Abstract

The literature in the field of smart cities shows a continuous emphasis and interest in the topic of big data due to the extensive use of Information and Communication Technologies by public and private institutions within each city. There is undoubtedly value in big data: in data lie insights on the city, its stakeholders, citizens, products, and services. Challenges, though, lie in data’s variety, volume, and velocity, but also in managing them, considering the complex interplay between stakeholders inside a city or a country. Another layer of complexity is added when we consider a smart city as a smart destination where the visitor - often an international tourist - becomes an additional stakeholder of a smart city bringing in additional data. Such challenges, though, are even stronger when tourists do not stop at geographical borders: smart destinations become cross-border destinations. While there is a physical border between them, but most importantly, a legal difference in how data should be collected, stored, managed, and re-used [56, 59], data flows do not stop at this border. This complexity has to be managed both by governmental and tourism agencies. However, the literature between eGovernment and tourism is often theoretical in nature, and while it highlights the potential benefits of smart destinations and data-management processes, it does not provide detailed guidelines on how to implement these concepts in practice [41], especially in the context of cross-border smart destinations. With regards to this, not only has the need for guidelines risen to help tourism destinations tackle smart data- and technology-related projects, but also to define how stakeholders can come together to determine data policies and governance in order to create private as well as public value [60]. This paper responds to such a need by presenting the results of a cross-border research project conducted in Switzerland and Italy, where the model of a smart destination’s structure proposed by Ivars-Baidal et al. [35] has been applied, and its dimensions have been operationalized in a data-related management project. This allowed the authors to understand how to create public and private value managing data flows in a cross-border context, while also elaborating on the model reflecting on data’s dual role as a starting point but also as a central component impacting other dimensions.

Citations

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