# Sub-sector, property rights system, and performance of China’s tourism enterprises: DEA and Luenberger approach

**Authors:** Liyang Yang, Nan Zhu, Huiru Bai

PMC · DOI: 10.1371/journal.pone.0320928 · PLOS One · 2025-04-03

## TL;DR

This study examines the efficiency and productivity of China's tourism enterprises using new methods and finds differences based on sub-sectors and ownership types.

## Contribution

The study is the first to apply DEA and the Luenberger index to analyze tourism enterprise performance in China.

## Key findings

- Tourism enterprises show high efficiency and progress in total factor productivity.
- Significant efficiency differences exist among sub-sectors and between state-owned and non-state-owned enterprises.
- A decision-making matrix helps enterprises identify their performance position and improve strategies.

## Abstract

This study is designed to delve into the efficiency and total factor productivity (TFP) of tourism enterprises, aiming to uncover the sources of variation in these metrics. To meet this objective, we have employed the data envelopment analysis (DEA) approach and the Luenberger index—making it the inaugural application within the tourism industry—to assess efficiency and TFP. Our findings indicate that the tourism industry boasts high efficiency, with progress in TFP and its component factors. In our quest to identify the determinants of efficiency and TFP, the research has taken into account sub-sectors and the property rights system. It has been revealed that there are notable efficiency disparities among different sub-sectors, while differences in TFP are not significant. When examining the property rights system, we found significant efficiency and TFP differences between state-owned enterprises (SOEs) and non-SOEs. To the best of our knowledge, this research pioneers the application of nonparametric analysis to differentiate the performance disparities in tourism enterprises attributable to the property rights system. Furthermore, we have constructed a decision-making matrix that incorporates an enterprise’s efficiency, TFP, and scale. This matrix allows each enterprise to precisely pinpoint its position, enabling managers to formulate targeted strategies for performance enhancement.

## Full-text entities

- **Genes:** INHCAP (inhibitor of carbonic anhydrase pseudogene) [NCBI Gene 100129696] {aka TFP, TFP1}
- **Diseases:** SBM (MESH:D016410), infections (MESH:D007239), EC (MESH:D009402), COVID-19 (MESH:D000086382)
- **Chemicals:** EC (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

72 references — full list in the complete paper: https://tomesphere.com/paper/PMC11967969/full.md

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Source: https://tomesphere.com/paper/PMC11967969