Evaluation of the quality of service and positioning of hotels

This research, entitled «Methodology for the evaluation of service quality and competitive positioning of hotel entities», is carried out with the aim of designing and applying a methodology for the competitive positioning of Hotel entities taking into account customer satisfaction as key indicator.

In the course of the investigation, questionnaires, the modified SERVQUAL model, the Kendall expert method and the Border models were used, which allowed the collection of information on the nine hotels under study, achieving as main results: The development of a methodology for the determination of the main problems that affect the quality of services in the hotel entities under study, the proposal of an improvement program for each facility of the different chains, which allows raising customer satisfaction and quality standards of services and the positioning of the 9 hotels under study within their chains, allowing them to be compared with leading hotels

Introduction

Tourism includes the activities that people carry out during their trips and stays in places other than their usual environment, for a consecutive period of less than one year, for leisure, business and other reasons. It constitutes a phenomenon of wide expansion, with significant economic and social repercussions for the world. In this expansive sector of the world economy, more than 74 million people are employed and about 600 million people participate in it per year, according to the World Tourism Organization (UNWTO).

Tourism is currently one of the branches of the economy that provides the most income and benefits, so it is predictable that it will increase in importance in the coming years compared to other industries. In the last decades it has transformed from an almost insignificant activity to a real field, with great influence on the balance of payments, on local investments and equipment, on the improvement of transport, on the generation of employment, this being one of the biggest social influences in the sector.

In the development of tourist activity, quality is of vital importance, since the needs and demands of humanity are constantly rising, with more demanding customers and in the face of a highly competitive world it is necessary to try to be the best within the best to survive, and this is not achieved if it is not by monitoring the quality of each of the services provided in the tourist facilities.

Any Quality Management System depends on the instruments applied to obtain information about the state of operation of the Quality System. The measurement instruments allow to better understand the object of analysis about which it is necessary to make certain decisions, make predictions about its development, measure the level reached by the activity that is being carried out and expose a certain problem.

One of the great difficulties existing in the tourism business sector is determining the competitive positioning of entities that belong to the same chain or between different chains using customer satisfaction and the quality of services as an output indicator.

Methodology for the Competitive Positioning of Hotel Entities.

Step 1. Diagnosis Summary

A summary of the difficulties diagnosed in the entities is made, both from the previous diagnosis and from the technical diagnosis. The Modified SERVQUAL model is used with its seven Gaps and it is applied to 9 hotels of two different chains. The diagnosed problems are quite common at the chain level and even between hotels. In general, it can be summarized as: little customer orientation, lack of management commitment to service quality, lack of a quality management system and consequently relatively high quality costs, fundamentally failure costs.

Step 2. Summary of the improvement program

From the main deficiencies detected in the diagnosis, a summary of the measures proposed for the improvement program of each chain and the hotels of the same is made; with the aim that they are implemented and require each entity to comply.

Step 3. Definition of input and output indicators.

Its main objective is to implement tools that facilitate the application of the improvement program in the diagnosed entities. For this, the border models are used with the aim of making a ranking that guides each entity to the reference hotel; applying benchmarking techniques based on the deficiencies detected and the variables used.

In order to determine the variables to be used for input and output, the following economic and service indicators used in hospitality are used, including:

Profit before tax, Profit after tax, Sales, Total Costs, Tourists / days, Average stay, Customer satisfaction,% occupancy, Number of Rooms and physical Tourists.

To determine the indicators to be used, 9 experts in hotel management were selected, all with more than 10 years linked to the activity, applying the Kendall method, a concordance coefficient of 0.85 (indicating that the study was valid) was obtained, remaining as Indicators. Resulting: Earnings before taxes, Sales, Total Costs, Customer Satisfaction and Number of rooms

Subsequently, it was decided to use as input variables: the number of rooms and the total costs of quality and as output: customer satisfaction and sales. The total costs indicator is replaced by total quality costs that were determined in each of the entities studied. Profit before tax is used as a basic element of verification, when comparing the analysis of efficiency rankings using the four variables, with the ranking prepared to measure efficiency in quality management that uses only two variables (costs quality as input and customer satisfaction as output).

Table # 1. Input and output indicators of both chains.

Entities	Quality Cost	# of rooms	Sales ($)	Customer satisfaction
H1	235871	147	823300	-0.313
H2	403920	254	2019 600	0.190
H3	283912	103	1822400	0.200
H4	757423	173	2494400	0.090
H5	848874	366	3264900	-0.127
H6	854360	273	3286100	0.141
H7	233038	78	896300	0.141
H8	448452	264	1401400	0.290
H9	240128	121	950000	0.010

Step 4. Definition of the period to evaluate, the size of the sample and the orientation of the sampling.

The study is carried out from a sample taken in the hotels of the different chains that includes clients who visited them from February to April 2003. The sample is calculated based on the population that visited the pole in that period. It works with an error of 4% in order to be able to obtain conclusive and detailed results for each market segment, the real error obtained was 3.7% due to the fact that more surveys were carried out than expected. We work with a confidence level of 95% and with values of P = Q = 0.5, giving the same probability of existence of a satisfied client as dissatisfied. According to these data, a sample magnitude of 1193 is obtained and 1196 are actually made. The quotas are determined by Paretto,using as an economic effect the purchases made by each issuing country.

Table # 2. Commercial Research Sheet.

Technique	Commercial investigation.
Methodological procedure	Surveys through questionnaires with closed questions.
Universe	Tourists, over 18 years of age and of both sexes staying in hotels.
Ambit	Hotel facilities.
Sample size	1193 valid surveys
Sample error	4%
Confidence level	95%, Z = 1.96 p = q = 0.5
Sample design	Non-probabilistic by quotas with allocation proportional to the number of tourists by nationality that visit the hotels. Surveys were carried out in tourist establishments.
Fieldwork Date	February 1 - April 2003

Step 5. Application of the Border models.

The evaluation of the efficiency in the quality management of the diagnosed chains is carried out through the application of the Border Models. It is really difficult to measure efficiency with the use of isolated indicators and even with ratios, even more in relation to quality due to the lack of culture, and the lack of records and records and the unreliability of the instruments used to assess customer satisfaction..

Border Models allow the efficiency of several homogeneous units (Hotels) known as DMUs to be analyzed through the use of predetermined variables that can be input (input) and other output (output); at the same time they indicate the units that can be taken as a comparison reference regarding the efficiency index.

In this case, DEA models are used, which are characterized by defining convex empirical production boundaries. The convexity assumption of DEA models is based on the fact that if two units can produce outputs from the consumption of inputs, it is also possible to achieve a feasible unit by establishing linear weights or combinations between them.

This model is one of the most widely used and known and its application is decided because it has a greater discriminating power than the FDH model when working with few DMUs, in this case it only works with 9 units.

The distance to the border of DEA models can be calculated radially and non-radially regardless of the orientation towards the input or towards the output or not oriented. A radial model means that the reduction of the input or the increase of the output is the same for all the elements. In contrast, non-radial models calculate particular reduction coefficients for each input or increase coefficients for each output. In any case, the global efficiency index is the average of these reduction or increase coefficients, respectively, and can be weighted in the case of non-radial models.

The following table shows a general ranking for the two chains.

Table # 3. Ranking for both chains using the Non-Oriented Radial model.

DMU	Scores	Costs {I} {V}	# Habit. {I} {V}	Sales {O} {V}	Satisfaction {O} {V}	Benchmarking
H1	29.55%	235871	147	823300	-0.313	3 (0.59)
H2	12.43%	403920	254	2019 600	0.190	3 (1.25)
H3	-23.49%	283912	103	1822400	0.200	8
H4	10.20%	757423	173	2494400	0.090	3 (1.51)
H5	25.06%	848874	366	3264900	-0.127	3 (2.24)
H6	19.03%	854360	273	3286100	0.141	3 (2.15)
H7	3.58%	233038	78	896300	0.141	3 (0.73)
H8	4.28%	448452	264	1401400	0.290	3 (1.51)
H9	23.74%	240128	121	950000	0.010	3 (0.65)

Analyzing the results of the 9 valued DMU's, it can be seen that there is only one efficient unit, this being H3, this can be determined through the Score, since it is the hotel that achieves the lowest value for this indicator, dominating the rest of the DMU's, In other words, the reference hotel for the improvement program remains the H3 for both chains.

Table # 4. Radial Model oriented to Inputs.

DMU	Scores	Costs {I} {V}	# Habit. {I} {V}	Sales {O} {V}	Satisfaction {O} {V}	Benchmarking
H1	54.38%	235871	147	823300	-0.313	3 (0.45)
H2	77.90%	403920	254	2019 600	0.190	3 (1.11)
H3	161.40%	283912	103	1822400	0.200	8
H4	81.49%	757423	173	2494400	0.090	3 (1.37)
H5	59.92%	848874	366	3264900	-0.127	3 (1.79)
H6	68.03%	854360	273	3286100	0.141	3 (1.80)
H7	93.10%	233038	78	896300	0.141	3 (0.70)
H8	91.80%	448452	264	1401400	0.290	3 (1.45)
H9	61.63%	240128	121	950000	0.010	3 (0.52)

Table # 5. Radial Model oriented to the Outputs.

DMU	Score	Cost {I} {V}	# Habit. {I} {V}	sale {O} {V}	satisfaction {O} {V}	Benchmarks
H1	183.90%	235871	147	823300	-0.313	3 (0.83)
H2	128.38%	403920	254	2019 600	0.190	3 (1.42)
H3	61.96%	283912	103	1822400	0.200	8
H4	122.71%	757423	173	2494400	0.090	3 (1.68)
H5	166.89%	848874	366	3264900	-0.127	3 (2.99)
H6	146.99%	854360	273	3286100	0.141	3 (2.65)
H7	107.42%	233038	78	896300	0.141	3 (0.76)
H8	108.93%	448452	264	1401400	0.290	3 (1.58)
H9	162.25%	240128	121	950000	0.010	3 (0.85)

Table # 6. Additive Model Not Oriented.

DMU	Score	Cost {I} {V}	# habit {I} {V}	sale {O} {V}	satisfaction {O} {V}	Benchmarks
H1	690 792.00	235871	147	823300	-0.313	3 (0.83)
H2	573 225.75	403920	254	2019 600	0.190	3 (1.42)
H3	0.00	283912	103	1822400	0.200	8
H4	847085.62	757423	173	2494400	0.090	3 (1.68)
H5	2183987,90	848874	366	3264900	-0.127	3 (2.99)
H6	1,646,000.43	854360	273	3286100	0.141	3 (2.65)
H7	501,806.59	233038	78	896300	0.141	3 (0.76)
H8	1,477,265.55	448452	264	1401400	0.290	3 (1.58)
H9	591 389.35	240128	121	950000	0.010	3 (0.85)

Table # 7. Input-Oriented Additive Model.

DMU	Score	Cost {I} {V}	# habit {I} {V}	sale {O} {V}	satisfaction {O} {V}	Benchmarks
H1	107,709.42	235871	147	823300	-0.313	3 (0.45)
H2	89 426.04	403920	254	2019 600	0.190	3 (1.11)
H3	0.00	283912	103	1822400	0.200	8
H4	368 852.03	757423	173	2494400	0.090	3 (1.37)
H5	340 416.16	848874	366	3264900	-0.127	3 (1.79)
H6	34,250.21	854360	273	3286100	0.141	3 (1.80)
H7	32,885.43	233038	78	896300	0.141	3 (0.70)
H8	36,894.25	448452	264	1401400	0.290	3 (1.45)
H9	92,194.65	240128	121	950000	0.010	3 (0.52)

Table # 8. Output-Oriented Additive Model.

DMU	Score	Cost {I} {V}	# habit. {I} {V}	sale {O} {V}	satisfaction {O} {V}	Benchmarks
H1	690 730.57	235871	147	823300	-0.313	3 (0.83)
H2	573 118.29	403920	254	2019 600	0.190	3 (1.42)
H3	0.00	283912	103	1822400	0.200	8
H4	566 524.52	757423	173	2494400	0.090	3 (1.68)
H5	2183929.86	848874	366	3264900	-0.127	3 (2.99)
H6	1544 145.05	854360	273	3286100	0.141	3 (2.65)
H7	483,769.91	233038	78	896300	0.141	3 (0.76)
H8	1477 164.25	448452	264	1401400	0.290	3 (1.58)
H9	591 355,46	240128	121	950000	0.010	3 (0.85)

Step 6. Measurement of efficiency using proprietary quality management indicators.

In order to evaluate the efficiency of quality management in the different DMU's, DEA models are applied using total quality costs as input and customer satisfaction as output. This can provide a measure of the cost that organizations must incur to achieve customer satisfaction.

This analysis is essential because it is stated with great emphasis that customer satisfaction or perceived quality is a determining factor for the competitiveness of companies and therefore for the benefits obtained by the organization. Some studies have tested this assumption like those made by PIMS, but managers have not really become aware of this interrelation.

Really, quality results should be expected in the medium and long term, never in the short term.

Tables 9 and 10 show these results below.

Table # 9. Ranking based on Customer Satisfaction, Non-Oriented Radial Model.

DMU	Score%	Cost {I} {V}	Total {O} {V}	Tang. {O} {V}	Fiab. {O} {V}	Capac {O} {V}	Secure {O} {V}	Empa {O} {V}	BMk
H1	100	235871	-0.31	-0.29	-0.25	-0.280	-0.419	-0.316
H2	9.75	403920	0.192	0.179	0.148	0.200	0.224	0.206	3 (1.28)
H3	-12.65	283912	0.200	0.153	0.156	0.206	0.296	0.192	5
H4	58.15	757423	0.090	0.108	0.050	0.083	0.107	0.100	3 (1.12)
H5	100	848874	-0.12	-0.12	-0.15	-0.143	-0.132	-0.083
H6	44.46	854360	0.141	0.177	0.113	0.131	0.151	0.133	3 (1.67)
H7	2.01	233038	0.141	0.096	0.123	0.155	0.189	0.145	3 (0.80)
H8	-4.22	448452	0.29	0.24	0.24	0.31	0.34	0.33	one
H9	46.53	240128	0.012	-0.05	-0.07	0.049	0.083	0.061	8 (0.10)

According to the results achieved, there are two efficient hotels: Firstly, the H3 hotel and, to a lesser extent, the H8 Hotel, this can be corroborated with the results of the Scores reached, which are the lowest registered in the table. Hotel H3 dominates 5 DMU's, which are: Hotel H2, H4, H6, H7 and H9; Hotel H8 dominates only Hotel H9. In this case the DMU's is dominated by two hotels, the H3 and the H8, but with higher priority than the H3 since it has a higher lambda value, which is the reference intensity.

The H5 and H1 hotels present the maximum Score, that is 100%, indicating this as we are in a Non-Oriented Radial model that the DMU's are totally inefficient, that is, they are far from the efficiency frontier. Below is a comparison in table # 10 between the efficiency ranking using the four variables, with the ranking prepared to measure the efficiency in quality management that uses only two variables (quality costs as input and satisfaction client as output).

Table # 10. Comparison between Utility, Scores, # of rooms and Benchmarking.

DMU	Score	Income before tax	# of rooms	Benchmarking
H1	100%	-383800	147
H2	9.75%	65600	254	3 (1.28)
H3	-12.65%	29600	103	5
H4	58.15%	196800	173	3 (1.12)
H5	100%	233600	366
H6	44.46%	338400	273	3 (1.67)
H7	2.01%	108 600	78	3 (0.80)
H8	-4.22%	2004500	264	one
H9	46.53%	706100	121	3 (0.30) 8 (0.10)

A more general analysis can clarify the results achieved. If quality is incorporated into the results obtained in the final ranking of both chains that show the efficiency in the quality management of each of the DMU's, it can be seen that there is a correspondence between the level of profits and the places reached in the ranking. that define the efficiency of each hotel.

Efficient or leading hotels achieve profits before tax of 29,600 in H3 and 200,400 in H8, to a greater extent the H8 because it has a greater number of rooms. However, the H1 hotel, one of the most inefficient and with a Score of 100%, makes losses. Another of the hotels with the highest score is the H5, which reaches utilities with a value of 233.6 MP with 366 rooms; and when compared to the H6 hotel, it gets more utility (338.4 MP) with 93 fewer rooms. It is for this reason that its place in the ranking is more favorable and has a lower Score.

The analysis carried out corroborates the decision to use the non-oriented modality because it considers both input and output. This does not exclude the use of Oriented models for subsequent analysis when the difficulties that affect efficiency management in each organization have actually been determined, either due to excess input consumption or output difficulties.

The use of these models is valid and can be very useful to guide the improvement program, but the correct selection of the input and output variables, as well as the orientation of the models, is decisively important. Because it is difficult to define the overall efficiency of an organization using very specific indicators or ratios and without an overall analysis, which is achieved through the application of these border models. It should also be borne in mind that in most of the DMUs analyzed there is no certified accounting, which can cause a false valuation of the inputs and even of the utility. It is for this reason that it was decided to use as input the determined and estimated quality costs in each hotel and the # of rooms thereof.The units within each chain were diagnosed and a summary of the difficulties for each of them was prepared. As well as the proposal for an improvement program to solve these difficulties. But it is very useful to be able to determine the reference DMU's both within the same chain and between them, this would greatly facilitate the implementation of the improvement program.

Despite these benefits of border models, it is recognized that the results obtained must be analyzed carefully and using various approaches, including comparing it with the indicators or economic ratios that have been achieved in the period and with the objective and practical conditions with which it operates. each installation. For example, the leading or reference hotel for both chains turned out to be the H3 operates with a very specific market segment, its service system is designed only for this market, which does not happen with the rest of the hotels, which operate with several markets simultaneously. This difference is significant and must be taken into account.

Conclusions

1. In the analysis of the ranking of both chains, it was possible to determine that the H3 hotel is the leader and the only efficient one. When analyzing the efficiency, specifically in the quality management for both chains, it was possible to specify that there are 2 efficient DMUs and that they dominate the rest; as well as its ordering based on the score obtained.

2. The correspondence between the rankings that evaluate the general efficiency of the organization (with 4 variables) and the one that evaluates the efficiency in quality management (which comprises only 2 variables) is verified by correlation and the results of the Kendall coefficient.

3. The effectiveness of the ranking results could be verified by comparing with real economic indicators obtained by the entities, in particular the profit before tax, which corroborate the results achieved.