ArticleInternational journal of environmental research and public health2022
Exploring Tourism Efficiency and Its Drivers to Understand the Backwardness of the Tourism Industry in Gansu, China.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Research on the impact of China's reform to delegate power, streamline administration, and optimize government services on the technology innovation efficiency of the pharmaceutical manufacturing industry.Frontiers in public health · 2024Article
- Spatiotemporal evolution and mechanisms of tourism efficiency and its decomposition: Evidence from 63 counties in Zhejiang, China.PloS one · 2024Article
- Analyzing the tourism efficiency and its influencing factors of China's coastal provinces.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gansu Province is rich in tourism resources, and it is the hometown of the "copper galloping horse", which is the logo of China's tourism. However, the scale and revenues of tourism in Gansu province are still at a low level. This paper first evaluated the tourism efficiency of 14 cities and prefectures of Gansu Province in China from 2011 to 2019 using the super-slack-based measure (Super-SBM) and then investigated the internal driving mechanism of the efficiency change through the Global Malmquist-Luenberger (GML) index and its decomposition, and finally analyzed the external influencing elements of tourist efficiency by the Tobit model. The results revealed that the tourism efficiency of Gansu Province had increased rapidly during the study period, especially after 2016, the rising range increased. From 2011 to 2019, the cumulative changes in GML index, technological change (TC), and efficiency change (EC) of tourism efficiency in Gansu Province were 5.053, 4.145 and 1.160, respectively, indicating that the improvement of tourism efficiency in Gansu province is mainly due to technological progress. The regression results of the Tobit model show that the status of the tourism industry, trade openness, information level, and technological innovation level can significantly promote tourism efficiency in the province. At the same time, upgrading the industrial structure and the improvement of greening coverage inhibit tourism efficiency. However, the impact of the economic development level on the tourism efficiency of Gansu Province is not apparent. According to the research results, this paper puts forward corresponding suggestions to promote the development of tourism in Gansu Province. This study is crucial for hospitality, tourism, and policy sectors to understand the underlying factors and promote the healthy development of the tourism industry in Gansu Province.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.