The Austrian Hotel and Restaurant Panel
5.4 An Heuristic Procedure to Enhance the Basic AHRP System
5.4.1 The heuristic model
Traditional static forms of publications,even when they are posted on the Internet,do not allow the flexibility and interactivity which is necessary to build a system which adapts to individually varying information needs. There- fore,the author developed a system which offers this functionality by using a multi-attribute weighting model. This is an heuristic modelling approach as the weighting is basically derived by expert judgements (Wöber,1999). The objective of this case study was to introduce a system which could identify similar hotel and restaurant businesses and simultaneously guarantee at least a minimum level of representation decided on by the user. This work was also the starting point for research on the optimal selection of comparison partners in business performance studies that led to the present book.
Recalling the extended transformation model from Chapter 3,the main variables identified in this model were the outputY,the discretionary input XD,and the non-discretionary inputXN. There are several possible ways the relationships between these constructs can be measured and used for the identification of optimal (best practice) comparison partners.
When there seems to be prior knowledge of what is a more favourable environment,caused by XN combinations,the identification of an optimal comparison partner could be evaluated by finding an optimalXD→Yrelation- ship. The investigation of the environmental factors concerns questions such as which factors are decisive and how many industry sectors (markets) must be distinguished. This evaluation is obviously a stratification problem which needs to consider the (discretionary) input/output relationships. Therefore,all methodologies capable of handling this clustering problem are relevant for the optimal selection of comparison partners.
The stratification of surveys is in part to form groups as homogeneous as possible so that,where numbers allow,analysis of a stratum can occur and otherwise,based on estimates of stratum size,the reliability of the information obtained on aggregation is increased. The AHRP survey uses six criteria (S) in the weighting approach to establish homogeneity while preserving sample
size. The stratification is done on the basis of the type of services offered by the establishments,S1 (Table 5.5),the number of days of operation, S2,the geographical area where the enterprise is located,S3(Table 5.6),the ownership,S4(Table 5.7),the size,S5(measured by turnover and number of employees) and the category, S6(Table 5.8).
Selection of the comparability values is a crucial decision because it has a significant influence on the performance of the decision support system.
There is no real theory in hospitality research concerning the criteria that
1 2 3 4 5 6 7
12 34 56 7
Hotel garni Hotel incl. F&B Spa hotel Restaurant Inn, pub, tavern Cafe house/shop Espresso/bar
10080 600 00 0
10080 5040 100
10040 300 0
10080 5020
10080 50 100
80 100 Table 5.5. Types of service offered by the establishments in the AHRP.
1 2 3 4
12 34
Vienna
> 50,000 inhabitants 15,000–50,000 inhabitants
< 15,000 inhabitants
10080 6040
10080
60 100
80 100
Table 5.6. Geographical area where the enterprise is located.
1 2
12 Complete ownership
Rented or leased 100
70 100
Table 5.7. The ownership.
1 2 3 4 5
12 34 5
*****
****
***
**
*
10080 5030 10
10080 5030
10080
50 100
80 100
Table 5.8. Categorization scheme.
determine competitive pressures among hotel and restaurant enterprises. As a result the values had to be assigned by expert judgements in cooperation with the Austrian Professional Hotel and Restaurant Associations and various consultants specializing in hotel and restaurant operations.
A general comparability equation is suggested in the form of an additive function which also includes an additional weighting procedure to adapt to the situational needs of the user accessing the system. The total weighted comparability valueCfor each individual enterpriseiis:
Ci=
∑
6j=1S Wij i (5.1)The weights forw1, . . . ,w6are assigned by the user according to his require- ments by a simple scale from 0 to 3 (0 = not important,1 = less important, 2 = important, 3 = very important).
After rating each establishment represented in the panel database,the enterprises can be easily sorted by their comparability with the user’s case.
The number of units under evaluation and the homogeneity in the data set are determined by the number of establishments in the sample,which will be drawn after this sorting procedure. According to the user’s desire in the accuracy of the results he will specify a high or low number of establishments which will go into the sample file. This trade-off is made explicit by a bi-polar rating scale where the user is able to decide which of the these conflicting objectives has more importance for the decision problem in hand (Fig. 5.4).
In order to keep the programming effort simple the prototype system uses an integer 9-point-rating (r= 9) to decide on the relation between reliability and similarity. The necessary transformation also considers a minimum sam- ple size (nmin) of establishments (e.g. 30) to guarantee a certain level of preci- sion. The actual sample size (n) in accordance with the user’s importance of reliability (e) is calculated by
( )
n n e N n
= + −r
min
min (5.2)
whereNdenotes the total number of establishments in the panel database.
The respondents to the AHRP study form a sample which is not randomly selected but self-selected. There is little that can be done about this except to make every effort to achieve a high response rate. In Austria some information is available on the structure of the hotel and restaurant industry which is used to weight the sample in order to correct certain sources of bias. For example, in the AHRP study businesses with more employees tend to participate more frequently in the survey. To correct this misdistribution,information about the real size of hotels and restaurants is used; this is available through a regular survey of the Austrian Central Statistical Office.
In a random sample generated from a large population the size of the standard error depends on the size of the sample and is unrelated to the size of the population. For a manager interested in comparing his/her business data with composite figures derived from the database,it is certainly important
to have a confidence interval for the estimated industry sector ratio x*
under investigation. For example,when assuming key ratios to be normally distributed,a confidence interval at a 95% significance level (α= 0.05) can be defined by
x n x x
−1 96× ≤ ≤ +1 96× n
2 2
. σ * . σ (5.3)
forα= 0.05, andx N~
[ ]
0,σx2The homogeneity of the resulting sample can be expressed by an indicator, derived from Equation 5.1 and expressed in Equation 5.4.
C
S w
n w
ij j
j i
n
j j
=
×
×
=
=
=
∑
∑
∑
1 6
1
1 6
6
(5.4)
The indicator,which is standardized between 0 and 100 (100 = complete similarity with the case example entered by the user,0 = no comparable establishment found in the total panel data set),is displayed together with the key ratios calculated by the program. This homogeneity indicator helps the
Fig. 5.4. The user decides whether comparability or reliability is more important to his/her decision problem.
manager to understand the composition of the sample and hence supports him/her during the interpretation of the results (Fig. 5.5).
The desired level of precision may be selected by giving the amount of error that someone is willing to tolerate in sample estimates. This amount is determined in light of the uses to which the sample results are to be put. Sometimes it is difficult to decide how much error should be tolerated, particularly when the results have several different uses. In the present application,for instance,an entrepreneur who is interested in opening a new hotel will certainly have completely different information needs in comparison to a manager of an operating establishment. Part of the difficulty is that not enough is known about the consequences of errors of different sample sizes as their effect on the decisions are difficult to observe.
The advantage of the proposed system is that the user can go back and forth and learn from the output. He/she can change the sample size and,therefore,the reliability of the results,as well as the criteria which define the competitive situation he/she is facing. They are not bound to a strict classification as is usual in ordinary printed publications of panel studies.
Hence the user will soon realize that results may vary significantly,sometimes even through minor changes in his/her preliminary assumptions. Therefore, he/she can gain more insights and a better understanding of how to interpret benchmarking results and how to use them for managerial purposes.
Fig. 5.5. Sample output page of the hospitality benchmarking program.
There are several caveats to this very simple heuristic approach in the selection of benchmarking partners by the means of financial key ratios, which will be highlighted here and investigated more thoroughly in following chapters. First,a problem relates to the question whether there is the necessary relation between the homogeneity in the company sample and the number of units derived from the panel database. Decisions on the significance levels of confidence intervals and the necessary preciseness in the benchmarking results have a major impact on this problem. Obviously,the adjustments to get reasonably accurate estimates depend on the underlying application which has to be investigated.
Another problem arises during the weighting process of the various competitive criteria. It is clear that the ideal set of weights depends on the decision problem the user is faced with. However,someone could argue that the user might have difficulty in objectively estimating how relevant this is to their decision problem. In fact,first empirical tests of the prototype program showed that users tend to indicate that all criteria are very important for their benchmarking task. Future improvements of the system could incorporate an evaluation of the various decision problems and perform a self-determination of the weighting values. Therefore,poor or irrelevant weights by users could be replaced by system values fed back to users.