decisions (Doganis, 2001, 2002; Kirkland et al., 2003; Caves and Gosling, 1999). Airline alliances2 , franchise agreements and code share3 agreements have led to airlines signing agreements with each other that require certain service levels and safety standards to be achieved. Major airlines have undertaken such agreements to maintain brand quality for customers (Denton and Dennis 2000; Hanlon, 1999). In extreme cases, partner airlines have had to withdraw from code share agreements or change service delivery as a result of ‘audit’ findings from partner airlines. For example, Korean Airlines was suspended from its alliance with Delta and Air France until safety standards were raised to the levels required. Both Delta and Air France compared performance information with Korean Airlines and provided the expertise, knowledge of safety systems and culture to develop the processes required to address possible safety problems (Braithwaite, 2001). Load factor data, yield and other commercial information is collected and fed into a database of commercial information to provide airline management with information upon which to base pricing and capacity decisions in the short, medium and long term. The volatility of the airline service with respect to hourly, daily and seasonal traffic patterns, the impact of competitor behaviour and the sensitivity to economic conditions has made collection of commercial performance data essential to enable management to react to market changes and to survive. Generally speaking, the use and analysis of commercial information is known to be common place, yet the exact way information is used has remained something upon which little academic work has been undertaken, due in no small part to the commercial sensitivity of such information. Examples of benchmarking from the literature include Southwest 1 A sector can be defined as a single air route from landing to take off. 2 Oneworld (including British Airways, Ammerican Airlines, Cathay Pacific, Aer Lingus and Iberia), Star Alliance (including Lufthansa, United, Thai and Bmi British Midland), Sky team alliance including Delta, Air France, KLM, Korean Air and Northwest).

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Airlines learning about the low cost model of airline operation through visits and spending time with Pacific Southwest Airlines in California. Likewise Ryanair spent time with Southwest Airlines to understand how to develop a low cost airline (Calder, 2002). Airline managers have used performance measures for comparing airline performance both within the airline and in relation to the performance of other airlines. The main players will depend heavily on inter-organizational learning if they are to meet the challenges facing them. Cost data comparisons from published sources by organizations such as the International Air Transport Association (IATA), the International Civil Aviation Organization (ICAO), the UK Civil Aviation Authority and periodicals such as Air Transport World and Aircraft Economics are available for use by management to assess comparative performance and as a starting point for exploring the reasons behind the performance differences. Some of these differences can be explained by geographical variation in labour and other input costs. In addition to published statistics a number of reports providing ‘benchmark’ statistics and comparisons of airline performance have been produced (such as Mason, Whelan and Williams, 2000; Morrell, Alamdari and Lu, 2000; TRL, 2002). Quality of service indicators are collected by airlines internally and by IATA’s annual world passenger survey which monitors customer satisfaction with 29 aspects of airline service (IATA, 2002). Each airline can compare itself with the ratings for the rest of the sample to provide a measure of relative performance. Although the literature identifies a range of data collection methods and a comparison of key performance indicators, these techniques and benchmarking activities have oriented towards process improvement within the sector and have not previously been identified in a systematic way. A prime motivation of this study therefore is to address this gap by identifying the relative use of different performance measurement practices by airline managers in response to the challenges they face. 3. Methods

Given the objective of trying to identify the nature and prevalence of the use of performance measurement techniques a questionnaire survey was viewed as the most appropriate way of gathering initial empirical evidence (Oppenheim, 1992). There are limitations inherent in the use of questionnaires as a research method and the authors intend to follow up this research with a number of detailed case studies (Scapens, 1999) of performance management practices with individual airlines. The set of airlines sampled was the largest 200 airlines as ranked by Air Transport World (ATW) in terms of total passengers for 2001 (ATW, 2002). The top 200 were chosen because it represented the major players in the industry who account for over 75 per cent of airline passenger kilometres performed. It is symptomatic of the volatility of the airline industry that at the start of the survey, 12 airlines listed in the top 200 were no longer operating and were

3 Code sharing is when two or more airlines use their own flight codes on a flight operated by one of them.

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therefore deleted from the list. The next 12 airlines still in operation were added to make the sample up to 200. The questionnaires were addressed where possible to the person concerned with flight operations. Where it was not possible to identify a named person, the questionnaire was sent to another named senior person. Each questionnaire sent out was given a unique identification number to ensure repeat mailings were only sent to non-respondents. A copy of the questionnaire (Appendix A) and a covering letter were sent out to airlines on 10 February 2003 and three repeat mailings, 17 March, 28 May and 27 August 2003. Two hundred were sent out in the first mailing. Two remained undeliverable and during the survey period a further two airlines ceased operations. Of the remaining 196 questionnaires, two airlines declined to participate and 43 were returned completed, a response rate of 23 per cent. 4. Demographics and non-response bias

The respondent airlines and the airlines in the sample were classified into geographic regions using the categories as defined in ATW (2002): Africa/Middle East, Asia/Pacific, Europe, Latin America/Caribbean and North America4 (Table 1). The profile of the respondents was then compared to the profile of the overall sample. In order to perform a Chi-square test, the categories of Latin America/Caribbean and Africa/Middle East were combined into a “rest of world” category so that the expected values of the categories were greater than 5. The Chi-square test showed that the profiles of respondent airlines were not significantly different to the profile of the samples at the 5 per cent level (χ2=4.02, ns). The geographic spread of the respondents is a good match to that of the sample. Table 1: Geographic profile of the respondents and the sample

Region Percentage of

sample airlines (N=196)

Percentage of respondent airlines

(N=43) Europe 37 52 North America 21 16 Asia/Pacific 23 16 Latin America/Caribbean 11 7 Africa/Middle East 8 9 The representativeness of the respondents can also be confirmed by examining the profiles of the total number of passengers handled per annum by the sample airlines and the respondents (Table 2). The Chi-square test showed that the profile of respondent airlines was not significantly different to the profile of the sample at the 5 per cent level (χ2=2.19, ns). The

4 In fact ATW distinguishes between Canada, US Majors, US Nationals, US Cargo and US Regional/Specialty. For the purposes of this research these were all coded as North America.

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range of passenger numbers handled per annum by the respondents is a very good match to that of the sample. Table 2: Number of passengers handled per annum by the respondents and the sample Passengers handled /million

Percentage of sample airlines

(N=196)

Percentage of respondent airlines

(N=43) 1 to 4* 61 51 5 to 9 22 26 10 to 19 8 9 20 and above 9 14 * Only includes up to the 200th busiest airline 5. Results

The questionnaire instrument included questions on the use of performance management techniques and metrics in a number of areas such as operations, financial, quality of service and environmental. Each of these will be outlined in the following sections. 5.1 The relative prevalence of performance measurement techniques

The questionnaire instrument included a question aimed at identifying the relative usage made of performance improvement techniques (see Table 3). Benchmarking is the single most used method. Quality management methods5 when looked at in total are also in very common usage. Quality issues and benchmarking will be covered in more detail in sections 5.4 and 5.6 respectively. Table 3: Performance improvement techniques used by respondents

Technique Percentage use

by airlines*

(N=41) Benchmarking 88 Quality Management Systems (e.g. ISO9000/BS5750 orsimilar)

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Balanced Scorecard 44 Business Process Reengineering 39 Activity Based Costing 34 Total Quality Management (TQM) 22 Environmental Management Systems (e.g. ISO14000) 17 Value Based Management 15 Business Excellence Model / EFQM 7 *Note that respondents frequently use more than one method 5 Such as ISO9000/BS5750, Business Excellence Model / EFQM and Total Quality Management (TQM)

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It is noteworthy that benchmarking was identified as the most used performance improvement technique for airlines with 88 per cent of the sample claiming to engage in some form of benchmarking activity. None of our respondents reported using DEA6 although its application to an airline was reported on by Schefczyk (1993). Table 4 provides a comparison to an earlier survey of the world’s busiest 200 airports, in terms of passengers handled, carried out in 2000, in which benchmarking was also the most used performance improvement technique with 72 per cent of airports claiming to undertake benchmarking activity (Francis, Fry and Humphreys, 2001). The authors were surprised by the high reported use of the balanced scorecard with 44 per cent of airlines reporting its use compared to 25 per cent in the airport study. Although the two business sectors are interrelated, they are very different, so direct comparisons between the two industries should be made with care. However, it is interesting to recognise that these distinctly different industries both use benchmarking to improve business performance more so than alternative performance techniques. Table 4: Performance improvement techniques used by responding airlines in comparison to the authors’ earlier study of airport performance measures (Francis. Fry and Humphreys, 2001)

Technique Percentage use

by airlines*

(N=41)

Percentage use by airports*

(N=56) Benchmarking 88 72 Quality Management Systems (e.g. ISO9000/BS5750 orsimilar)

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Balanced Scorecard 44 25 Business Process Reengineering 39 23 Activity Based Costing 34 36 Total Quality Management (TQM) 22 41 Environmental Management Systems (e.g. ISO14000) 17 27 Value Based Management 15 9 Business Excellence Model / EFQM 7 12 *Note that respondents could use more than one method The pressure for improved performance and the dynamic nature of airline management with respect to looking for new ways to measure airline performance is perhaps reflected by the surveys finding that 62 per cent of the airlines that responded to the survey had introduced new performance measures within the last two years. The size of airline does seem to impact on their use of performance measurement techniques. In general, as expected, larger airlines are more likely to engage in performance measurement.

6 Data Envelopment Analysis (a form of linear programming)

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This is most noticeable in the uptake of benchmarking. The larger the organization the more likely there will be the resources to benchmark. A note of caution, the pattern is in part distorted by geographical differences between airlines being interrelated with airline size (Table 5). Table 5: Use of performance measurement techniques in relation to airline size Passengers handled per annum /million

1 to 4* 5 to 9 10 to 19 20 and above

Overall weighted average

Percentage benchmarking 80 91 100 100 88 Quality Management Systems (e.g. ISO9000/BS5750 or similar)

35 82 50 67 54

Balanced Scorecard 30 54 50 67 44 Business Process Reengineering 20 55 50 67 39 Activity Based Costing 35 36 0 50 34 Total Quality Management (TQM) 10 36 25 33 22 Environmental Management Systems (e.g. ISO14000)

0 27 50 33 17

Value Based Management 15 9 50 0 15 Business Excellence Model / EFQM 0 9 25 17 7 * Only includes up to the 200th busiest airline Table 6 shows the use of performance measurement techniques in relation to region. This highlights a different propensity to use various methods around the world. The lack of use of Environmental Management systems among North American airlines was a surprising finding given that the largest of these operate into congested hub airports. However, perhaps the formalised system of environmental management for these airlines is not prioritised to the same extent as for European and Asian carriers where the political pressure to engage in proactive community and sustainability practice has been very intense over the last decade and is set to continue to become even more significant with traffic growth (Upham, 2003). Perhaps less surprising is that the use of the EFQM model is restricted to Europe. When examining performance improvement techniques used in relation to ownership, the results are not necessarily what might have been expected as there is a tendency for those airlines with a government stake in ownership to make greater use of performance improvement techniques. This may in part be explained by the pressure of accountability of governments that still have an ownership stake in an airline (see Table 7) in order to demonstrate that the tax paying public are receiving good value for money.

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Table 6: Use of performance measurement techniques in relation to region Region Europe North

America Asia / Pacific

Latin America / Caribbean

Africa / Middle East

Overall weighted average

Percentage benchmarking 95 86 86 67 67 88 Quality Management Systems (e.g. ISO9000/BS5750 or similar)

65 29 57 33 50 54

Balanced Scorecard 45 29 42 50 67 44 Business Process Reengineering 25 57 57 33 50 39 Activity Based Costing 40 29 29 0 50 34 Total Quality Management (TQM)

20 0 43 0 50 22

Environmental Management Systems (e.g. ISO14000)

20 0 29 0 25 17

Value Based Management 20 0 14 33 0 15 Business Excellence Model / EFQM

15 0 0 0 0 7

Table 7: Performance improvement techniques used in relation to ownership

Technique

Percentage use by airlines with a

government stake in ownership (N=19)

Percentage use by airlines without a

government stake in ownership (N=22)

Total percentage

(N=41)

Benchmarking 100 78 88 Quality Management Systems (e.g. ISO9000/BS5750 or similar)

74 36 54

Balanced Scorecard 58 32 44 Business Process Reengineering 53 27 39 Activity Based Costing 42 27 34 Total Quality Management (TQM) 37 10 22 Environmental Management Systems (e.g. ISO14000)

32 5 17

Value Based Management 16 14 15 Business Excellence Model / EFQM 11 5 7

*Note that respondents could use more than one method Many governments have reduced their stakes in their airlines, and in particular geographic regions, such as Europe, the subsidy of a state carrier has been outlawed (Doganis 2001, 2002). Governments have seen that countries with privatised airlines and independent operators run airlines at break even or above and have begun to run their own airlines to

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commercial objectives. Some have tried to make their airline attractive to private investors, with a view to further reducing their stake. In a number of cases there has been increased accountability to government and an increased pressure for commercial viability of the airline. This push for improved viability could be driving the increased emphasis of these airlines on performance improvement techniques and increased levels of performance measurement. The choice of techniques used is also influenced to varying degrees by the use of management consultants. Table 8: The influence of management consultants on the introduction of new performance measures Level of influence of management consultants

Percentage of respondents (N=34)

Very influential 14 Some influence 31 Not influential 26 Management consultants not used 29 5.2 Operational performance measures

The survey illustrated an interesting uptake of operational performance measures. The responses are summarised in Table 9. It was expected that load factor 7 and punctuality indicators would be seen as important, but the use of turnaround time was lower than expected. However, the sample was of the largest 200 airlines; if the sample had been of low cost airlines, turnaround time may have had a higher priority. Output measures of magnitude rather than efficiency tended to be widely used but not rated as useful as efficiency indicators8. In line with the cost conscious nature of the airline business environment, 90 per cent of respondents used cost per seat kilometre as a measure. Overall this was the measure seen as most useful to managers. Interestingly, although ‘belly hold’ freight 9 has become more important in recent years, measures that reflect airline performance in relation to freight, such as tonne kilometre and work load Units (WLU)10, were relatively infrequently used. However, those managers using them reported them to be of value. Average fleet age was measured by 80 per cent of our respondents but was seen as the least useful. This is a reflection of the fact that it is more of a facet of long term planning than performance measurement.

7 Load factor is measured in terms of the number of seats occupied as a percentage of the total seats available. 8 There was no statistically significant correlation between use and reported usefulness of operational measures. Spearman rank-order correlation between percentage using operational performance measures with mean of usefulness of measure: rs = 0.27, ns at the 5 percent level. 9 ‘Belly hold’ freight is that which is carried in the hold of aircraft operating on passenger services. 10 WLU are equivalent units of activity for comparison purposes. 1 WLU is 1 passenger or 100kg of freight.

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Table 9: Operational performance measures Usefulness of

measure* Operational performance measure Used

/% Not

used /% Don’t

know /% Mean S

Punctuality/on-time performance per operation 100 0 0 4.6 0.9 Revenue passenger kilometres 95 5 0 4.2 1.1 Load factor per flight 100 0 0 4.5 1.0 Average fleet age 80 17 3 3.0 1.1 Available seat kilometres 93 7 0 4.2 0.9 Available tonne kilometres per employee 49 49 2 4.0 0.9 Average turnaround time 76 21 3 4.1 0.9 Labour cost as % of total operating cost 87 11 2 3.9 1.0 Cost per seat kilometre 90 8 2 4.7 0.7 Daily aircraft utilisation (hours) 98 0 2 4.3 1.0 Total revenue per Work Load Unit 43 40 17 4.5 0.5 Other 78 11 11 4.8 0.5 *Scale: 1=Not useful to 5=Very useful, S=Standard Deviation 5.3 Financial performance measures

Measuring various aspects of financial performance is important for airlines operating on tight margins. It is important to measure those aspects of which are contributing to the overall performance and not just the traditional ‘bottom line’ measure. As one of our respondents observed: ‘Airlines are by nature a strange mix of retail and technical industries with large cost base and cashflow variations. Performance measurement is essential in the tough low-margin business environment.’ In terms of the reported use of financial performance measures (see Table 10) the more traditional profit based measures were the most used and tended to be seen as useful11. In particular there was a focus on operating revenue and expenses. Profit was far more widely used than investor ratios such as earnings per share (EPS) and price earnings ratios (P/E), whose low uptake and usefulness can be explained by the fact that by no means all the respondents were in private ownership (46 per cent of respondents had a government stake in ownership and 54 per cent did not).

11 There was a correlation between the uptake and perceived usefulness of financial performance measures. Spearman rank-order correlation between percentage using financial performance measures with mean of usefulness of measure: rs = 0.71, p<0.05

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Table 10: Financial performance measures Usefulness of

measure* Financial performance measure

Used /%

Not used /%

Don’t know /%

Mean S Operating costs 95 0 5 4.8 0.6 Cash flow 95 0 5 4.4 0.9 Operating revenue 93 2 5 4.5 0.7 Profit 93 2 5 4.7 0.8 Return on Capital Employed 81 11 8 4.2 1.0 Gearing (debt to equity ratio) 76 11 13 3.9 1.0 Revenue to expenditure ratio 75 17 8 4.1 1.0 Price earnings (P/E) ratio 49 43 8 3.6 1.0 Share price 46 46 8 3.5 1.2 Earnings per share 38 54 8 3.6 1.2 Other 75 0 25 5.0 0.0 *Scale: 1=Not useful to 5=Very useful, S=Standard Deviation Table 11 shows the use and potential uptake of various financial performance indicators. While it also illustrates that the more traditional profit and accrual accounting based measures still dominate, newer approaches are having an impact. The balanced scorecard, which combines financial and non-financial indicators, is the most adopted of the ‘newer’ approaches. Shareholder value and other value based management methods such as Economic Value Added (EVA®) are used by a number of airlines. Table 11: The use and potential uptake of financial performance indicators Financial performance indicator Used

/% Being

considered /% Not being

considered /% Not aware

of /% Ability to stay within budget 98 0 0 2 Cash flow 98 0 0 2 Net profit 98 0 0 2 Return on Capital Employed 74 5 13 8 Balanced Scorecard 44 14 20 22 Economic Value Added (EVA®) 35 21 21 23 Shareholder Value Analysis (SVA) 28 22 34 16 Residual Income 24 6 36 33 Table 12 compares the findings with the findings from our airport study (Francis, Fry and Humphreys, 2001) and an earlier study across various sectors in the UK (Minchington and Francis, 2000). Airlines are generally making greater use of these financial performance techniques than revealed by these earlier studies. Though the relative ‘popularity’ of each technique is reasonably consistent between the studies, it is not surprising to see a very high

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usage of traditional accounting methods, such as budgets and Return on Capital Employed. However, newer approaches, such as the balanced scorecard, are in fairly widespread use. Table 12: The relative use of financial performance measures by airports Financial performance measure World airlines

/% World airports

/% Minchington and Francis (2000) (UK all sectors) /%

Ability to stay within budget 98 98 99 Return on Capital Employed 74 51 71 Balanced Scorecard 44 23 24 Economic Value Added (EVA®) 35 13 10 Shareholder Value Analysis (SVA) 28 10 15 Residual Income 24 17 6 5.4 Quality of service performance measures

It is interesting to reflect on the fact that several of these reportedly widely used quality of service indicators (Table 13) are not directly within the airlines’ control; control is devolved to airports or third parties. The use of airport service quality measures by the airlines has been a point of commercial contention for a number of years (Graham, 2003). Airline passengers have their own individual opinions on the acceptability of the service they receive (referred to as user perceived level of service, see Francis, Humphreys and Fry, 2003) determined by such activities as check in and baggage reclaim. A bad experience at the airport may determine the passengers’ propensity to fly again with a certain airline, so it is important for the airline to monitor the provision of airport services and surface access and third party ground handling services in order to maintain quality for their passengers12. In many parts of the world airlines enter into service level agreements with airports and third party handlers in an attempt to maintain levels of service for passengers. Passengers see the airline as the provider of the service and may not realise that the airport and third party handlers are involved (see Francis, Humphreys and Fry, 2003). This performance measurement activity is important, particularly in a competitive market, where there is a ‘rule of thumb’ which claims “it costs ten times more to win a new business passenger than to keep an existing one” an idea that has underpinned airlines introducing frequent flier programmes to maintain customer loyalty and to provide rich data streams that enable them to market to passengers on the basis of extensive market intelligence (Goetz, 2002; Shaw, 1999).

12 The traditional airport airline relationship has been evolving (see Graham 2003) but there is still a tendency for some airports to see airlines not passengers as their customers so airlines need to ensure the quality of service provided at the airport for their customers (the passengers).

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Table 13: Quality of service performance measures Usefulness of

measure* Performance measure Used

/% Not used

/% Don’t know

% Mean S

Level of service 86 7 7 4.5 0.9 Baggage delivery time 78 17 5 4.1 0.9 Lost baggage 98 2 0 4.3 0.8 Check in waiting time 85 13 3 4.1 0.9 Consumer complaints 98 2 0 4.4 0.9 Other 89 11 0 4.8 0.4 *Scale: 1=Not useful to 5=Very useful, S=Standard Deviation In Table 13 it can be seen that all quality of service indicators were widely used and considered to be very useful13. The tendency is for these to have a customer focus such as customer complaints. Airlines find level of service indicators14 useful in monitoring their critical relationships with airports and handling agents. The airlines frequently collected their own internal data of both physical and user perceived measures. This is carried out through a variety of methods, as illustrated in Table 14. Passenger questionnaires were the most prevalent method, but most airlines used more than one way of gathering data. As well as using the methods illustrated in Table 14, benchmark data from external agencies is frequently used (see section 5.6). Table 14: Methods of collecting performance measurement data by airlines Method Percentage of respondents

(N=39) Passenger questionnaires 87 Passenger interviews 49 Focus groups 39 Comment cards 62 Other 26 5.5 Environmental performance measures

The relative use of environmental performance measures is illustrated by Table 15, and is consistent with the trend identified by Upham (2003) that airlines are keen to undertake measures that lead to eco-efficiency and a reduction in input costs such as fuel and electricity, but are less keen on ‘sustainability’ issues that do not involve cost savings. Though it must be recognised that, in terms of fuel efficiency, there may be congruence between operational and 13 Although there was no statistically significant correlation between uptake and perceived usefulness. Spearman rank-order correlation between percentage using quality of service performance measures with mean of usefulness of measure: rs = 0.50, ns at the 5 per cent level 14 Level of service indicators such as: check in waiting time, time for baggage delivery to passenger, level of satisfaction with retail outlets, quality of directional signage. See IATA (2002) which includes 29 aspects of

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environmental performance improvements, the focus on measuring fuel consumption rather than emissions may have future significance in terms of the regulatory and taxation policy of governments wishing to control the environmental impact of airlines. Table 4 in section 5.1 showed that only 17 per cent of airlines (and 27 per cent of airports) were using ISO14000. Track keeping15 is becoming ever more important, particularly at airports where capacity is determined not by the operational capabilities of the airport infrastructure but by the environmental restrictions placed on operations to mitigate communities affected by aircraft noise. The measure of numbers of the population affected by noise and number of community complaints are particularly important in the European context where environmental pressure at the major hubs is bringing to bear political pressure to constrain airport expansion. Ultimately, this may affect an airline’s long term viability and the competitiveness of its hub operations. The choice of environmental measures appears linked to their perceived usefulness16. Table 15: Environmental performance measures Performance measure Usefulness of

measure*

Used

%

Not used %

Don’t know

% Mean S Percentage of passengers using public transport to access the airport

14 70 16 2.8 0.4

Percentage of departures on track 61 27 12 4.3 0.7 Energy efficiency of installations managed 32 38 30 3.7 0.6 Population affected by noise at base airport(s) 37 58 5 3.6 1.2 Percentage of waste recycled per annum 27 62 11 3.6 1.2 Number of community complaints about operations

32 53 15 3.7 0.5

CO2 emissions g/rtk17 24 54 22 3.8 0.8 Fuel consumption and efficiency g/rtk 83 12 5 4.5 0.7 Other 50 0 50 4.0 0.0 *Scale: 1=Not useful to 5=Very useful, S=Standard Deviation Surface access to airports is seen as an airport problem and hence only 14 per cent of airlines used this measure in relation to their passengers. However, in certain locations, airport capacity may be constrained by the surface access behaviour of airline passengers and staff. The UK Government’s 2003 White Paper only allows expansion of certain airports if passengers and staff make more journeys by public transport or car share schemes. In customer satisfaction. 15 Track keeping is the practice of flying aircraft along very specific routes (usually to avoid residential areas and minimize noise impact) when approaching or departing from an airport. 16 Spearman rank-order correlation between percentage using environmental performance measures with mean of usefulness of measure: rs = 0.76, p<0.05 17 g/rtk = grams per revenue tonne kilometre

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particular, at a number of airports, the air quality levels are driven more by surface transport than by emissions from aircraft18. In short, Heathrow will only receive a third runway if there is a reduction in the use of single occupancy car trips by all airport users, including airline staff. British Airways have recognised the significance of this and have led a number of measures to reduce single car occupancy trips (Ison and Humphreys, 2003). Other airlines are likely to take the measure of surface access trips ever more seriously, particularly in the European context where congestion and environmental legislation threatened to restrict an airline’s business. 5.6 The use of benchmarking by airlines

As illustrated in Table 3, the level of benchmarking activity across the airline sector was high and confirmed prior expectations of benchmarking prevalence. Table 16 shows that international airlines from Europe, North America and Asia/Pacific demonstrated a higher propensity to benchmark, with Europe showing the highest propensity. These three regions are also the strongest performing in terms of world airline traffic. Table 16: Prevalence of airline benchmarking by region

Region Percentage benchmarking (N=41)

Europe 95 North America 86 Asia/Pacific 86 Latin America / Caribbean 67 Africa/Middle East 67 Overall weighted average 88

In terms of airline size, larger airlines were more likely to engage in some form of benchmarking activity than smaller airlines (see Table 17). Benchmarking was undertaken by airline management at all airlines handling ten million passengers per annum or more that responded to the survey. This is consistent with the findings of Holloway et al. (1999) who found that larger organizations were more likely to benchmark than smaller ones. The prevalence of benchmarking activity is also high among the airlines handling between one and nine million passengers per annum.

18 At Heathrow for example it has been estimated that around 80% of air pollution is derived from surrounding road traffic and airside vehicles and only 20% is derived directly from aircraft.

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Table 17: Benchmarking in relation to airline size Passengers handled per annum /million

Percentage benchmarking (N=39)

1 to 4* 80 5 to 9 91 10 to 19 100 20 and above 100 Overall weighted average 88 Airline alliances were found to provide useful frameworks for benchmarking activity with the survey discovering that 49 per cent of airlines benchmarking undertook these activities with alliance partners. Given the trend towards globalization of the industry, benchmarking with alliance partners is a further means beyond the established commercial agreements of leveraging management benefits from alliances and creates a natural opportunity for benchmarking activity that ought to be less prone to data sensitivity and confidentiality issues. Within the airline industry the trend is for collaboration and a drive for airlines to enter into partnerships or be left in the cold. Code sharing and franchise agreements now number over 2000 (Upham, 2003). The nature of agreements between airlines demands that levels of service quality are maintained in order to protect brand quality. This has led to increased contact between airlines, and to the formal agreement and measurement of performance, particularly from a customer (passenger) perception perspective, and in terms of operational performance such as on time departures and safety. The formation of agreements and entry into alliances has seen geographically disparate airlines come together to share performance data, look for reasons for performance differences and to share best practice. Larger airlines are more likely to benchmark because they are likely to be alliance leaders and have wider network scope that enables comparisons to be made across geographically disparate areas. The drive towards collaboration has provided, and is likely to continue to provide, closer links between the world’s airlines and opportunities for increased performance measurement comparison and for benchmarking. The questionnaire instrument included the opportunity for respondents to describe their benchmarking experiences. Most comments were positive such as: ‘It is good to check how we are doing and to identify industry trends’ and ‘Useful as a driver for creating a sense of urgency’. However, although generally favourable, not all airlines reported equally favourable experiences. One respondent describing the outcomes as ‘unremarkable’ and another commented that benchmarking was ‘difficult due to availability of data.’ There is a tendency for airlines to look within the industry for benchmarking partners (see Table 18) as opposed to benchmarking and learning from organizations that have similar processes but are part of non-air transport related organizations: ‘It was good experience, letting us position our company towards the other airlines.’ The value of comparison with

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similar organizations and the difficulties associated with obtaining certain commercially sensitive data were highlighted by a number of managers. One stated that benchmarking was:

Very useful even if, for commercial reasons, the exchange of information is limited and slow with competitors. Other airlines are very easy to approach and good at sharing process, & technology applications, experience. Naturally large culture and environment (and resistance sometimes) issues can make adaptation or replication difficult.

The problem of data comparability for benchmarking between airlines was highlighted by comments such as: ‘Benchmarking can be of limited value due to widely different circumstances of benchmark’. Targets and benchmarking comparisons ‘have not always be[en] useful because data [is] not always comparable.’ Another airline manager reported that benchmarking partners were selected from airlines that were perceived to be non-competitors, typically those operating in different geographic markets. Table 18: Comparator organisation used by airlines, similar or dissimilar?

using mainly similar partners

1 2 3 4 5 6 7 using mainly

dissimilar partners 13% 43% 13% 22% 3% 3% 3% 69% 9%

(N=32) The selection of benchmarking partners from outside the industry can overcome issues of competitive sensitivity that can make access to certain information problematic. A well reported example of the benefits of this was the case of Southwest Airlines who benchmarked their refuelling and aircraft turnaround processes and practises against Formula 1 motor racing. The valuable lessons learned improved their turnaround times from 40 minutes to as little as 12 minutes in certain cases (Murdoch, 1997). Lateral thinking and looking outside the industry for examples of best practice might assist management. However such generic benchmarking activities by airlines are very rare. Benchmarking activity appears to be balanced between process improvement and performance measurement (see Table 19). For some airlines the perceived need is to develop an understanding of comparative performance, whereas for others the focus is on learning how to improve operations (processes). The balance may reflect the global reaction of the airlines to declining yields, a trend that has increased the pressure on airlines not only to manage the current performance of different business units but to look for opportunities to improve efficiency. The trend for full service airlines to look at and adopt different elements of the low cost model such as direct internet sales, one way fares and charging for snacks and drinks is an example of process improvements based on learning from other industry participants.

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Table 19: Is airline benchmarking focussed on process improvement or performance measurement?

more to do with process improvement

1 2 3 4 5 6 7 More to do

with measurement

3% 28% 12% 13% 16% 16% 12% 43% 44%

(N=32) Airlines use benchmarking as much for financial comparisons as for operational comparisons (see Table 20). In the survey benchmarking applied to non financial (operational) practices as opposed to financial measures was found to be roughly equally prevalent. This is interesting, as there are relatively few examples of this covered by the literature (Zairi, 1998), whereas there are more references to financial benchmarking among airlines (see for example: Feng and Wang, 2000 and Doganis, 2002). Table 20.: Financial or non-financial benchmarking comparisons in airlines? Primarily financial

measures 1 2 3 4 5 6 7

primarily non-financial measures

3% 3% 19% 44% 16% 12% 3% 25% 31%

(N=32) There is evidence that competing airlines are sharing engineering and maintenance data and meet regularly to share knowledge, particularly when new aircraft types were being introduced into service (Francis et al., 1999). Several competing airlines undertake maintenance for each other in different geographical regions. The competitive rhetoric of marketing departments is put aside in favour of the commercial sense of pooling maintenance resources. There was one example of airlines within the same alliance sending personnel to check third party maintenance by partner airlines to ensure quality was being maintained and to share lessons learned from the airlines’ own maintenance experience elsewhere in the world. A case study of Britannia Airlines revealed how they selected benchmarking partners operatingsss in different parts of the world (Francis et al., 1999). Benchmarking activity was focussed on comparisons with other airlines as opposed to benchmarking performance historically within and across different parts of their own airline (see Table 21). This is unexpected in some ways because airlines could easily and readily compare year on year performance across their network. There may however be a sense that within an airline the potential for learning new performance enhancing information via comparisons may be limited due to the airline working to the same company rules and operating practises. This trend may be a further echo of the structural pressure in the highly competitive airline market where comparisons with other airlines might be seen as holding

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greater potential for performance improvement and the increased ease of making comparisons due to the increased level of alliance, franchise and code share collaboration between airlines, a trend that looks likely to continue. Table 21: Internal or external benchmarking comparisons in airlines mainly internal

comparisons 1 2 3 4 5 6 7

mainly external comparisons

0% 0% 9% 6% 27% 43% 15% 9% 85%

(N=33) The significance of understanding the implications of work processes and activities of other airlines, particularly competitors, was further highlighted by comments made in response to the questionnaire survey. Airline management saw it as ‘critical to measure how we are performing, particularly against our competitors’ and ‘with a main competitor we find it [benchmarking] a very valuable tool.’ Historic comparisons had been exploited by one airline in the wake of a merger to try and capture the best work processes and practices from the acquired airline: ‘Having merged two airlines we are able to use historic benchmarking to a high degree.’ Benchmarking was equally used for specific comparisons of particular tasks or activities and more general comparisons of general practices and performance (see Table 22). Table 22: Is airline benchmarking concerned with specific tasks or general practices? Concerned with

specific tasks 1 2 3 4 5 6 7

concerned with general practices

0% 18% 24% 16% 24% 15% 3% 42% 42%

(N=33) 6. Conclusions

Performance measurement has become increasingly important in aviation as markets become more competitive and the number of asymmetric shocks seems to increase. Performance management is likely to become more critical with increased congestion in the air transport system, lower yields, pressure to reduce costs and increased operational pressures regarding the environmental and social impact of aviation. Prior to this study little was known about the nature and prevalence of performance measurement techniques by individual airlines. The survey revealed an interesting and diverse range of practices around the world. Follow up work will involve case studies with individual airlines to gain a deeper understanding of individual practices. Performance measures may need to evolve to reflect increased competition and cost constraints. For example, the impact of the low cost airlines may be to

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increase the importance placed on turnaround time, aircraft utilization, role of direct ticket sales via internet and call centres, flexible labour practices, as well as for airlines to evaluate the entire cost structure inherent in their operations. Our survey found that performance measurement practices were widespread within the airline industry but that there was primarily a focus on financial and operational measures. Given the pressure for cost efficiency it was perhaps not surprising that cost per seat kilometer operated was considered the most useful financial performance measure. Environmental performance measurement was most prevalent among European and Asian airlines but on the whole use of environmental measures frequently lagged behind operational and financial performance. These findings reflect the geographical disparity in environmental restrictions in different operating contexts. The most used performance improvement technique was best practice benchmarking although what is taking place in the name of benchmarking subsumed a wide range of activities. Most respondents reported using more than one performance improvement technique and as such quality management measures, balanced scorecard and business process reengineering were each in use by over 39 per cent of the sample. The survey found that size matters! The larger the airline, the greater the prevalence of performance measurement. The majority of airline benchmarking takes place within the industry sector and so there is the potential for more airlines to follow Southwest’s lead and benchmark outside the airline sector. The move towards airline alliances has facilitated the availability of benchmarking partners. This will continue to provide opportunities for alliance members to learn from each other, but will also provide challenges regarding the most appropriate and effective ways for management to measure and report performance across the entire network. References

ACI (2003) ACI Worldwide and Regional Forecasts Airport Traffic 2002-2020, Airports Council International, Geneva.

ATW (2002) ‘World airline traffic results – 2001’, Air Transport World, vol. 39 no. 7, pp. 44-56.

Braithwaite, G. (2001) Attitude or Latitude? Australian Aviation Safety, Ashgate, Aldershot. Calder, S. (2002) No Frills: the truth behind the low-cost revolution in the skies, Virgin

Books, London. Caves, D. and Gosling, G. (1999) Strategic Airport Planning, Pergamon, Oxford. Denton, N. and Dennis, N. (2000) ‘Airline franchising in Europe: benefits and disbenefits to

airlines and consumers’, Journal of Air Transport Management, vol.6, no.4, pp.179-190.

Doganis, R. (2001) The Airline Business in the 21st Century, Routledge, London. Doganis, R. (2002) Flying Off Course, Routledge, London.

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Feng, C. and Wang, R. (2000) ‘Performance evaluation for airlines including the consideration of financial ratios’, Journal of Air Transport Management, vol. 6 no. 2, pp. 133-142.

Francis, G., Hinton, M., Holloway, J. and Humphreys, I. (1999) ‘Best practice benchmarking: a route to competitiveness’, Journal of Air Transport Management, vol.5 no.2, pp.105-112.

Francis, G., Fry, J. and Humphreys, I. (2001) An International Survey of Performance Measurement in Airports, Open University Business School Working Paper, no. 01/4.

Francis, G.A.J., Humphreys, I. and Fry, J. (2003) ‘An international survey of the nature and prevalence of quality management systems in airports’, TQM & Business Excellence, vol. 14, no. 7, pp. 819-829.

Goetz, A. (2002) ‘Deregulation, competition, and antitrust implications in the US airline industry’, Journal of Transport Geography, vol. 10, pp. 1-19.

Graham, A. (2003) Managing Airports: an international perspective, Butterworth-Heinemann, Oxford.

Hanlon, P. (1999) Global Airlines: competition in a transnational industry, Macmillan, London.

Humphreys, I and Ison, S. (2003) ‘Lessons from United Kingdom airports on ground control strategies’, Transportation Research Record, no.1850, pp. 70-78.

Holloway, J.A., Hinton, C.M., Francis, G.A.J. and Mayle, D. (1999) Identifying Best Practice in Benchmarking, CIMA Research Monograph, CIMA, London.

IATA (2002) Global Airport Monitor, International Air Transport Association, Montreal. Kirkland, I., Caves, R. E., Hirst, M. and Pitfield, D. E. (2003) ‘The normalisation of aircraft

overrun accident data’, Journal of Air Transport Management, vol. 9 no. 6, pp. 333-341.

Mason, K., Whelan, C. and Williams, G. (2000), Europe’s Low Cost Airlines, Cranfield University Air Transport Group, Cranfield.

Minchington, C. and Francis, G. (2000) ‘Value based metrics as divisional performance measures’, in Arnold, G. and Davies, M., (eds) Value-Based Management: Context and Application, Wiley Chichester, pp.151-162.

Morrell, P., Alamdari, F. and Lu, C. (2000) Measures of Strategic Success: the evidence over ten years. A comparative study of 24 airlines from Asia/Pacific, North America and Europe, Cranfield University Air Transport Group, Cranfield.

Murdoch, A. (1997) ‘Lateral benchmarking or what formula one taught an airline’, Management Today, November, pp. 64-67.

Oppenheim, A.N. (1992) Questionnaire Design, Interviewing and Attitude Measurement, London, Pinter.

Scapens, R.W. (1999) ‘Researching management accounting practice: the role of case study methods’, British Accounting Review, vol. 22, no. 3, pp. 259-281.

Shaw, S. (1999) Airline Marketing and Management, Aldershot, Ashgate. Schefczyk, M., (1993) ‘Operational Performance of Airlines: an extension of traditional

measurement paradigms’, Strategic Management Journal, vol., 14, pp.301-317.

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TRL (2002) Airline Performance Indicators, TRL, Crowthorne. Upham , P. (2003) Towards Sustainable Aviation, Earthscan, London. Zairi, M. (1998) ‘Benchmarking at TNT Express’, Benchmarking for Quality Management

and Technology, vol. 5 no. 2, pp.138-149.

Appendix A Questionnaire Note: Percentage of respondents given for each closed question

World Airline Performance Measurement Survey This questionnaire forms part of a major project being conducted by Loughborough University, The OpenUniversity Business School in the UK and Waikato University in New Zealand on the use of performancemeasures in airlines. We do not want to know confidential information on how you are performing; we areonly concerned with how your airline measures and manages its performance. The information you providewill be treated in the strictest confidence and will only be used for academic purposes. Neither individuals nor airlines will be identified in any analysis. Q1 What job function do you work within? 12 Planning 3 Finance 13 Operations 0 Administration 31 Senior management 41 Other (please specify)

Q2 Has your airline employed any of the following methodologies to help improve performance?

Please tick all that apply. 34 Activity Based Costing 44 Balanced Scorecard 88 Best Practice Benchmarking 7 Business Excellence Model / European Foundation for Quality Management (EFQM) 39 Business Process Reengineering 5 Data envelopment analysis (DEA) 17 Environmental Management Systems (e.g. ISO14000) 0 Malcolm Baldrige Award 54 Quality Management Systems (e.g. ISO9000/BS5750 or similar) 22 Total Quality Management (TQM) 15 Value Based Management 12 No measures used 0 Other (please specify)

If your airline does use any of the above, what is your opinion of their effectiveness?

Please turn over ➨

Q3 Which of the following performance measures does your airline use and please rate the ones you douse on a scale of not useful (1) to very useful (5).

Operational measures Not useful Very

useful

Yes No Don’t know 1 2 3 4 5

Punctuality/on-time performance per operation

100 0 0 2 2 2 22 72

Revenue passenger kilometres 95 5 0 5 5 8 28 54 Load factor per flight 100 0 0 2 5 7 13 73 Average fleet age 80 17 3 10 19 35 26 10 Available seat kilometres 93 7 0 3 0 11 43 43 Available tonne kilometres per employee 49 49 2 0 6 18 47 29 Average turnaround time 76 21 3 0 4 25 32 39 Labour cost as % of total operating cost 87 11 2 0 13 13 43 31 Cost per seat kilometre 90 8 2 0 3 5 11 81 Daily aircraft utilisation (hours) 98 0 2 3 3 10 28 56 Total revenue per Work Load Unit 43 40 17 0 0 0 50 50 Other (please specify) 78 11 11 0 0 0 25 75

Financial indicators Not useful Very

useful

Yes No Don’t know 1 2 3 4 5

Share price 46 46 8 6 12 35 18 29 Earnings per Share 38 54 8 7 7 28 29 29 Price earnings (P/E) ratio 49 43 8 6 0 44 33 17 Operating revenue 93 2 5 0 0 14 25 61 Gearing (debt to equity ratio) 76 11 13 0 12 15 46 27 Revenue to expenditure ratio 75 17 8 0 12 12 34 42 Return on Capital Employed 81 11 8 4 4 7 39 46 Profit 93 2 5 0 3 3 11 83 Operating Costs 95 0 5 0 0 6 14 80 Cash flow 95 0 5 3 0 8 28 61 Other (please specify) 75 0 25 0 0 0 0 100

Quality of service measures Not useful Very

useful

Yes No Don’t know 1 2 3 4 5

Level of service 86 7 7 3 0 6 29 62 Baggage delivery time 78 17 5 0 3 25 34 38 Lost baggage 98 2 0 0 0 15 36 49 Check in waiting time 85 13 3 0 3 21 38 38 Consumer complaints 98 2 0 0 2 18 15 65 Other (please specify) 89 11 0 0 0 0 20 80

Environmental indicators Not useful Very

useful

Yes No Don’t know 1 2 3 4 5

Percentage of passengers using public transport to access the airport

14 70 16 0 17 83 0 0

Percentage of departures on track 61 27 12 0 0 12 40 48

Energy efficiency of installations managed

32 38 30 0 0 38 54 8

Population affected by noise at base airport(s)

37 58 5 0 14 36 21 29

Percentage of waste recycled per annum 27 62 11 9 9 18 46 18

Number of community complaints about operations

32 53 15 0 0 31 61 8

CO2 emissions g/rtk 24 54 22 0 0 40 40 20 Fuel consumption and efficiency g/rtk 83 12 5 0 0 9 31 60 Other (please specify) 50 0 50 0 0 0 0 100 Q4 How does your airline measure its performance in terms of safety?

Q5 What indicators do you use to measure performance in terms of security?

Please turn over ➨

Q6 Is your airline involved in any form of benchmarking? 88 Yes 7 No 5 Don't know If yes, please locate your airline's experience of benchmarking on each of the following scales:

concerned with specific tasks 0 18 24 16 24 15 3 concerned with

general practices

more to do with process improvement 3 28 12 13 16 16 12 more to do with

measurement

mainly internal comparisons 0 0 9 6 27 43 15 mainly external

comparisons

primarily financial measures 3 3 19 44 16 12 3 primarily non-financial

measures

using mainly similar partners 13 43 13 22 3 3 3 using mainly dissimilar

partners How would you describe your benchmarking experience?

Were your benchmarking activities undertaken with Alliance partners? 49 Yes 49 No 2 Don't know Q7 So far as you are aware, has your airline introduced any new performance measures in the last two

years? 62 Yes 33 No 5 Don't know If yes, what performance measures were introduced?

How influential were management consultants in the choice of the new measure(s)?

14 Very influential 26 Not influential 31 Some influence 29 Management consultants not used What other factors influenced the choice of the new measures?

Q8 What process do you use to collect performance measurement data?

87 Passenger questionnaires 39 Focus groups 49 Passenger interviews 62 Comment cards 26 Other (please specify)

Q9 As far as you are aware, which of the following financial measures are:

• used currently to evaluate the performance of your airline • being considered for use in the future for your airline • not being considered • a measure you are not aware of

Used Being considered

Not being considered Not aware

Ability to stay within budget 98 0 0 2 Net Profit (loss) 98 0 0 2 Cash Flow 98 0 0 2 Balanced Scorecard 44 14 20 22 Economic Value Added (EVA®) 35 21 21 23 Residual Income (RI) 24 6 36 33 Return on Capital Employed (ROCE or ROI) 74 5 13 8 Shareholder Value Added (SVA) 28 22 34 16 Other (please specify) 34 16 16 33

Q10 Are there any additional comments you would like to make concerning the use of performance

measures?

Please turn over ➨

Would you be interested in receiving the report produced from this study?

86 Yes 14 No Would you be interested in participating further in this study?

58 Yes 42 No If yes, please give details to enable us to contact you:

Name Job Title Airline Address E-mail Telephone number

Thank you for taking the time to complete this questionnaire. Please return it to Dr. Jackie Fry, The Open University Business School, Walton Hall,

Milton Keynes, UK in the international reply paid envelope provided

OR ATTACH BUSINESS CARD