Cultural calibration: Technology design for tourism websites

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Abstract

With favourable socio-economic and political conditions, tourism has registered an impressive growth both at the international and domestic level. According to the figures released by UNWTO (2016), the total number of international tourist arrivals worldwide was 1.186 billion in 2015, forecasting to reach 1.8 billion in 2030 with a growth of 3.3% a year. Supporting this phenomenon, Information and Communication Technologies (ICTs), particularly the Internet, play a major role at every stage of the tourism experience. As a consequence, Destination Marketing/Management Organizations (DMOs) should leverage on good-quality websites to answer visitors’ needs and prevail over competitors. Especially for National Tourism Organizations (NTOs), given their role in promoting the destination abroad, the elaboration of online marketing strategies should also take into account culture-bound preferences and needs of reference publics. Within this context, the concept of cultural calibration refers to any technology that transforms contents and tools that people use to communicate with each other in an effort to match with their culture-dependent expressions. Despite the recognized effects of culture on online behavior and willingness to travel, content managers often have to rely on common sense when designing country-specific website editions. In addition to that, differently from eLearning, ecommerce, and online marketing areas, little research has been done regarding software design for adaptation activities in the eTourism context. Consequently, the objective of this paper is to provide a design concept for a software for cultural calibration of destination websites. Its relevance for researchers and practitioners is also presented, together with the evaluation of its functionalities and possible applications.

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APA

Mele, E., & Sutinen, E. (2017). Cultural calibration: Technology design for tourism websites. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10290 LNCS, pp. 501–513). Springer Verlag. https://doi.org/10.1007/978-3-319-58640-3_36

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