1. (Mt) – Article reviews
Assignment Complete.Running head: ARTICLE REVIEW 11Article Review
1NameInstitutional AffiliationARTICLE REVIEW 12Article Review 1Tourism Demand
Modelling and Forecasting (2008)The article Tourism Demand Modelling and Forecasting:
A Review of Recent Researchby Song & Li (2008) presents a review of published studies on
demand forecasting in tourismfrom 2000. It presents empirical findings of the research,
which is based on methodologicaldevelopments, competition, combination, and integration
of forecasts, and some generalobservations. In this review, the writer presents a summary
of the article, the association betweenthe lessons of the article with the lessons in class. The
writer concludes by presenting hisperspectives of the article.Article SummaryThe article
presents a review of published studies that majored in tourism demand andforecasting
from 2000. The increasing demand for tourism worldwide over the previous twentyyears
has compelled scholars to research demand and forecasting in tourism. The findings
drawnfrom these studies suggest that the methods for analyzing as well as forecasting
demand are morediverse as compared to other review articles. In addition to econometric
models, various newmethods are presented in the literature. Nevertheless, when it comes to
forecasting accuracy, thestudy makes it clear that no single model performs better than the
other in various situations.Moreover, this research identifies various newfangled research
directions, which encompassoptimizing the forecasting precision through prediction
amalgamation, combining bothquantitative and qualitative forecasting approaches,
seasonality analysis, tourism cycles, impactassessment events, as well as risk forecasting.
All the same, seasonality analysis is usuallyacknowledged when performing an analysis of
tourism demand. The reason behind this is thatthe market in this sector is marked with
seasonal demand. Seasonal demand is a particular timeARTICLE REVIEW 13series with
predictable or repetitive demand patterns because of the reoccurrence of the
seasonalevents. These patterns can reoccur for some days, months, or even quarters, and
this may hencemake it difficult for businesses to project imminent demand trends. The
researchers gaveexamples of some seasonal events in the United Kingdom (UK) that occur
yearly. Someexamples of these events are Ramadan, Christmas, yearly events like Bonfire
Night andValentines’ Day, and seasonal weather patterns like the hot climate in summer
and snow amidwinter.Song & Li (2008) make it clear that forecasters normally prefer to
forecast demand byconsidering the seasonal analysis model, which enables when applied in
businesses, it promotestheir competitiveness in the market. Various reasons suggest why
forecasters prefer seasonalanalysis when performing demand forecasting. Employing the
2. model enables them to drawmeaningful outcomes which first, allows investors to capitalize
on the peaks in demand. Thereason behind this is that forecasting for seasonal
discrepancies ensures that investors haveadequate stock levels available to consequently
take advantage of upsurges in the demand of theproduct during peak. Subsequently, this
may enable investors to enjoy substantial profits.Secondly, it restricts investors from
acquiring excessive stock and issue that may contribute tocash flow problems as well as the
morbid balance sheet. However, while seasonality analysisseems to be highly adopted when
it comes to forecasting tourism demand, the ways in which theanalysis is handled are still
obscure. Seasonal fractional integration, which was introducedrecently, is an optional
approach to model seasonality. Despite this fact, scholars are should becommitted to
engaging in more methods to improve forecast accuracy. While a lot of attentionhas been
directed to forecasting the intensity of tourism demand, there is a need to conductextensive
research on improving the accuracy of forecasting.ARTICLE REVIEW 14Song & Li (2008)
present thoughtful information that is not only useful to forecasters butalso forthcoming
researchers because it presents new research guide such as combining bothquantitative
and qualitative forecasting methodologies, inform the readers the necessity ofintegrating
models when forecasting as it helps to advance accuracy in forecasting.Connecting Lessons
from the Article with Lessons in the ClassThe article presents thoughtful information
regarding forecasting demand. After readingthrough the article, it became clear to me that
organizations along with enterprises acknowledgedemand forecasting because it enables
them to determine and project the number of services thecustomers will be willing to
acquire in the foreseeable future. The article also presents variousmodels that researchers
can engage to improve efficiency and accuracy when forecasting. Theresearchers of this
article have made me realize that there is no ideal way in which a singlemodel can promote
accuracy in forecasting when used alone. The researchers also confirmed thatseasonal
fraction is the ultimate model when used alone in conducting tourism
demandforecasting.While the seasonality model in determining tourism demand is often
used following itseffectiveness, the researchers encourage those working in the tourism
sector to considerintegrating this model with other models to improve the accuracy of
demand forecasting. This isbecause by integrating the model with other, errors are
minimized, and this subsequently, willenable them to identify the covariant variables which
augment the degree of accuracy inforecasting.There is a connection between what I learned
from the article with what in learned inclass. The article informs the reader on how to
improve accuracy in demand forecasting by usingmultiple models when working towards
improving the efficiency and accuracy of demandARTICLE REVIEW 15forecasting. By
applying multiple models, errors will be minimized, and it would be necessary toconfirm
this by engaging forecasting measurement criteria that institutes mean absolute
deviation(MAD) and mean error (BIAS) where low percentages in MAD and BIAS suggest
minimizederrors hence higher accuracy and efficiency in demand forecasting.My
Perspectives on the ArticleMy perspectives of the article are based on the idea of how to
perform demandforecasting while getting rid of the errors that may arise. However,
engaging multiple models aresignificant in improving the process of demand forecasting.
However, the researchers primarilymajored in forecasting the degree of tourism demand
3. while researching a little on forecasting theturning point forecast accuracy. This points out
the need to encourage future researchers to focuson this area.My New Learning from the
ArticleI learned that it would be possible to promote the accuracy in demand forecasting
byengaging multiple models because there is no single model that proves to be more
accurate thanthe other.The Interesting…
