Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

Song, Haiyan; Lin, Songhua; Witt, Stephen F.; Zhang, Xin Yan

doi:10.1016/j.tourman.2010.05.006

Cited by 142 publications

(112 citation statements)

References 37 publications

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“…A recent development in dynamic modelling is the integration of the time-varying-parameter (TVP) technique and the causal structural time series model (Song, Li, Witt, & Athanasopoulos, 2011), which combines the technical advantages of both methods and shows superior forecasting performance. Recently, a new cointegration-error correction method-the bound test of Pesaran, Shin, and Smith (2001)-has been applied to tourism (e.g., Halicioglu, 2010;Song, Lin, Witt, & Zhang, 2011). This is a test to detect the long-run co-integration relationship among variables in a demand model.…”

Section: Methodological Developmentsmentioning

confidence: 99%

“…It should be noted that there are two types of impact studies based on CGE models and demand forecasting models, respectively. The former estimate the economic effects of demand shocks not only within the tourism industry but also in other related economic sectors (Adams & Parmenter, 1995), while the latter (e.g., Smeral, 2009b;Song, Lin, Witt, & Zhang, 2011) focus on the impact on tourism demand only. Often the results of the latter are employed in the CGE simulation process (e.g., Lee et al, 2010).…”

Section: Impact Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Tourism economics research: A review and assessment

Song

Dwyer

et al. 2012

Annals of Tourism Research

Self Cite

386

269

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Section: Methodological Developmentsmentioning

confidence: 99%

Section: Impact Analysis Methodsmentioning

confidence: 99%

Tourism economics research: A review and assessment

Song

Dwyer

et al. 2012

Annals of Tourism Research

Self Cite

386

269

View full text Add to dashboard Cite

“…The demand for hotel accommodation is measured by a variety of variables, from different perspectives. Some variables relate to the scale of demand, such as guest arrivals (Guizzardi and Stacchini, 2015), the number of nights stayed (Falk, 2014;Lim et al, 2009), the number of rooms sold (such as Corgel et al, 2013;Song et al, 2011b), and occupancy rates (Koupriouchina et al, 2014;Wu et al, 2010).…”

Section: Measurement Of Hotel Demandmentioning

confidence: 99%

New developments in tourism and hotel demand modeling and forecasting

Song

Shen

2017

IJCHM

Self Cite

255

217

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Design/Methodology/approachArticles on tourism and hotel demand modeling and forecasting published in both Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) journals were identified and analyzed. FindingsThis review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior, and consumer confidence indicators, among others. More sophisticated techniques such as Research limitations/implicationsThe main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implicationsThis review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/valueThe value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

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“…More specifically as regards tourism research into the impact of an economic crisis, it can be concluded that studies are either limited to a crisis in a single country (Okumus & Karamustafa, 2005, concerning the 2001 crisis in Turkey; Henderson (1999), concerning the crisis in Singapore in the '90s), or focused on econometric models predicting tourist expenditure allocation at an aggregate level (Divisekera, 2010;Song, Lin, Witt, & Zhang, 2011), or are outdated because the last global recession was long ago. Evidently, tourism research is lacking in empirical data about a worldwide crisis whose influence is not limited to a single country, is conducted at the level of individual vacationers and not at an aggregate level, and is directed at the recent crisis and not a crisis from the past.…”

Section: Introductionmentioning

confidence: 99%

Economizing strategies during an economic crisis

Bronner

Hoog

2012

Annals of Tourism Research

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Recently, the consumer was hit hard by the consequences of the global economic crisis, which still has effects on tourists' spending. These effects are investigated using a general framework linking crises/disasters to indiv'idual tourist behavior. In 2010, data were collected in the Netherlands about economizing strategies on vacations. Intentions and behavior were measured. Two-thirds of the population economized on the main summer holiday. Different strategy segments are discerned: some tourists choose a pruning strategy, others employ a cheese-slicing strategy. Cheese-slicing was the predominant strategy, confirming a prediction derived from the general framework. For some segments, strategies made when planning the holiday change during the holiday itself, while for other segments the strategies are stable. Theoretical and practical implications are discussed.

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Impact of financial/economic crisis on demand for hotel rooms in Hong Kong

Cited by 142 publications

References 37 publications

Tourism economics research: A review and assessment

Tourism economics research: A review and assessment

New developments in tourism and hotel demand modeling and forecasting

Economizing strategies during an economic crisis

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