A Study of Different Types of Air Pollutants on the Efficiency of China's Hotel Industry.

Xiaoying Guo, Wei Wei, Yang Li, Lei-Ya Wang

Open access · goldAbstract read

In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

3.2field-weighted citation impact, top 7% of its field

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 13 citations in OpenAlex.

Effect of Hotel Air Quality Management on Guests' Cognitive and Affective Images and Revisit Intentions.International journal of environmental research and public health · 2021
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Xiaoying GuoDepartment of International Economics and Trade, School of Economics, Tianjin University of Commerce, Tianjin 300134, China.

Wei WeiDepartment of Economics, School of Economics, Tianjin University of Commerce, Tianjin 300134, China.

Yang LiDepartment of International Economics and Trade, School of Economics, Tianjin University of Commerce, Tianjin 300134, China.

Lei-Ya WangInstitute of Business and Management, National University of Kaohsiung, Kaohsiung 811, Taiwan.

Tianjin University of Commerce · CNNational University of Kaohsiung · TWNew Huadu Business School · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study focuses on visible and invisible air pollutants and their impacts on China's hotel industry. Overall, visible air pollutants may block the sights and sceneries and worsen the quality of visitors' sensory experiences, and invisible air pollutants are unlikely to result in the same perceptions and sensations. Hence, different types of air pollutants may have various impacts on the hotel industry's operational performance. We employed a bootstrapped truncated regression model to investigate whether different types of air pollutants had distinctive impacts on the hotel industry. The dataset consisted of 31 provinces of China for the period 2012-2015. Empirical results indicate that visible air pollutants significantly decrease the operational efficiency of China's hotel industry, while invisible air pollutants insignificantly affect the hotel industry.

Indexed as

Efficiency, OrganizationalAir PollutantsAir PollutionChinaEnvironmental MonitoringIndustryTravelAir Pollutantsbootstrapped truncated regressionChina’s hotel industryData envelopment analysis (DEA)invisible air pollutantvisible air pollutant

Identifiers

PMID31698789

DOI10.3390/ijerph16224319

PMCPMC6888604

OpenAlexW2989001446

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.