Understanding Tourism Suppliers’ Resilience to Climate Change in a Rural Destination in Maine

Horne, Lydia; Urioste-Stone, Sandra De; Rahimzadeh-Bajgiran, Parinaz; Seekamp, Erin

doi:10.1080/21568316.2022.2083222

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…Finally, climate change presents numerous challenges for tourism globally and destination resilience in particular [95]. Lew and Cheer [96] noted that climate change's greatest threat to tourist destinations relates to the loss of natural amenities, disappearing attractions, seasonal inaccessibility, or changes in visitor markets requiring different tourism structures and product supply.…”

Section: Relevant Limitations and Future Research Directionsmentioning

confidence: 99%

Evaluating Nature-Based Tourism Destination Attractiveness with a Fuzzy-AHP Approach

Hunt

Jia³

et al. 2022

Sustainability

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Nature-based tourism attractiveness (NBTA) has yet to be assessed by coupling empirical measurement of supply and demand indicators with simultaneous assessment of tourist and tourism expert perspectives. Based on a guiding principle that the overall attractiveness of a tourism destination should combine the evaluation of existing resources or attractions and their perceived attractiveness, the purpose of this study is to develop and apply a novel methodological approach for assessing tourism attractiveness of nature-based destinations. This approach developed here combines an Analytic Hierarchy Process (AHP) with a Fuzzy Comprehensive Evaluation Method (FCEM). The resulting Fuzzy-AHP approach to NBTA was tested at the Changbai Mountain Biosphere Reserve, a popular nature-based tourism destination in China. The findings confirm that this Fuzzy-AHP approach is a more reliable and comprehensive method for evaluating the destination attractiveness than pre-existing approaches. In addition to theoretical contributions related to the merging of various approaches to assessing destination attractiveness and the development of a tool specific to nature-based tourism destinations, this work will be of interest to decision makers seeking more effective tools for planning, marketing, and developing nature-based tourism destinations.

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Section: Relevant Limitations and Future Research Directionsmentioning

confidence: 99%

Evaluating Nature-Based Tourism Destination Attractiveness with a Fuzzy-AHP Approach

Hunt

Jia³

et al. 2022

Sustainability

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“…According to previous studies, this resilience can be linked to the sector's adaptability, collaboration, and innovation (Aburumman, 2020;Dillette & Ponting, 2020;Lekgau & Tichaawa, 2021a, 2022a. Studies such as Horne et al (2022) point to the importance of examining supplier experiences and adaptability during periods of significant change and uncertainty. The responses presented in the previous section describe changes implemented and considered by MICE event venues.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Changes and Recovery in Event Venues: Perspectives from MICE Event Planners and Venue Managers

Lekgau,

Tichaawa

2023

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The study examines the role played by event venues in helping the MICE event industry in South Africa recover from the Covid-19 pandemic, adapt to new conditions and innovate. The analysis is based on qualitative data collected during in-depth interviews with MICE event venue suppliers and planners. It was found that a crucial first step in order to reopen these venues was to ensure compliance with safety regulations. In response to the quick shift to virtual and hybrid events, many venues implemented necessary upgrades to accommodate such forms of participation. A number of event planners also considered using multiple venues to host large-scale events. The crisis caused by the pandemic re-emphasised the importance of alternative event venues and outdoor spaces. The study has implications for MICE event planners and suppliers.

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“…The practical implications of this research emphasize the importance of collective public action in response to the issue of climate change through digital campaigns aimed at enhancing tourists' and local communities' understanding of anticipatory measures against climate change-induced disasters in tourism. By harnessing digital platforms for advocacy and information dissemination, these campaigns have the potential to mobilize widespread support and engagement, fostering a shared sense of responsibility toward mitigating the adverse impacts of climate change on tourist destinations [14]. Moreover, they facilitate the communication of proactive measures and adaptation strategies, empowering tourists and residents to make informed decisions and take necessary actions to safeguard the resilience and sustainability of tourism in the face of climate-related challenges [15].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine

Singgalen

2024

JoSYC

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This research aims to classify public sentiment regarding the issue of climate change and tourism. The research problem addressed in this study pertains to the classification of public sentiment concerning climate change within the tourism sector. Specifically, the study aims to explore and classify the public's sentiments regarding the impact of climate change on tourism activities.The methodology employed is CRISP-DM, which encompasses stages of business understanding, data understanding, modeling, evaluation, and deployment. Specifically, the SVM and SMOTE algorithms are utilized in the modeling stage to achieve optimal results. By leveraging this systematic approach and advanced algorithms, the study seeks to comprehensively analyze public sentiment towards climate change within the context of tourism, thus contributing valuable insights to academia and industry practitioners. Applying CRISP-DM methodology coupled with SVM and SMOTE algorithms enhances the rigor and effectiveness of sentiment analysis in addressing the complexities of climate change discourse in the tourism sector. The findings of this research demonstrate that the SVM and SMOTE algorithms yield promising results in sentiment classification, with an accuracy of 86.15% +/- 1.68% (micro average: 86.15%), precision of 85.17% +/- 2.16% (micro average: 85.11%) (positive class: Positive), recall of 87.64% +/- 3.39% (micro average: 87.64%) (positive class: Positive), f_measure of 86.34% +/- 1.79% (micro average: 86.35%) (positive class: Positive), and AUC of 0.923 +/- 0.012 (micro average: 0.923) (positive class: Positive). These metrics indicate the effectiveness and reliability of the SVM and SMOTE algorithms in accurately classifying sentiment toward climate change in the context of tourism. The high accuracy, precision, recall, f_measure, and AUC scores suggest that the models produced by these algorithms are robust and capable of capturing nuanced sentiment patterns, thereby contributing to the advancement of sentiment analysis techniques in climate change research within the tourism domain.

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Understanding Tourism Suppliers’ Resilience to Climate Change in a Rural Destination in Maine

Cited by 8 publications

References 64 publications

Evaluating Nature-Based Tourism Destination Attractiveness with a Fuzzy-AHP Approach

Evaluating Nature-Based Tourism Destination Attractiveness with a Fuzzy-AHP Approach

Dynamic Changes and Recovery in Event Venues: Perspectives from MICE Event Planners and Venue Managers

Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine

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