Faculty of Economics and Business Sciences, Department of Applied Economics II, University of Seville, Seville, Spain.
Faculty of Technical Sciences, Mathematics Department, University of Pinar del Río, Pinar del Río, Cuba.
PLoS One. 2021 May 25;16(5):e0252139. doi: 10.1371/journal.pone.0252139. eCollection 2021.
This study presents a new method for measuring tourism destination competitiveness based on data covering a specific time span. Issues, such as the type of data gathered, tools and methods employed, and the size and number of destinations, are addressed, as is the consideration of a specified time period. The proposal is based on the information given by linear regression equations, which not only enables the behaviour of destinations to be observed over time, but also facilitates their comparison. The data employed was from the period 2000-2019. Cluster Analysis was introduced to group destinations according to their performance. Moreover, various aggregation methods are proposed to obtain competitiveness rankings. A comparison between destinations was carried out using the non-aggregative and an aggregative approach. Certain destinations attained better positions than others that are considered as being more competitive in global international rankings. Five clusters were clearly identified. The results were consistent with the World Travel and Tourism Council outputs and underlined the importance attached to tourism development in the destinations from Central America and the Caribbean.
本研究提出了一种新的基于特定时间段数据的旅游目的地竞争力衡量方法。文中探讨了所收集数据的类型、使用的工具和方法、目的地的规模和数量，以及特定时间段的考虑因素。该建议基于线性回归方程提供的信息，不仅能够观察目的地随时间的行为，还便于进行比较。所使用的数据来自 2000 年至 2019 年期间。聚类分析被引入到根据表现对目的地进行分组中。此外，还提出了各种聚合方法来获得竞争力排名。使用非聚合和聚合方法对目的地进行了比较。某些目的地的排名优于被认为在全球国际排名中更具竞争力的其他目的地。明确识别出了五个集群。结果与世界旅游理事会的产出一致，并强调了中美洲和加勒比地区目的地对旅游发展的重视。
