Measuring Hotel Service Productivity Using Two-Stage Network DEA

For the sustainable development of the service industry, the industry’s resources must be allocated efficiently so that productivity can be steadily improved. Accurate measurement of productivity is very important for the sustainable growth of the industry because it can be used as basic information for optimizing resource allocation, but in the service industry, it is difficult to accurately reflect consumer feedback due to simultaneity of service. This study aims to overcome these limitations and present a new service productivity measurement model. To this end, we set service quality as an intermediate and used two-stage network DEA, which can better reveal the impact of service quality in productivity than existing DEA models. The subjects of the study were 57 hotels, and they were analyzed according to service level. The analysis revealed that 2- and 3-star hotels with relatively low service levels were efficiently using the given resources to create service quality but were unable to effectively generate sales due to low unit prices. However, 4- and 5-star hotels with relatively high service levels received low service quality evaluations compared to the given resources and effectively generated sales through high per-guest prices. This study has academic significance in that it empirically demonstrated that including service quality as an intermediate is a more effective method for measuring productivity in the hotel industry. In addition, this study suggests practical implications in that high-star hotels need to allocate appropriate resources to improve service quality, and low-star hotels need an appropriate price strategy that is in line with service quality.

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Publication Details

Journal
Sustainability
Published
2024-10-17
DOI
https://doi.org/10.3390/su16208995
Citations
4
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
1.96

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article

Measuring Hotel Service Productivity Using Two-Stage Network DEA

Kyu-Wan Choi, JinKwon Kim
4 citations
Sustainability
Efficiency Analysis Using DEA
1.96
article

Measuring Hotel Service Productivity Using Two-Stage Network DEA

Kyu-Wan Choi, JinKwon Kim
article en
4 citations

Abstract

For the sustainable development of the service industry, the industry’s resources must be allocated efficiently so that productivity can be steadily improved. Accurate measurement of productivity is very important for the sustainable growth of the industry because it can be used as basic information for optimizing resource allocation, but in the service industry, it is difficult to accurately reflect consumer feedback due to simultaneity of service. This study aims to overcome these limitations and present a new service productivity measurement model. To this end, we set service quality as an intermediate and used two-stage network DEA, which can better reveal the impact of service quality in productivity than existing DEA models. The subjects of the study were 57 hotels, and they were analyzed according to service level. The analysis revealed that 2- and 3-star hotels with relatively low service levels were efficiently using the given resources to create service quality but were unable to effectively generate sales due to low unit prices. However, 4- and 5-star hotels with relatively high service levels received low service quality evaluations compared to the given resources and effectively generated sales through high per-guest prices. This study has academic significance in that it empirically demonstrated that including service quality as an intermediate is a more effective method for measuring productivity in the hotel industry. In addition, this study suggests practical implications in that high-star hotels need to allocate appropriate resources to improve service quality, and low-star hotels need an appropriate price strategy that is in line with service quality.

SustainabilityVol. 16(20)
Kyung Hee University (KR)
Kyung Hee University
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Efficiency Analysis Using DEA
1.96
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