- Email: [email protected]
Financial performance and customer service: An examination using activity-based costing of 38 international airlines
Journal of Air Transport Management 19 (2012) 13e15
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Financial performance and customer service: An examination using activity-based costing of 38 international airlines Wen-Cheng Lin Department of Business Administration, National Taipei College of Business, 321, 1, Jinan Rd., Taipei City, Taiwan, ROC
a b s t r a c t Keywords: Financial performance of airlines Airline customer service Activity-based costing for airlines
This study looks at the financial performance of a set of large international airlines from North America, Europe, Latin America, Asia, and the Middle East. Efficiency measures are related to their strategically focused expenditures on operations and on customer services. The results, based on data envelopment analysis, indicate that operation management, including that of customer service attribute evaluation, may be improved through the adoption of activity-based costing analysis. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The trend towards increasingly commercially driven markets for international air services accompanying the liberalization of many bilateral air service agreement, and the repeated financial crises in the airline industry, has caused large carriers to reassess the way they operate and the nature of the customer services that they offer. A major perceived area of cost savings is time in transit and flight costs, but there are also likely potential savings to be made from improved operational processes and elimination of other inefficiencies. The activity-based costing (ABC) systems offer a potential way to help identify ways to control costs and enhance customer series. It is not only concerned with allocating costs more precisely but also seeks to improve inefficiency. In this paper, we explore the relationship between financial performance and customer service using data envelopment analysis (DEA) as part of the ABC process.
2. Methodology The ABC system, which has its basis in activity costing and inputeoutput accounting, has developed over the past 20 years as a tool for improving the behavioral, business and accounting practices in industrial organizations (Anderson, 1995; Compton, 1996).1 In a business organization, the ABC methodology assigns an organization’s resource costs through activities to the products
E-mail address: [email protected]. As explained by Peter F. Drucker (1999), traditional cost accounting focuses on what it costs to do something, for example, to cut a screw thread; activity-based costing also records the cost of not doing, such as the cost of waiting for a needed part. In this way, activity-based costing records the costs that traditional cost accounting does not do. 1
0969-6997/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2011.12.002
and the services that it provides to its customers. It is generally used as a tool for understanding product and customer cost and profitability based on the production or performing processes. As such, ABC has largely been used to support strategic decisions such as pricing, outsourcing, and the identification and measurement of process improvement initiatives. In more detail, the approach initially involves dividing the production procedure into a series of activities and allocating overhead costs to each. This model assigns more indirect costs into direct costs compared to conventional costing models. Then, based on the levels of these activities consumed by the final products or services being produced, it allocates overhead cost to each of these. Production costs are thus allocated through a “cost driver method” in two stages to minimize distortions. In focusing on the costs associated with activities, ABC can also evaluate whether those activities add value, thus providing a way to assess efficiency and/or enhance customer services. It also has the practical appeal to many firms that it is focused on activities, not responsibilities, and so is seen as less threatening to the mangers of the various intrabusiness functions under review.2 To examine this efficiency aspect within an ABC context we deploy a long established, and well-tried programming technique; data envelopment analysis3 as part of an ABC process in assessing the performance of international airlines. While there are alternative techniques, generally involving some form of stochastic
2 ABC is not without its limitations, and in particular, manually driven ABC can bean inefficient use of resources; it can be expensive and difficult to implement for small gains, but running against this it covers a broader-broad band of activities than many alternative. 3 Cooper et al. (1999) is a comprehensive text that explains DEA models and applications and provides further reading as well as DEA-Solver software.
14
W.-C. Lin / Journal of Air Transport Management 19 (2012) 13e15
frontier analysis, the DEA methodology avoids the need for an initial specification of a functional form for the efficiency frontier. The basic idea of DEA, as with stochastic approaches, is to identify the most efficient decision-making unit (DMU). In this context, efficiency is the ratio of the weighted sum of a firm’s outputs to the weighted sum of its inputs. Once the efficiency scores are derived, they are regressed against a set of financial and operating variables that include the percentage of passenger operations, passenger revenue at the average load factor, international passenger revenue-kilometers as a percentage of passenger revenuekilometers, scheduled service revenues as a percentage of revenues, sales, indirect cost, fixed assets, and receivable turnover in days. In terms of anticipated relationships and expected signs, studies by Caves et al. (1983) and others have shown evidence of a positive correlation between average load factors and financial performance while Oum and Yu’s work (1999) suggests that average load factor is a reflection of an airline’s choice of aircraft and flight frequencies. Fethi et al. (2002) find that an increase in the international focus of an airline leads to spatial disparities in its financial environment. The share of scheduled service revenues is anticipated to have a positive impact on operational efficiency; scheduled flights require different products and marketing than charter flights that allows a rationalization of routines and greater overall efficiency. In terms of passenger/cargo mix, Oum and Yu (1999) argue that this can be particularly important in Asian and European markets where the latter accounts for a large portion of output of many Asian and European carriers based in export-oriented countries. The economic of carrying passenger and cargo is different; cargo for example is usually carried one-way, while passengers generally make round trips. Average flight length captures economies of distance that Trethaway (1984) and others have demonstrated affects costs. Activity-based cost accounting breaks down cost estimation into four stages:
identify activities, assign resource costs to activities, identify outputs, link activity costs to outputs.
ABC differs from traditional cost accounting that assigns costs directly to outputs (Cooper and Kaplan, 1992). The information on costs, activities, and outputs that ABC generates can be used in DEA that seeks individual producing units that are getting the most output of their inputs for each activity or profit center. Together ABC and DEA can provide a two-dimensional portrayal of a business across individual operating units and individual inputs and outputs.
2.1. Stage 1: activity-based costing We distinguished between two basic activities: providing services for passengers and carrying out transactions. Distinguishing between these is the basis of any carrier’s route planning; essentially projecting what market conditions on various routes will look like in a decade or so and to then preparing for these changes well in advance. One aspect of the ABC approach is to emphasizes drivers; factors that drive costs and that drive income. There is a major difference between cost drivers for providing services and cost drivers for carrying out transactions with regard to a route. Providing services for customers involves providing them with access to flight transactions anywhere in the country using a variety of technologies; whereas route services are geographically specific.
Table 1 Airline efficiency values. Airline
Country
Efficiency value
British Airways Virgin Atlantic American Airlines Continental Airlines Delta Airlines Northwest Airlines Trans World Airline United Airlines US Airways Aerolineas Argentinas Austrian Airlines Biman Bangladesh Lloyd Aereo Boliviano Air Canada Canadian Airlines Lan Chile Cathay Pacific Avianca Colombia Czech Airlines Air France Lufthansa Air India All Nippon Airways Japan Airlines Kuwait Airways Malaysia Airlines Aeromexico Mexicana KLM Royal Dutch Airlines Pakistan International Airlines LOT Asiana Korean Air Scandinavian Singapore Airlines Iberia Srilankian Thai Airways International
United Kingdom United Kingdom United States United States United States United States United States United States United States Argentina Austria Bangladesh Bolivia Canada Canada Chile China, Hong Kong Colombia Czech Republic France Germany India Japan Japan Kuwait Malaysia Mexico Mexico Netherlands Pakistan Poland Republic of Korea Republic of Korea Scandinavia Singapore Spain Sri Lanka Thailand
0.99 1.00 1.00 1.00 1.00 1.00 0.94 1.00 0.82 1.00 0.86 0.91 1.00 0.85 0.84 1.00 1.00 0.75 1.00 1.00 0.97 0.91 0.73 1.00 0.86 0.94 0.93 0.91 1.00 0.84 0.86 1.00 1.00 0.77 1.00 0.91 0.99 1.00
2.2. Stage 2: data envelopment analysis Our use of ABC involves generating data on cost drivers for separate services and transactions that are then fed into a DEA to measure the relative efficiency of each. For the input-oriented model, efficiency measures the proportions of inputs used by a relatively efficient airlines to produce its current level of output, with the most efficient carriers having values of one. DEA analysis does not provide guidance as to the potentially most efficient use of resources but rather it develops as benchmarks a list of airlines that are at the frontier of efficiency of those in the market. It may be possible to be more efficient than these, but no current airline, or at least none in the data set, is at that level. Table 2 Mean values of Tobit regression variables. Variables
Mean
Minimum
Maximum
International percentage of operations Load factor: ton-kilometers (%) Percentage of operating revenues from passenger services (%) Scheduled service revenues as a percentage of revenues (%) Operations focus ($/ATKm) Ticketing, sales, promotion focus ($/revenue passengers) Indirect cost ($thousands) Fixed assets ($thousands) Receivable turnover (days)
73.35
13.238
100
60.39 81.12
42.34 58
82.53 99
91.74
79
99
0.25 0.04
0.178 0.012
0.728 0.072
268 3571 28
12 123 10
1589 56,854 58
W.-C. Lin / Journal of Air Transport Management 19 (2012) 13e15 Table 3 Tobit model results. Intercept Indirect cost ($ thousands) International percentage of operations Load factor (%) Percentage of operating revenues from passenger services Scheduled service revenues as a percentage of revenues Operations focus ($/ATKm) Ticketing, sales, promotion focus ($/revenue passengers) Fixed assets ($ thousands) Receivable turnover (days) Indirect cost international percentage of operations Indirect cost load factor Indirect cost percentage of operating revenues from passenger services Indirect cost scheduled service revenues as a percentage of revenues
12,485 1.391 0.385** 1.259*** 0.956** 1.676* 0.463 6.422 3.158 0.549 0.151 1.448 2.763*** 1.612
Significance levels using Chi-squared tests: *p < 0.1; **p < 0.05; ***p < 0.01.
We use this approach to develop benchmarks against which airlines can compare their performance; essentially their relative efficiency. The aim is to provide guidance for less efficient carriers as to way of increasing their productivity and more closely time their activities to consumer demands. 3. Results To make inter-firm comparisons meaningful, it is necessary to compare firms that are similar regarding such things as the business environment they operate in, their mix of activities, and their size. It makes no sense, for example to compare a large international carrier such as Lufthansa with a small regional airline. To this end Kantor and Maital (1999) conducted a cluster analysis of airlines that involved dividing them into groups that have a high degree of internally homogeneous. We use the grouping developed in our applications of DEA, of financial statement analysis, and of ABC to international airlines. We use data for thirty-eight international airline for fiscal year 2008 taken from the International Air Transport Association, International Civil Aviation Association and financial statement of the carriers. The results of the DEA analysis are seen in Table 1, where we see that efficiency values range from a low of 0.73 to a high of 1.00. Thirty4 of the 38 airlines in the sample are seen to be operating with the efficient frontier. In terms of interpretation, All Nippon Airways with an efficiency value of 0.74, could have reduced all inputs proportionately by 26% and still have produced the same level of output. Other airline that could have reduced all inputs proportionately by more than 10% include US Airways, Austrian Airlines, Air Canada, Canadian Airlines, Avianca Colombia, Kuwait Airways, Pakistan International Airlines, LOT and Scandinavian. Table 2 presents the mean values of the explanatory variables used in the subsequent Tobit analysis, the results of which, including statistical significance, are presented in Table 3.5 In addition to just using variables independently, a number of
4 There is a tendency when using DEA to have a larger proportion of observations on the frontier as the sample size increases and as more inputs are considered. This is because each individual observation e airlines in this case e tends to have more specific features making it the most efficient at offering that particular bundle of outputs using that collection of inputs. The number on the frontier in these calculations is within the range one would expect for the type of analysis being conducted here. 5 A Tobit or similar non-linear sigmoid model is required because the values from the DEA analysis are bounded by one and zero.
15
interactive terms are included. These involve multiplying each of the financial variables by the ABC measures. The independent variables are also standardized to limit multicollinearity problems. Five of the variables examined are seen to be significant at the 10% level or better. Focusing initially on the direct effects, in line with previous studies load factor has the expected positive impact on financial efficiency (costs decline as load-factor rises) and is significant. Similarly, there is a significant positive effect on costs from offering a high percentage of scheduled services. It is also seen, however, that a specialization in serving international, and passenger markets exerts a significant negative impact on efficiency; costs rise as they increase. This is also true of the interaction term between the indirect cost and the percentage of operating revenues from passenger services. 4. Conclusions Traditional cost accounting methods, which allocate carrier’s indirect costs on the basis of one driver, such as flight length or aircraft load factor, while simple to do and understand, can excessively narrow and ultimately be misleading. An effective application of ABC procedures can incorporate a variety of potentially important factors that influence airline performance. Here we provide a case study by using DEA to conduct a financial statement analysis of 38 large international airlines. The results show the importance of operating scheduled services on lowering costs, and of having a high load factor. Acknowledgments This work was supported by National Science Council, Taiwan, ROC under grant NSC 99-2410-H-141-013. The authors would like to thank anonymous reviewers for their helpful comments. References Anderson, S., 1995. A framework for assessing cost management system changes: the case of activity-based costing implementation at general motors. Journal of Management Accounting Research 7, 1e51. Caves, D.W., Christensen, L.R., Trethaway, M.W., 1983. Productivity performance of US trunk and local service airlines in the era of deregulation. Economic Inquiry 21, 312e324. Compton, T.R., 1996. Implementing activity-based costing. The CPA Journal 66, 20e27. Cooper, R., Kaplan, R.S., 1992. Implementing Activity-Based Cost Management. Prentice Hall, Montvale. Cooper, W.W., Seiford, L.M., Tone, K., 1999. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Kluwer, Boston. Drucker, P., 1999. Management Challenges of the 21st Century. Harper Business, New York. Fethi, M., Jackson, P., Weyman-Jones, T., 2002. Measuring the Efficiency of European Airlines: An Application of Tobit Analysis. Working Paper. University of Leicester, Management Center. Kantor, J., Maital, S., 1999. Measuring efficiency by product group: integrating DEA with activity-based accounting in a large mideast bank. Interfaces 29, 27e36. Oum, T., Yu, C., 1999. Winning Airlines: Productivity and Cost Competitiveness of the World’s Major Airlines. Kluwer Academic Publishers, Norwell. Trethaway, M., 1984. An international comparison of airlines. Proceedings of the Canadian Transportation Research Forum, 34e43.
Contents lists available at SciVerse ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Financial performance and customer service: An examination using activity-based costing of 38 international airlines Wen-Cheng Lin Department of Business Administration, National Taipei College of Business, 321, 1, Jinan Rd., Taipei City, Taiwan, ROC
a b s t r a c t Keywords: Financial performance of airlines Airline customer service Activity-based costing for airlines
This study looks at the financial performance of a set of large international airlines from North America, Europe, Latin America, Asia, and the Middle East. Efficiency measures are related to their strategically focused expenditures on operations and on customer services. The results, based on data envelopment analysis, indicate that operation management, including that of customer service attribute evaluation, may be improved through the adoption of activity-based costing analysis. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction The trend towards increasingly commercially driven markets for international air services accompanying the liberalization of many bilateral air service agreement, and the repeated financial crises in the airline industry, has caused large carriers to reassess the way they operate and the nature of the customer services that they offer. A major perceived area of cost savings is time in transit and flight costs, but there are also likely potential savings to be made from improved operational processes and elimination of other inefficiencies. The activity-based costing (ABC) systems offer a potential way to help identify ways to control costs and enhance customer series. It is not only concerned with allocating costs more precisely but also seeks to improve inefficiency. In this paper, we explore the relationship between financial performance and customer service using data envelopment analysis (DEA) as part of the ABC process.
2. Methodology The ABC system, which has its basis in activity costing and inputeoutput accounting, has developed over the past 20 years as a tool for improving the behavioral, business and accounting practices in industrial organizations (Anderson, 1995; Compton, 1996).1 In a business organization, the ABC methodology assigns an organization’s resource costs through activities to the products
E-mail address: [email protected]. As explained by Peter F. Drucker (1999), traditional cost accounting focuses on what it costs to do something, for example, to cut a screw thread; activity-based costing also records the cost of not doing, such as the cost of waiting for a needed part. In this way, activity-based costing records the costs that traditional cost accounting does not do. 1
0969-6997/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2011.12.002
and the services that it provides to its customers. It is generally used as a tool for understanding product and customer cost and profitability based on the production or performing processes. As such, ABC has largely been used to support strategic decisions such as pricing, outsourcing, and the identification and measurement of process improvement initiatives. In more detail, the approach initially involves dividing the production procedure into a series of activities and allocating overhead costs to each. This model assigns more indirect costs into direct costs compared to conventional costing models. Then, based on the levels of these activities consumed by the final products or services being produced, it allocates overhead cost to each of these. Production costs are thus allocated through a “cost driver method” in two stages to minimize distortions. In focusing on the costs associated with activities, ABC can also evaluate whether those activities add value, thus providing a way to assess efficiency and/or enhance customer services. It also has the practical appeal to many firms that it is focused on activities, not responsibilities, and so is seen as less threatening to the mangers of the various intrabusiness functions under review.2 To examine this efficiency aspect within an ABC context we deploy a long established, and well-tried programming technique; data envelopment analysis3 as part of an ABC process in assessing the performance of international airlines. While there are alternative techniques, generally involving some form of stochastic
2 ABC is not without its limitations, and in particular, manually driven ABC can bean inefficient use of resources; it can be expensive and difficult to implement for small gains, but running against this it covers a broader-broad band of activities than many alternative. 3 Cooper et al. (1999) is a comprehensive text that explains DEA models and applications and provides further reading as well as DEA-Solver software.
14
W.-C. Lin / Journal of Air Transport Management 19 (2012) 13e15
frontier analysis, the DEA methodology avoids the need for an initial specification of a functional form for the efficiency frontier. The basic idea of DEA, as with stochastic approaches, is to identify the most efficient decision-making unit (DMU). In this context, efficiency is the ratio of the weighted sum of a firm’s outputs to the weighted sum of its inputs. Once the efficiency scores are derived, they are regressed against a set of financial and operating variables that include the percentage of passenger operations, passenger revenue at the average load factor, international passenger revenue-kilometers as a percentage of passenger revenuekilometers, scheduled service revenues as a percentage of revenues, sales, indirect cost, fixed assets, and receivable turnover in days. In terms of anticipated relationships and expected signs, studies by Caves et al. (1983) and others have shown evidence of a positive correlation between average load factors and financial performance while Oum and Yu’s work (1999) suggests that average load factor is a reflection of an airline’s choice of aircraft and flight frequencies. Fethi et al. (2002) find that an increase in the international focus of an airline leads to spatial disparities in its financial environment. The share of scheduled service revenues is anticipated to have a positive impact on operational efficiency; scheduled flights require different products and marketing than charter flights that allows a rationalization of routines and greater overall efficiency. In terms of passenger/cargo mix, Oum and Yu (1999) argue that this can be particularly important in Asian and European markets where the latter accounts for a large portion of output of many Asian and European carriers based in export-oriented countries. The economic of carrying passenger and cargo is different; cargo for example is usually carried one-way, while passengers generally make round trips. Average flight length captures economies of distance that Trethaway (1984) and others have demonstrated affects costs. Activity-based cost accounting breaks down cost estimation into four stages:
identify activities, assign resource costs to activities, identify outputs, link activity costs to outputs.
ABC differs from traditional cost accounting that assigns costs directly to outputs (Cooper and Kaplan, 1992). The information on costs, activities, and outputs that ABC generates can be used in DEA that seeks individual producing units that are getting the most output of their inputs for each activity or profit center. Together ABC and DEA can provide a two-dimensional portrayal of a business across individual operating units and individual inputs and outputs.
2.1. Stage 1: activity-based costing We distinguished between two basic activities: providing services for passengers and carrying out transactions. Distinguishing between these is the basis of any carrier’s route planning; essentially projecting what market conditions on various routes will look like in a decade or so and to then preparing for these changes well in advance. One aspect of the ABC approach is to emphasizes drivers; factors that drive costs and that drive income. There is a major difference between cost drivers for providing services and cost drivers for carrying out transactions with regard to a route. Providing services for customers involves providing them with access to flight transactions anywhere in the country using a variety of technologies; whereas route services are geographically specific.
Table 1 Airline efficiency values. Airline
Country
Efficiency value
British Airways Virgin Atlantic American Airlines Continental Airlines Delta Airlines Northwest Airlines Trans World Airline United Airlines US Airways Aerolineas Argentinas Austrian Airlines Biman Bangladesh Lloyd Aereo Boliviano Air Canada Canadian Airlines Lan Chile Cathay Pacific Avianca Colombia Czech Airlines Air France Lufthansa Air India All Nippon Airways Japan Airlines Kuwait Airways Malaysia Airlines Aeromexico Mexicana KLM Royal Dutch Airlines Pakistan International Airlines LOT Asiana Korean Air Scandinavian Singapore Airlines Iberia Srilankian Thai Airways International
United Kingdom United Kingdom United States United States United States United States United States United States United States Argentina Austria Bangladesh Bolivia Canada Canada Chile China, Hong Kong Colombia Czech Republic France Germany India Japan Japan Kuwait Malaysia Mexico Mexico Netherlands Pakistan Poland Republic of Korea Republic of Korea Scandinavia Singapore Spain Sri Lanka Thailand
0.99 1.00 1.00 1.00 1.00 1.00 0.94 1.00 0.82 1.00 0.86 0.91 1.00 0.85 0.84 1.00 1.00 0.75 1.00 1.00 0.97 0.91 0.73 1.00 0.86 0.94 0.93 0.91 1.00 0.84 0.86 1.00 1.00 0.77 1.00 0.91 0.99 1.00
2.2. Stage 2: data envelopment analysis Our use of ABC involves generating data on cost drivers for separate services and transactions that are then fed into a DEA to measure the relative efficiency of each. For the input-oriented model, efficiency measures the proportions of inputs used by a relatively efficient airlines to produce its current level of output, with the most efficient carriers having values of one. DEA analysis does not provide guidance as to the potentially most efficient use of resources but rather it develops as benchmarks a list of airlines that are at the frontier of efficiency of those in the market. It may be possible to be more efficient than these, but no current airline, or at least none in the data set, is at that level. Table 2 Mean values of Tobit regression variables. Variables
Mean
Minimum
Maximum
International percentage of operations Load factor: ton-kilometers (%) Percentage of operating revenues from passenger services (%) Scheduled service revenues as a percentage of revenues (%) Operations focus ($/ATKm) Ticketing, sales, promotion focus ($/revenue passengers) Indirect cost ($thousands) Fixed assets ($thousands) Receivable turnover (days)
73.35
13.238
100
60.39 81.12
42.34 58
82.53 99
91.74
79
99
0.25 0.04
0.178 0.012
0.728 0.072
268 3571 28
12 123 10
1589 56,854 58
W.-C. Lin / Journal of Air Transport Management 19 (2012) 13e15 Table 3 Tobit model results. Intercept Indirect cost ($ thousands) International percentage of operations Load factor (%) Percentage of operating revenues from passenger services Scheduled service revenues as a percentage of revenues Operations focus ($/ATKm) Ticketing, sales, promotion focus ($/revenue passengers) Fixed assets ($ thousands) Receivable turnover (days) Indirect cost international percentage of operations Indirect cost load factor Indirect cost percentage of operating revenues from passenger services Indirect cost scheduled service revenues as a percentage of revenues
12,485 1.391 0.385** 1.259*** 0.956** 1.676* 0.463 6.422 3.158 0.549 0.151 1.448 2.763*** 1.612
Significance levels using Chi-squared tests: *p < 0.1; **p < 0.05; ***p < 0.01.
We use this approach to develop benchmarks against which airlines can compare their performance; essentially their relative efficiency. The aim is to provide guidance for less efficient carriers as to way of increasing their productivity and more closely time their activities to consumer demands. 3. Results To make inter-firm comparisons meaningful, it is necessary to compare firms that are similar regarding such things as the business environment they operate in, their mix of activities, and their size. It makes no sense, for example to compare a large international carrier such as Lufthansa with a small regional airline. To this end Kantor and Maital (1999) conducted a cluster analysis of airlines that involved dividing them into groups that have a high degree of internally homogeneous. We use the grouping developed in our applications of DEA, of financial statement analysis, and of ABC to international airlines. We use data for thirty-eight international airline for fiscal year 2008 taken from the International Air Transport Association, International Civil Aviation Association and financial statement of the carriers. The results of the DEA analysis are seen in Table 1, where we see that efficiency values range from a low of 0.73 to a high of 1.00. Thirty4 of the 38 airlines in the sample are seen to be operating with the efficient frontier. In terms of interpretation, All Nippon Airways with an efficiency value of 0.74, could have reduced all inputs proportionately by 26% and still have produced the same level of output. Other airline that could have reduced all inputs proportionately by more than 10% include US Airways, Austrian Airlines, Air Canada, Canadian Airlines, Avianca Colombia, Kuwait Airways, Pakistan International Airlines, LOT and Scandinavian. Table 2 presents the mean values of the explanatory variables used in the subsequent Tobit analysis, the results of which, including statistical significance, are presented in Table 3.5 In addition to just using variables independently, a number of
4 There is a tendency when using DEA to have a larger proportion of observations on the frontier as the sample size increases and as more inputs are considered. This is because each individual observation e airlines in this case e tends to have more specific features making it the most efficient at offering that particular bundle of outputs using that collection of inputs. The number on the frontier in these calculations is within the range one would expect for the type of analysis being conducted here. 5 A Tobit or similar non-linear sigmoid model is required because the values from the DEA analysis are bounded by one and zero.
15
interactive terms are included. These involve multiplying each of the financial variables by the ABC measures. The independent variables are also standardized to limit multicollinearity problems. Five of the variables examined are seen to be significant at the 10% level or better. Focusing initially on the direct effects, in line with previous studies load factor has the expected positive impact on financial efficiency (costs decline as load-factor rises) and is significant. Similarly, there is a significant positive effect on costs from offering a high percentage of scheduled services. It is also seen, however, that a specialization in serving international, and passenger markets exerts a significant negative impact on efficiency; costs rise as they increase. This is also true of the interaction term between the indirect cost and the percentage of operating revenues from passenger services. 4. Conclusions Traditional cost accounting methods, which allocate carrier’s indirect costs on the basis of one driver, such as flight length or aircraft load factor, while simple to do and understand, can excessively narrow and ultimately be misleading. An effective application of ABC procedures can incorporate a variety of potentially important factors that influence airline performance. Here we provide a case study by using DEA to conduct a financial statement analysis of 38 large international airlines. The results show the importance of operating scheduled services on lowering costs, and of having a high load factor. Acknowledgments This work was supported by National Science Council, Taiwan, ROC under grant NSC 99-2410-H-141-013. The authors would like to thank anonymous reviewers for their helpful comments. References Anderson, S., 1995. A framework for assessing cost management system changes: the case of activity-based costing implementation at general motors. Journal of Management Accounting Research 7, 1e51. Caves, D.W., Christensen, L.R., Trethaway, M.W., 1983. Productivity performance of US trunk and local service airlines in the era of deregulation. Economic Inquiry 21, 312e324. Compton, T.R., 1996. Implementing activity-based costing. The CPA Journal 66, 20e27. Cooper, R., Kaplan, R.S., 1992. Implementing Activity-Based Cost Management. Prentice Hall, Montvale. Cooper, W.W., Seiford, L.M., Tone, K., 1999. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Kluwer, Boston. Drucker, P., 1999. Management Challenges of the 21st Century. Harper Business, New York. Fethi, M., Jackson, P., Weyman-Jones, T., 2002. Measuring the Efficiency of European Airlines: An Application of Tobit Analysis. Working Paper. University of Leicester, Management Center. Kantor, J., Maital, S., 1999. Measuring efficiency by product group: integrating DEA with activity-based accounting in a large mideast bank. Interfaces 29, 27e36. Oum, T., Yu, C., 1999. Winning Airlines: Productivity and Cost Competitiveness of the World’s Major Airlines. Kluwer Academic Publishers, Norwell. Trethaway, M., 1984. An international comparison of airlines. Proceedings of the Canadian Transportation Research Forum, 34e43.