Improving the resident–tourist relationship in urban hotspots

Vu, Huy Quan; Muskat, Birgit; Li, Gang; Law, Rob

doi:10.1080/09669582.2020.1818087

Cited by 15 publications

(7 citation statements)

References 58 publications

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“…Fourth, the range of analytical techniques adopted to analyze data (especially UGC textual data) is expanding quickly and the past five years have witnessed a growth in the adoption of techniques borrowed from data science. These include: topic modeling techniques (Vu et al , 2020; Xiang et al , 2017), textual metadata processing (Becken et al , 2019; Miah et al , 2017), sentiment analysis (Aggarwal and Gour, 2020; Hao et al , 2020; Mehralyiev et al , 2020; Kirilenko et al , 2018), Machine Learning techniques (Ahani et al , 2019; Chang et al , 2020; Höpken et al , 2020; Sánchez-Medina and C-Sánchez, 2020) and deep learning models (Chang et al , 2020; Hao et al , 2020; Ma et al , 2018; Zhang et al , 2020; Zhang et al , 2019). Several recent studies combine some of the aforementioned techniques (Aggarwal and Gour, 2020).…”

Section: Discussionmentioning

confidence: 99%

Big data and analytics in hospitality and tourism: a systematic literature review

Mariani

Baggio

2021

IJCHM

125

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Purpose The purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas, research streams and gaps and to develop an agenda for future research. Design/methodology/approach This research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling. Findings The number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as BD analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques. Originality/value This work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on BD and analytics, combining these two interconnected topics.

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Section: Discussionmentioning

confidence: 99%

Big data and analytics in hospitality and tourism: a systematic literature review

Mariani

Baggio

2021

IJCHM

125

View full text Add to dashboard Cite

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“…This study adopted perplexity as the metric to evaluate the performance of the LDA topic model and then determine the optimal number of abstract topics (K). Perplexity measures how well the actual observed word distribution can be represented by the word distribution as the outcomes of the theoretical model (Loureiro et al , 2020; Vu et al , 2020). In practice, manually adjusting the number of topics is also required since the topics need to be interpretable and understood by human beings.…”

Section: Methodology Of Study 1: Text Miningmentioning

confidence: 99%

All without leaving home: building a conceptual model of virtual tourism experiences

Wei

Baker

Önder

2022

IJCHM

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Purpose This study aims to use mixed methods to create a new conceptual framework to understand the unique characteristics of virtual tourism experiences (VTE), which has not been systemically examined. Design/methodology/approach Study 1 uses topic modeling with Latent Dirichlet Allocation to analyze 91,609 online reviews from the Airbnb Experience platform. Study 2 uses content analysis of open-ended qualitative responses from VTE customers. The two studies together are used to build a new conceptual model. Findings Building upon the Stimulus–Organism–Response (S-O-R) model and the experience economy, results present a new conceptual model and identify VTE as unique in terms of Stimulus (education, entertainment, esthetics, escapism and connection), Organism (experiencing synchronicity, telepresence, participation and customization, emotion) and Response (evaluation and behavioral responses). Given the uniqueness of VTE, the new construct of the virtual servicescape is incorporated, recognizing the host, the focal customer and other customers, and the technology as the four main components. Practical implications The proposed framework can be used to guide the design, development, and evaluation of VTE, including identifying the key considerations, engagement within the ecosystem and providing guidance to hosts and operators. Originality/value To the best of the authors’ knowledge, this is the first study that systematically explores VTE and proposes the theoretical framework to comprehensively understand this new form of experience in sharing economy by combining the unique aspects of the stimulus, organism, response and virtual servicescape.

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“…Crowding due to the density of individuals, families, and homes are detrimental to tourists, because it increases the insecurity coefficient. Thus, some tourists' concerns about population insecurity and dislike for noise and crowds are motivated by their negative emotions and feelings toward a tourist destination [26]. To this end, population density maps were used as one of the influential factors in selecting tourist destinations in this study.…”

Section: Methodsmentioning

confidence: 99%

A GIS-Based Assessment of Urban Tourism Potential with a Branding Approach Utilizing Hybrid Modeling

2022

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Urban tourism has developed into one of the most efficient and effective economic and recreational activities in the modern era since the late second half of the twentieth century. Although attention to urban tourism development is increasing, this paper indicates that no comprehensive study has yet combined the two subjects of tourism development and branding for spatial modeling. Thus, for assessing urban tourism potential (AUTP), a novel hybrid modeling approach combining K-mean, fuzzy logic, and an artificial neural network (ANN) was used. The findings indicate that in Tabriz metropolis, areas 3 and 7 and the southwestern portion of region 6 have the most significant potential for urban tourism. In contrast, areas 4, 8, and 10 located within the worn-out urban fabric have the least potential for tourism. Subsequently, by examining the correlation between urban tourism conditioning factors (UTCFs) and tourism maps, it was determined that factors including distance from the catering centers, distance from the fault, quality of construction materials, distance from the historical centers, distance from the health centers, maximum temperature, and distance from the parking are the most critical in terms of increasing urban tourism's potential and branding. The analyses conducted in this study provide valuable and practical information for developing future strategies urban tourism. To this end, recommendations have been made to enhance tourism destination service delivery and management through branding.

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Improving the resident–tourist relationship in urban hotspots

Cited by 15 publications

References 58 publications

Big data and analytics in hospitality and tourism: a systematic literature review

Big data and analytics in hospitality and tourism: a systematic literature review

All without leaving home: building a conceptual model of virtual tourism experiences

A GIS-Based Assessment of Urban Tourism Potential with a Branding Approach Utilizing Hybrid Modeling

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