Performance Measurement and Benchmarking of Large-Scale Tourist Hotels

Tien-Chin Wang; Huang Shu-Li

doi:doi:10.11648/j.ijber.20180704.13

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Performance Measurement and Benchmarking of Large-Scale Tourist Hotels

Tien-Chin Wang, Huang Shu-Li

Published in International Journal of Business and Economics Research (Volume 7, Issue 4)

Received: 9 August 2018 Published: 13 August 2018

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Abstract

Taiwanese tourism policy underwent a major change in 2008 when restrictions were gradually relaxed on Chinese tourists visiting Taiwan. According to the Tourism Bureau of Taiwan’s statistics, the overall number of mainland tourists increased from 329,204 in 2008 to 4,184,102 in 2015; however, there was a 16.1% reduction (670,000) occurring in 2016. This significant event will cause more harm than good to Taiwan’s all-important tourism industry. In response to such contractions, this study applied cluster analysis combined with entropy to derive suitable clusters useful towards identifying the best market performers among hoteliers through a measurement of large-scale tourist hotels’ operational performance. This may signal a benchmark for the improvement of poor performance hotels. Entropy is used as an objective weight method to calculate the relative importance of all salient attributes by comparing the entropy values of each given attribute. Large-scale international tourist hotels have become the market mainstream in Taiwan; therefore, 17 tourist hotels with more than 5000 employees yearly were selected to become part of this study. Operational performance was measured by attributive means of occupancy rate, average room rate, average production-value-per-employee, total number of domestic tourists, and total number of foreign tourists (including overseas Chinese). A significant F value of the ANOVA analysis indicates that there is at least one significant difference found between the two clusters. Further post-hoc analysis uses the Scheffé method to identify any difference found between clusters and to determine the best performance cluster useful as a benchmark. The methods of this study are different from those of previous studies because of the use of a Data Envelopment Analysis (DEA) technique or mix, while there is also applied cluster analysis combined with entropy. Clear indicators are deemed useful for exacting improvement standards among under-performing tourist hotel properties.

Published in	International Journal of Business and Economics Research (Volume 7, Issue 4)
DOI	10.11648/j.ijber.20180704.13
Page(s)	97-101
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Performance Measurement, Cluster Analysis, Entropy, Tourist Hotels

References

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[2]	E. Claver-Cortés, J. F. Molina-Azorín, J. Pereira-Moliner, “The impact of strategic behaviours on hotel performance,” International Journal of Contemporary Hospitality Management, 19 (1), 2007, pp.6-20.
[3]	Y. L. Lin, S. Y. Chen, “Data Mining of Operation Performance of Tourist Hotels in Taiwan Journal of Tourism and Leisure Management 2, 2014, pp.20-29.
[4]	H. Cheng, Y. C. Lu, J. T. Chung, “Performance benchmarking by improved slack-based context dependent DEA for the hotel industry in Taiwan,” Management Review, 28, 2009, pp.141-146.
[5]	J. Wu, H. Tsai, Z. Zhou, “Improving efficiency in international tourist hotels in Taipei using a non-radial DEA model,” International Journal of Contemporary Hospitality Management, 23 (1), 2011, pp.66-83.
[6]	C. Bernini and A. Guizzardi, “Improving performance measurement and benchmarking in the accommodation sector,” International Journal of Contemporary Hospitality Management, 27 (5), 2015, pp. 980-1002.
[7]	W. E. Chiang, M. H. Tsai, L. S. M. Wang, “A DEA evaluation of Taipei hotels,” Annals of Tourism Research 31 (3), 2004, pp.712-715.
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[9]	W. W. Wu, L. W. Lan, Y. T. Lee, “Benchmarking hotel industry in a multi-period context with DEA approaches: A case study,” Benchmarking: An International Journal, 20 (2), 2013, pp.152-168.
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[15]	G. SANTORO, “Evaluating performance in the hotel industry: An empirical analysis of Piedmont.” Journal of Investment and Management, 4, 2015, 17-22.
[16]	S. N. Hwang, T. Y. Chang, “Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan,” Tourism management, 24 (4), 2003, pp.357-369.
[17]	K. W. Wöber, “Benchmarking for tourism organizations. An eGuide for Tourism Managers,” National Laboratory for Tourism and eCommerse. University of Illinois at Urbana-Champaign. Osoitteessa http://fama2. us. es, 2011, 8080.
[18]	C. Yang, W. M. Lu, “Performance benchmarking for Taiwan’s international tourist hotels,” INFOR: Information Systems and Operational Research, 44.3: 2006, pp.229-245.
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APA Style

Tien-Chin Wang, Huang Shu-Li. (2018). Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. International Journal of Business and Economics Research, 7(4), 97-101. https://doi.org/10.11648/j.ijber.20180704.13

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ACS Style

Tien-Chin Wang; Huang Shu-Li. Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. Int. J. Bus. Econ. Res. 2018, 7(4), 97-101. doi: 10.11648/j.ijber.20180704.13

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AMA Style

Tien-Chin Wang, Huang Shu-Li. Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. Int J Bus Econ Res. 2018;7(4):97-101. doi: 10.11648/j.ijber.20180704.13

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@article{10.11648/j.ijber.20180704.13,
  author = {Tien-Chin Wang and Huang Shu-Li},
  title = {Performance Measurement and Benchmarking of Large-Scale Tourist Hotels},
  journal = {International Journal of Business and Economics Research},
  volume = {7},
  number = {4},
  pages = {97-101},
  doi = {10.11648/j.ijber.20180704.13},
  url = {https://doi.org/10.11648/j.ijber.20180704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20180704.13},
  abstract = {Taiwanese tourism policy underwent a major change in 2008 when restrictions were gradually relaxed on Chinese tourists visiting Taiwan. According to the Tourism Bureau of Taiwan’s statistics, the overall number of mainland tourists increased from 329,204 in 2008 to 4,184,102 in 2015; however, there was a 16.1% reduction (670,000) occurring in 2016. This significant event will cause more harm than good to Taiwan’s all-important tourism industry. In response to such contractions, this study applied cluster analysis combined with entropy to derive suitable clusters useful towards identifying the best market performers among hoteliers through a measurement of large-scale tourist hotels’ operational performance. This may signal a benchmark for the improvement of poor performance hotels. Entropy is used as an objective weight method to calculate the relative importance of all salient attributes by comparing the entropy values of each given attribute. Large-scale international tourist hotels have become the market mainstream in Taiwan; therefore, 17 tourist hotels with more than 5000 employees yearly were selected to become part of this study. Operational performance was measured by attributive means of occupancy rate, average room rate, average production-value-per-employee, total number of domestic tourists, and total number of foreign tourists (including overseas Chinese). A significant F value of the ANOVA analysis indicates that there is at least one significant difference found between the two clusters. Further post-hoc analysis uses the Scheffé method to identify any difference found between clusters and to determine the best performance cluster useful as a benchmark. The methods of this study are different from those of previous studies because of the use of a Data Envelopment Analysis (DEA) technique or mix, while there is also applied cluster analysis combined with entropy. Clear indicators are deemed useful for exacting improvement standards among under-performing tourist hotel properties.},
 year = {2018}
}

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TY  - JOUR
T1  - Performance Measurement and Benchmarking of Large-Scale Tourist Hotels
AU  - Tien-Chin Wang
AU  - Huang Shu-Li
Y1  - 2018/08/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijber.20180704.13
DO  - 10.11648/j.ijber.20180704.13
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 97
EP  - 101
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20180704.13
AB  - Taiwanese tourism policy underwent a major change in 2008 when restrictions were gradually relaxed on Chinese tourists visiting Taiwan. According to the Tourism Bureau of Taiwan’s statistics, the overall number of mainland tourists increased from 329,204 in 2008 to 4,184,102 in 2015; however, there was a 16.1% reduction (670,000) occurring in 2016. This significant event will cause more harm than good to Taiwan’s all-important tourism industry. In response to such contractions, this study applied cluster analysis combined with entropy to derive suitable clusters useful towards identifying the best market performers among hoteliers through a measurement of large-scale tourist hotels’ operational performance. This may signal a benchmark for the improvement of poor performance hotels. Entropy is used as an objective weight method to calculate the relative importance of all salient attributes by comparing the entropy values of each given attribute. Large-scale international tourist hotels have become the market mainstream in Taiwan; therefore, 17 tourist hotels with more than 5000 employees yearly were selected to become part of this study. Operational performance was measured by attributive means of occupancy rate, average room rate, average production-value-per-employee, total number of domestic tourists, and total number of foreign tourists (including overseas Chinese). A significant F value of the ANOVA analysis indicates that there is at least one significant difference found between the two clusters. Further post-hoc analysis uses the Scheffé method to identify any difference found between clusters and to determine the best performance cluster useful as a benchmark. The methods of this study are different from those of previous studies because of the use of a Data Envelopment Analysis (DEA) technique or mix, while there is also applied cluster analysis combined with entropy. Clear indicators are deemed useful for exacting improvement standards among under-performing tourist hotel properties.
VL  - 7
IS  - 4
ER  -

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Author Information

Tien-Chin Wang

Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Huang Shu-Li

Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

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Tien-Chin Wang, Huang Shu-Li. (2018). Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. International Journal of Business and Economics Research, 7(4), 97-101. https://doi.org/10.11648/j.ijber.20180704.13

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ACS Style

Tien-Chin Wang; Huang Shu-Li. Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. Int. J. Bus. Econ. Res. 2018, 7(4), 97-101. doi: 10.11648/j.ijber.20180704.13

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AMA Style

Tien-Chin Wang, Huang Shu-Li. Performance Measurement and Benchmarking of Large-Scale Tourist Hotels. Int J Bus Econ Res. 2018;7(4):97-101. doi: 10.11648/j.ijber.20180704.13

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@article{10.11648/j.ijber.20180704.13,
  author = {Tien-Chin Wang and Huang Shu-Li},
  title = {Performance Measurement and Benchmarking of Large-Scale Tourist Hotels},
  journal = {International Journal of Business and Economics Research},
  volume = {7},
  number = {4},
  pages = {97-101},
  doi = {10.11648/j.ijber.20180704.13},
  url = {https://doi.org/10.11648/j.ijber.20180704.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20180704.13},
  abstract = {Taiwanese tourism policy underwent a major change in 2008 when restrictions were gradually relaxed on Chinese tourists visiting Taiwan. According to the Tourism Bureau of Taiwan’s statistics, the overall number of mainland tourists increased from 329,204 in 2008 to 4,184,102 in 2015; however, there was a 16.1% reduction (670,000) occurring in 2016. This significant event will cause more harm than good to Taiwan’s all-important tourism industry. In response to such contractions, this study applied cluster analysis combined with entropy to derive suitable clusters useful towards identifying the best market performers among hoteliers through a measurement of large-scale tourist hotels’ operational performance. This may signal a benchmark for the improvement of poor performance hotels. Entropy is used as an objective weight method to calculate the relative importance of all salient attributes by comparing the entropy values of each given attribute. Large-scale international tourist hotels have become the market mainstream in Taiwan; therefore, 17 tourist hotels with more than 5000 employees yearly were selected to become part of this study. Operational performance was measured by attributive means of occupancy rate, average room rate, average production-value-per-employee, total number of domestic tourists, and total number of foreign tourists (including overseas Chinese). A significant F value of the ANOVA analysis indicates that there is at least one significant difference found between the two clusters. Further post-hoc analysis uses the Scheffé method to identify any difference found between clusters and to determine the best performance cluster useful as a benchmark. The methods of this study are different from those of previous studies because of the use of a Data Envelopment Analysis (DEA) technique or mix, while there is also applied cluster analysis combined with entropy. Clear indicators are deemed useful for exacting improvement standards among under-performing tourist hotel properties.},
 year = {2018}
}

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TY  - JOUR
T1  - Performance Measurement and Benchmarking of Large-Scale Tourist Hotels
AU  - Tien-Chin Wang
AU  - Huang Shu-Li
Y1  - 2018/08/13
PY  - 2018
N1  - https://doi.org/10.11648/j.ijber.20180704.13
DO  - 10.11648/j.ijber.20180704.13
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 97
EP  - 101
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20180704.13
AB  - Taiwanese tourism policy underwent a major change in 2008 when restrictions were gradually relaxed on Chinese tourists visiting Taiwan. According to the Tourism Bureau of Taiwan’s statistics, the overall number of mainland tourists increased from 329,204 in 2008 to 4,184,102 in 2015; however, there was a 16.1% reduction (670,000) occurring in 2016. This significant event will cause more harm than good to Taiwan’s all-important tourism industry. In response to such contractions, this study applied cluster analysis combined with entropy to derive suitable clusters useful towards identifying the best market performers among hoteliers through a measurement of large-scale tourist hotels’ operational performance. This may signal a benchmark for the improvement of poor performance hotels. Entropy is used as an objective weight method to calculate the relative importance of all salient attributes by comparing the entropy values of each given attribute. Large-scale international tourist hotels have become the market mainstream in Taiwan; therefore, 17 tourist hotels with more than 5000 employees yearly were selected to become part of this study. Operational performance was measured by attributive means of occupancy rate, average room rate, average production-value-per-employee, total number of domestic tourists, and total number of foreign tourists (including overseas Chinese). A significant F value of the ANOVA analysis indicates that there is at least one significant difference found between the two clusters. Further post-hoc analysis uses the Scheffé method to identify any difference found between clusters and to determine the best performance cluster useful as a benchmark. The methods of this study are different from those of previous studies because of the use of a Data Envelopment Analysis (DEA) technique or mix, while there is also applied cluster analysis combined with entropy. Clear indicators are deemed useful for exacting improvement standards among under-performing tourist hotel properties.
VL  - 7
IS  - 4
ER  -

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