Applied Management Research Unit

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Organizational Unit

Publications

Challenges and opportunities for island tourist destinations: the case of the Island of Sal, Cape Verde

Publication . Neves, Gilberto A.; Nunes, Catarina S.; Fernandes, Paula Odete

The Tourism Area Life Cycle destination goes through different phases from its exploration until its decline or rejuvenation. The knowledge about these different phases allows the improvement of investment decisions by the private sector or by the government, in a context of challenges and opportunities. The main objective of this study was to verify in which phase the Island of Sal and Cape Verde were at an individual and competitive level during the period 2010-2018, considering the Tourism Development Index (TDI). To calculate the TDI, destinations with the same ‘sun and beach’ market were chosen, such as the Dominican Republic, Morocco, Tunisia and the Canary Islands, because they compete for the same European market and their geographical proximity to this market; data from government and non-government sources were used. It was concluded that the Island of Sal is in the Development phase, the same phase as that of Cape Verde. As for competitiveness, they are in the exploration and stagnation phase, both needing to increase the TDI to 48% and 43%, respectively to reach the involvement phase. For the calculation of the TDI, data from Cape Verde can be used to analyse the Sal Island index and vice versa. Strategic policies must be considered in the long term, incorporating information on the relative positions of direct competitors and unexpected events such as COVID 19, which can be seen as an opportunity to diversify the offer, create new segments and discover new inbound markets.

2021Conference object

Restricted access

Seasonal autoregressive integrated moving average time series model for tourism demand: the case of Sal Island, Cape Verde

Publication . Neves, Gilberto A.; Nunes, Catarina S.; Fernandes, Paula Odete

This article appears as an essential contribution for decision-makers in the Cape Verdean tourism sector given the impact that the number of overnight stays has on the economy of the country and the Sal Island, which until 2018 had been increasing every year. Since seasonality is a strong feature of the island's tourism, decision-makers are interested in knowing the seasonal variation in tourism demand. Thus, this study focused on the application of the Box-Jenkins method to the time series of the monthly number of nights stays in tourist establishments on the Sal Island, Cape Verde, over the period from January 2000 to December 2018, to find a model that better describes the series and with good forecast results for the year 2019. Several SARIMA models were studied using the Box-Jenkins method, with the SARIMA(1,1,1)(0,1,1)12 and the SARIMA(2,1,0)(0,1,1)12 demonstrating the best predictive performance in the test phase. However, in forecasting the series for the year 2019 the SARIMA(2,1,0)(0,1,1)12 achieved the best results with a MAPE=8.78%. This model can be used to simulate and analyse the number of overnight stays that be expected on the Island, if the tourism sector were not affected by the pandemic caused by COVID-19.

2022-05Book part

Restricted access

Modelação e previsão da procura turística na Ilha do Sal – Cabo Verde: modelo SARIMA versus rede neuronal artificial

Publication . Neves, Gilberto A.; Fernandes, Paula Odete; Nunes, Catarina S.

O objetivo principal deste trabalho é a análise da série temporal "Número de dormidas mensais nos estabelecimentos turísticos da ilha do Sal - Cabo Verde” entre 2000 e 2018 e demonstrar que, no quadro dos modelos quantitativos, estimativas e previsões fiáveis para o comportamento da procura turística são preferencialmente obtidas com modelos estatísticos de previsão adequadamente especificados e testados, e com redes neuronais artificiais que permitem prever um passo à frente. Para tal, iniciou-se a investigação com uma revisão da literatura e análise de dados que possibilitou conhecer: a dinâmica do turismo mundial; o mercado emissor europeu; o desenvolvimento turístico da ilha; as características da série temporal; modelos de previsão e tendências da procura turística. Seguidamente implementaram-se diferentes estruturas de modelos de previsão. Os resultados obtidos mostram que, a nível individual, a ilha se encontra na fase de Desenvolvimento; a nível de competitividade, a ilha está estagnada dentro da fase de Exploração; e que o seu Índice de Desenvolvimento Turístico deverá crescer em 48% para entrar na fase de Envolvimento. Quanto aos modelos de previsão, obtiveram-se: o modelo SARIMA(2,1,0)(0,1,1)[12], com uma acurácia medida pelo MAPE igual a 6,77%; o modelo de redes neuronais do tipo RNAR(12,1,7)[12], com um erro de 5,61% e o método de Holt-Winters que produziu um modelo com uma precisão de 7,94%. Todos esses modelos têm alta precisão, com destaque para a rede neuronal, apesar dos dados da série não estarem adaptados à Lei de Benford. Porém, o proposto Algoritmo de Atribuição do Erro, traz melhorias ao resultado do modelo SARIMA, com uma precisão de 4,98%. Esta tese pretendeu contribuir para mostrar o potencial dos modelos estatísticos de previsão e da aplicação das redes neuronais artificiais para a previsão do número de dormidas mensais na ilha do Sal. Também se avalia a precisão das previsões de cada modelo e compara os seus diferentes desempenhos.

2022-11-04Doctoral thesis

Embargoed access

Application of Benford’s Law to the Tourism Demand: the case of the Island of Sal, Cape Verde

Publication . Neves, Gilberto A.; Nunes, Catarina S.; Fernandes, Paula Odete

This article presents Benford’s Law applied for the first time to the tourism context, focusing on tourism demand. This law states that in sets of random numbers of natural events, the probability of the first digit of these numbers being 1 is approximately 30%, of being 2 is 18%, and so on until reaching 9 with 4.6% probability. In this context, the objective is to verify if Benford’s Law applies to the monthly numbers of overnight stays registered in the accommodation establishments of the Island of Sal, in the period between 2000 and 2018, to test the data reliability. This research focus on data provided by the National Statistics Institute of Cape Verde. The Chi-Square test (χ2) was used to assess the discrepancy between the observed and expected relative frequencies. The results show that the observed χ2 value is higher than the χ2 critical value (5% significance level), meaning that the number of monthly overnight stays recorded in accommodation establishments on the Island of Sal does not follow Benford’s Law. However, certain possible data disturbances must be considered, such as the occurrence of specific events during that time period. Other factors that could influence the results are the size of the data set and a sub notification in the data collection process. These circumstances may be the cause of the non-adaptation of the number of overnight stays to Benford’s Law. The implication of this fact on the estimation of tourism demand is crucial for the development and optimization of prediction models.

2021Conference object

Open access