Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts
The issue of tourism impacts is one that has plagued the tourism industry. This study develops a quantitative approach using hierarchical variance analysis, which deals with the exploration of the relevant factors and the confirmation of their significant contribution to analyze the residents’ perce...
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doaj-0d217d7d4df44c9c9c3d8bd10cc4d74e2020-11-25T02:55:17ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012020-04-01172786278610.3390/ijerph17082786Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism ImpactsQuang Hai Truong0An Thinh Nguyen1Quoc Anh Trinh2Thi Ngoc Lan Trinh3Luc Hens4Institute of Vietnamese Studies & Development Sciences, Vietnam National University (VNU), Hanoi 10000, VietnamUniversity of Economics and Business, Vietnam National University (VNU), Hanoi 10000, VietnamUniversity of Science, Vietnam National University (VNU), Hanoi 10000, VietnamHanoi University, Hanoi 10000, VietnamVlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, 2400 Mol, BelgiumThe issue of tourism impacts is one that has plagued the tourism industry. This study develops a quantitative approach using hierarchical variance analysis, which deals with the exploration of the relevant factors and the confirmation of their significant contribution to analyze the residents’ perception of tourism impacts. Hierarchical variance analysis includes three mathematical procedures: Cronbach’s alpha tests, the exploration of relevant factors, and a hierarchical factor confirmation. Data are collected using a structured questionnaire completed by 452 surveyed residents living in Ly Son Island, Vietnam. The significant effects of socio-demographic variables on the overall impact assessment are observed. The bilateral and simultaneous relationships are analyzed using a one-factor ANOVA. A two-factor ANOVA shows the significant contribution of each socio-demographic variable on the economic, socio-cultural, and environmental impacts. Interaction between factors such as “Education level”, “Type of work”, etc. are hierarchically confirmed. The findings allow a better understanding of the residents’ perception of the effects of tourism on society, the economy, and the environment. This provides a scientific basis to help define problems and promote legal regulations for community participation in tourism planning in a small island destination.https://www.mdpi.com/1660-4601/17/8/2786hierarchical variance analysisresidents’ perceptionssocio-demographic variablesANOVAlinear regression modelperceived tourism impacts |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Quang Hai Truong An Thinh Nguyen Quoc Anh Trinh Thi Ngoc Lan Trinh Luc Hens |
spellingShingle |
Quang Hai Truong An Thinh Nguyen Quoc Anh Trinh Thi Ngoc Lan Trinh Luc Hens Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts International Journal of Environmental Research and Public Health hierarchical variance analysis residents’ perceptions socio-demographic variables ANOVA linear regression model perceived tourism impacts |
author_facet |
Quang Hai Truong An Thinh Nguyen Quoc Anh Trinh Thi Ngoc Lan Trinh Luc Hens |
author_sort |
Quang Hai Truong |
title |
Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts |
title_short |
Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts |
title_full |
Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts |
title_fullStr |
Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts |
title_full_unstemmed |
Hierarchical Variance Analysis: A Quantitative Approach for Relevant Factor Exploration and Confirmation of Perceived Tourism Impacts |
title_sort |
hierarchical variance analysis: a quantitative approach for relevant factor exploration and confirmation of perceived tourism impacts |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2020-04-01 |
description |
The issue of tourism impacts is one that has plagued the tourism industry. This study develops a quantitative approach using hierarchical variance analysis, which deals with the exploration of the relevant factors and the confirmation of their significant contribution to analyze the residents’ perception of tourism impacts. Hierarchical variance analysis includes three mathematical procedures: Cronbach’s alpha tests, the exploration of relevant factors, and a hierarchical factor confirmation. Data are collected using a structured questionnaire completed by 452 surveyed residents living in Ly Son Island, Vietnam. The significant effects of socio-demographic variables on the overall impact assessment are observed. The bilateral and simultaneous relationships are analyzed using a one-factor ANOVA. A two-factor ANOVA shows the significant contribution of each socio-demographic variable on the economic, socio-cultural, and environmental impacts. Interaction between factors such as “Education level”, “Type of work”, etc. are hierarchically confirmed. The findings allow a better understanding of the residents’ perception of the effects of tourism on society, the economy, and the environment. This provides a scientific basis to help define problems and promote legal regulations for community participation in tourism planning in a small island destination. |
topic |
hierarchical variance analysis residents’ perceptions socio-demographic variables ANOVA linear regression model perceived tourism impacts |
url |
https://www.mdpi.com/1660-4601/17/8/2786 |
work_keys_str_mv |
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