بهره وری آژانس های مسافرتی: یک مطالعه موردی از آلیکانته، اسپانیا

This study analyses the relative efficiency of 22 travel agencies of similar characteristics based in Alicante (Spain). This analysis is carried out using the Data Envelopment Analysis (DEA) technique and smoothed bootstrap. Following the analysis, possible lines of action that the agencies can take in order to improve their efficiency in the future are provided. Finally, using the Mann Whitney U Test, the relationship, or lack thereof, between the levels of efficiency of these agencies and their ownership type, location and level of experience is examined.

The growing amount of competition in the economy over the last few years has stimulated interest in the analysis and assessment of efficiency in all economic sectors. The services sector is no exception, despite the fact that its unique characteristics (such as intangibility or heterogeneity of its outputs) mean that it is difficult to assess and quantify its efficiency (McLaughlin & Coffey, 1990). Travel agencies work in the services sector, and more specifically in the tourism sector. Given the importance and global scope of today's tourism sector, it seems important to carry out an analysis of the efficiency of agencies whose main, albeit not only, aim is to help connect supply to demand. It would also be useful for any economic agents with direct or indirect links to these agencies to be able to access information about their level of efficiency so that they can make informed decisions about investment and/or management. With this in mind, this project aims to analyse the efficiency of a group of travel agencies based in Alicante (Spain). Alicante was selected for the analysis for two main reasons: firstly, because it is an area of Spain where the tourism sector is of great importance, in terms of both supply and demand; and secondly, because the number of travel agencies in the area has increased at such an exceptionally high rate over the last few years that the number of agencies per capita has now more than doubled the national average. Between 2000 and 2007, the number of travel agencies in Alicante increased by 205.26%, while the total increase for the whole of Spain was just 67.41%. Furthermore, in 2007, the proportion of agencies per inhabitant was 0.05% in Alicante, compared with 0.022% for the whole of Spain (County Council of Alicante, 2009 and National Statistics Institute, 2009). A setting with such a high level of competitiveness was therefore a very suitable choice for the study of efficiency in this sort of agency. Then, based on the results of this analysis, it will assess the way in which certain characteristics of the agencies may affect their efficiency parameters. In particular, the study aims to ascertain whether or not the agency's ownership type, location or the length of time it has been in operation are factors which affect its level of efficiency. By examining these variables, the study will try to ascertain whether or not the fact that an agency forms part of a chain, is located in the city centre or has many years of experience in the sector are relevant factors when it comes to improving its level of efficiency.

This study has assessed the level of efficiency of the economic activity of travel agencies in the city of Alicante in the year 2007. It has also examined the relationship between certain variables (agency ownership type, location and experience) and the level of efficiency of those agencies. Data Envelopment Analysis, smoothed bootstrap and the Mann Whitney U Test were used for these purposes. In the first part of the study, relating to DEA efficiency levels, it was found that 7 of the 22 agencies assessed are efficient, representing 31.82% of the sample total. Having said that, the average level of efficiency is quite high (81.33%). However, some agencies have quite low levels of inefficiency (i.e. DMUs 1, 5, 10 and 11). Nevertheless, it is possible to increase these levels by reducing the amount of resources used and increasing the outputs produced by inefficient agencies until they reach the levels shown in Table 4. At the same time, a change required in the management strategy of each inefficient DMU in order to achieve these targets should be based on the strategies used in units which belong to its peer group, as this would reflect the efficient agencies that DEA has used as a reference in order to evaluate their level of efficiency. In any case, and given that it may be difficult for the inefficient agencies to obtain this information, the target values shown in Table 4 are the levels of the inputs and outputs which should have been used to identify operative responses to management priorities in order to implement strategies to make them efficient. However, smoothed bootstrap was also used to analyse the sensitivity of DEA estimates to sampling variation and to obtain measures of uncertainty relating to these estimates in order to confirm or refute the initial values. The bootstrap results are quite different from the initial DEA estimates. They also show that confidence intervals significantly change the conclusions reached earlier about efficiency levels, as frequent overlaps can make it impossible to rank DMUs in the same way as the DEA results obtained at the beginning. In fact, DMU17 and DMU20 have the highest scores based on their high bias-corrected value, despite the fact that the DEA results did not lead to them being classified as efficient (even though it gave them high View the MathML sourceφ⌢j values). In the second part of the study, which analysed the relationship between three different variables and the efficiency of the travel agencies, the results show that the location of an agency is the only one of these factors that affects their levels of efficiency. This shows that setting up an agency in areas close to the city centre has a major influence on the efficiency of that agency. As such, a higher concentration of the agencies around the city centre would imply an increase in their level of efficiency in spite of the fact that it could result in more elevated initial costs for them. In addition to this, no link was found between the levels of efficiency of agencies and their ownership type, nor with their level of experience. These results suggest that neither belonging to a group nor having a lot of experience affect the efficiency of an agency. In the first case, the result shows that the advantages of belonging to a group are not statistically significant. This means that agencies which form part of large chains may have access to management conditions which do not, in the end, have any influence on their efficiency. From this point of view, it would seem reasonable for units which form part of groups to look at the advantages that they should, in theory, enjoy because they belong to a larger organisation, to find out whether they are really materialising. In any case, including information about the profits that each agency makes may lead to a change in this conclusion. Unfortunately, it was not possible to obtain this type of data. In the second case (level of experience), the result obtained is similar to the first. This means that having more years of experience in the sector does not have a statistically significant link with the level of efficiency of agencies. This seems to suggest that, once an agency has overcome the logical first stage of learning and become established, increasing the amount of time that it has been operating in the sector does not necessarily mean that it becomes more efficient. With regard to the limitations of this study, the main drawback lies in the fact that not all of the information requested was obtained. It was impossible to increase the number of units analysed as higher response rates were not obtained. As a result, access to an official or public database which included the economic information required to carry out a study of this type may have made the task easier and improved results. In any case, the representativeness of the sample falls within the limits established in other similar studies (such as Hu & Cai, 2004 or Köksal & Aksu, 2007). In addition, another limitation of this study lies in the fact that there are a number of non-controllable factors which affect the efficiency of DMUs and which have not been taken into account here (such as the number of inhabitants in the area surrounding the agency, or their level of income). Access to information relating to these variables would make it possible to analyse their overall influence using DEA models which include non-controllable variables, or other complementary statistical methods (e.g. Tobit regression). Nevertheless, this study has attempted to incorporate new features not included in a number of other very good studies carried out on this sort of establishment (e.g. Barros and Dieke, 2007 and Köksal and Aksu, 2007 and Wöber, 2006). In particular, with regard to the variables used, the experience variable has been approached from the point of view of the experience acquired by an agency over the course of the period during which it has been operating in the sector. Furthermore, it has also been possible to incorporate a variable relating to the average spend per customer, which could be used to gain a better insight into the output level of each unit by combining it with the number of customers served by the agency during the period studied. In terms of the analysis method used, the target values for each variable were calculated using efficiency variables and slack variables, as opposed to using specific weights of each efficient DMU in the peer groups of each inefficient agency. This makes it possible to see where additional efforts need to be made in some resources even when the level of efficiency of an agency has been corrected. In addition to this, the smoothed bootstrap technique was also used to study the stability of the results after sampling variation and to carry out statistical inference. Finally, the analysis could be continued through the calculation of allocative and economic efficiency levels or even, if dynamic data could be obtained, through the calculation of changes in the levels of productivity. Clearly, this information could add to the results because, for example, Malmquist indices or quasi-Malmquist indices could be used in order to find out how the productivity levels of agencies develop over time, and the way in which inputs and outputs influence those levels. It would also be useful to include output-oriented analyses which could show the maximum production levels that DMUs could generate (with the amount of resources they use) if they demonstrate efficient behaviour.